# Advancing wheat crop analysis: A survey of deep learning approaches using hyperspectral imaging

Fadi Abdeladhim Zidi, Abdelkrim Ouafi, Fares Bougourzi, Cosimo Distante, Abdelmalik Taleb-Ahmed

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Fadi Abdeladhim Zidi, Abdelkrim Ouafi, Fares Bougourzi, Cosimo Distante, Abdelmalik Taleb-Ahmed. Advancing wheat crop analysis: A survey of deep learning approaches using hyperspectral imaging. Computers and Electronics in Agriculture, 2025, 238, 10.1016/j.compag.2025.110770. hal-05214134

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## Review article

# Advancing wheat crop analysis: A survey of deep learning approaches using hyperspectral imaging

Fadi Abdeladhim Zidi<sup>a</sup>, Abdelkrim Ouafi<sup>a</sup>, Fares Bougourzi<sup>b</sup>, Cosimo Distante<sup>c, d</sup>,*
Abdelmalik Taleb-Ahmed<sup>d</sup>

## ARTICLE INFO

### Keywords:

Hyperspectral imaging

HSI

Deep learning

Wheat crops

Recognition

Nutrient estimation

Yield estimation

Wheat diseases

## ABSTRACT

As one of the most widely cultivated and consumed crops, wheat is essential to global food security. However, wheat production is increasingly challenged by pests, diseases, climate change, and water scarcity, threatening yields. Traditional crop monitoring methods are labor-intensive and often ineffective for early issue detection. Hyperspectral imaging (HSI) has emerged as a non-destructive and efficient technology for remote crop health assessment. However, the high dimensionality of HSI data and limited availability of labeled samples present notable challenges. In recent years, deep learning has shown great promise in addressing these challenges due to its ability to extract and analysis complex structures. Despite advancements in applying deep learning methods to HSI data for wheat crop analysis, no comprehensive survey currently exists in this field. This review addresses this gap by summarizing benchmark datasets, tracking advancements in deep learning methods, and analyzing key applications such as variety classification, disease detection, and yield estimation. It also highlights the strengths, limitations, and future opportunities in leveraging deep learning methods for HSI-based wheat crop analysis. We have listed the current state-of-the-art papers and will continue tracking updating them in the following GitHub Repository.

## Contents

<table><tr><td>1.</td><td>Introduction</td><td>2</td></tr><tr><td>2.</td><td>Methodology</td><td>2</td></tr><tr><td>3.</td><td>Hyperspectral imaging technology: Platforms and sensors</td><td>3</td></tr><tr><td>4.</td><td>Datasets</td><td>5</td></tr><tr><td>4.1.</td><td>Public datasets</td><td>5</td></tr><tr><td>4.2.</td><td>Private datasets</td><td>5</td></tr><tr><td>4.3.</td><td>Discussion</td><td>6</td></tr><tr><td>5.</td><td>Deep learning models for wheat crop analysis</td><td>7</td></tr><tr><td>5.1.</td><td>Supervised learning</td><td>7</td></tr><tr><td>5.1.1.</td><td>Convolutional neural networks (CNNs)</td><td>7</td></tr><tr><td>5.1.2.</td><td>Recurrent neural networks (RNNs)</td><td>8</td></tr><tr><td>5.1.3.</td><td>Deep belief networks (DBNs)</td><td>9</td></tr><tr><td>5.1.4.</td><td>Stacked autoencoder</td><td>9</td></tr><tr><td>5.1.5.</td><td>Transformer</td><td>10</td></tr><tr><td>5.1.6.</td><td>Mamba-based models</td><td>10</td></tr><tr><td>5.2.</td><td>Semi-supervised learning</td><td>11</td></tr><tr><td>5.2.1.</td><td>Pseudo-labeling</td><td>11</td></tr></table>

<footnote><sup>a</sup> Mohamed Khider University, BP 145 RP Biskra, 07000, Algeria</footnote>

<footnote><sup>b</sup> Junia, UMR 8520 CNRS 59000 Lille, France</footnote>

<footnote><sup>c</sup> National Research Council of Italy, Institute of Applied Sciences and Intelligent Systems, via Monteroni snc 73100 Lecce, Italy</footnote>

<footnote><sup>d</sup> Institute d'Electronique de Microelectronique et de Nanotechnologie (IEMN), UMR 8520, Universite Polytechnique Hauts de France, Universite de Lille, CNRS, 59313, Valenciennes, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: fadiabdeladhim.zidi@univ-biskra.dz (F.A. Zidi), a.ouafi@univ-biskra.dz (A. Ouafi), faresbougourzi@gmail.com (F. Bougourzi), cosimo.distante@cnr.it (C. Distante), Abdelmalik.Taleb-Ahmed@uphf.fr (A. Taleb-Ahmed).</footnote>
<table><tbody><tr><td>5.2.2.</td><td>Generative models</td><td>12</td></tr><tr><td>5.3.</td><td>Unsupervised learning.</td><td>12</td></tr><tr><td>5.3.1.</td><td>Preprocessing methods</td><td>12</td></tr><tr><td>5.3.2.</td><td>Clustering methods (classification)</td><td>14</td></tr><tr><td>5.4.</td><td>Discussion.</td><td>14</td></tr><tr><td>6.</td><td>Applications of HSI in wheat crops.</td><td>15</td></tr><tr><td>6.1.</td><td>Wheat crop classification</td><td>15</td></tr><tr><td>6.2.</td><td>Wheat crop disease detection and monitoring</td><td>17</td></tr><tr><td>6.3.</td><td>Wheat crop nutrient estimation</td><td>17</td></tr><tr><td>6.4.</td><td>Wheat crop yield estimation.</td><td>18</td></tr><tr><td>7.</td><td>Discussion</td><td>18</td></tr><tr><td>8.</td><td>Challenges and future directions</td><td>20</td></tr><tr><td>9.</td><td>Conclusion</td><td>22</td></tr><tr><td></td><td>CRediT authorship contribution statement</td><td>22</td></tr><tr><td></td><td>Declaration of competing interest.</td><td>22</td></tr><tr><td></td><td>Data availability</td><td>22</td></tr><tr><td></td><td>References.</td><td>23</td></tr></tbody></table>

# 1. Introduction

Wheat is regarded as the most important agricultural product for global food security, contributing approximately 20% of global dietary energy and protein intake (Shiferaw et al., 2013). As the most widely cultivated cereal in the world (Acevedo et al., 2018), wheat grains consist of substantial amounts of carbohydrates (75%–80%) and protein (9%–18%), making them a valuable source of vitamins and minerals. However, wheat production faces numerous challenges, including pests, diseases, climate change, and the pressing need for yield optimization. These factors significantly impact crop health and overall productivity, highlighting the necessity for effective monitoring and management strategies.

To overcome challenges in wheat crop analysis, advanced remote sensing techniques like HSI have gained prominence for their non-destructive, sensitive, and reliable capabilities. HSI provides detailed spectral data across diverse wavelengths, enabling precise disease detection, nutrient monitoring, and yield estimation. Modern advancements, including low-cost hyperspectral sensors like FireflyEYE and HySpex VNIR, have expanded the applicability of HSI through UAVs, satellites, and ground-based systems (Gonzalez-Dugo et al., 2015; Marotto et al., 2013; Marshall and Thenkabail, 2015; Lodhi et al., 2019). However, the complexity and time-consuming nature of HSI data analysis necessitate the development of automated AI-based models to ensure efficiency and accuracy while minimizing human error.

Deep Learning (DL) models for hyperspectral imaging offer transformative advantages in wheat crop analysis, providing real-time, non-destructive insights into plant health. These techniques enable early stress and disease detection, accurate yield prediction, and optimized nutrient management, ultimately reducing costs and minimizing environmental impact (Khan et al., 2022; Li et al., 2022a). Despite significant progress in applying DL to agricultural HSI (Tejasree and Agilandeeswari, 2024; Wang et al., 2021b; Kuswidiyanto et al., 2022), existing reviews often neglect the unique challenges in wheat crop analysis, such as high spectral dimensionality, integration complexities, and limited labeled data, highlighting the need for targeted research in this area.

In the context of deep learning, several challenges emerge when applying DL methods to wheat crop analysis using HSI data. One of the most critical issues is the high dimensionality of hyperspectral data, which complicates efficient processing and often necessitates substantial computational resources and specialized architectures (Zidi and Ouafi, 2024). Another major challenge is the scarcity of labeled training data, which is essential for building accurate and generalizable DL models. Furthermore, integrating environmental factors into DL frameworks remains difficult due to the variability in spectral responses of wheat crops under different biotic and abiotic stresses (Kaur et al., 2024).

Overfitting is also a common issue when models are trained on small datasets, reducing their generalizability across varied field conditions. Addressing these challenges requires ongoing advancements in model design and the development of more efficient training strategies (Sharma et al., 2024).

To effectively tackle these limitations, a comprehensive review is crucial, not only to synthesize current advancements and identify persistent challenges but also to investigate promising future directions. Despite the growing importance of this field for global food security, no dedicated survey currently exists that focuses specifically on the intersection of DL and HSI for wheat crop analysis. Table 1 summarizes the most relevant existing reviews. For instance, Terentev et al. (2022) focuses on plant disease detection using HSI data, while (Barbedo, 2023) offers a broader overview of HSI applications in agriculture, including some studies related to wheat. This survey aims to bridge that gap by providing a focused and detailed examination of state-of-the-art research on wheat crop analysis using HSI and DL. We present an in-depth analysis of available datasets, a thorough investigation of recent advancements in deep learning techniques for wheat crop analysis followed by a comparative evaluation, and an exploration of diverse wheat-specific applications. These contributions uniquely position this work as a foundational roadmap for advancing precision wheat farming.

To provide a structured and comprehensive overview of the field, existing methods are systematically categorized based on learning paradigms into: (i) supervised, (ii) semi-supervised, and (iii) unsupervised approaches. Additionally, we delve into recent advancements demonstrating significant potential in the field, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, Generative Adversarial Networks (GANs), Deep Belief Networks (DBNs), Stacked Autoencoders (SAEs), Diffusion models, and others. Beyond exploring these methodologies, we examine their applications, emphasizing current progress and limitations for each. To the best of our knowledge, this is the first review to focus specifically on the intersection of HSI data and DL techniques for wheat crop analysis.

As depicted in Fig. 1, the remaining sections of this review are organized as follows: Section 2 describes the research methodology. Section 3 provides an overview of HSI technology. Section 4 summarizes available databases, discussing their advantages and limitations. Section 5 details deep learning methods, while Section 6 focuses on HSI applications in wheat crop analysis. Sections 7 and 8 discuss challenges and potential future research directions, respectively. Finally, Section 9 concludes the review.

# 2. Methodology

In this section, we describe the used research strategy for collecting and analyzing the relevant research papers in this survey, as depicted in
<figure><img src="image_3.png" /><figcaption>Fig. 1. Visual representation of the survey structure.</figcaption></figure>

<table><caption>**Table 1**

Comparison of our survey and other related HSI surveys for wheat agriculture. The investigated elements are related to wheat crop analysis. PC: Period Covered, Sup: Supervised, Semi-Sup: Semi-supervised, Unsup: Unsupervised.</caption><thead><tr><th rowspan="2">Survey</th><th rowspan="2">PC</th><th rowspan="2">Datasets</th><th colspan="3">Learning Paradigms</th><th colspan="4">Applications</th></tr><tr><th>Sup</th><th>Semi-sup</th><th>Unsup</th><th>Classification</th><th>Disease</th><th>Monitoring</th><th>Yield</th></tr></thead><tbody><tr><td>2022 (Terentev et al., 2022)</td><td>2006–2021</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr><tr><td>2023 (Barbedo, 2023)</td><td>2013–2022</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr><tr><td>2025 Ours</td><td>2015–2024</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr></tbody></table>

Note: • Covered, • Partially covered, • Not covered.

**Fig. 2.** This process involves retrieving articles from several reputable scientific databases, including ScienceDirect, IEEE Xplore, Springer-Link, and MDPI. The steps followed in the screening process and review of the articles are outlined below:

1. **Search in Databases:** We initially conducted a broad literature search using comprehensive academic databases such as **Web of Science** and **Scopus** to capture a representative body of work on HSI and DL in wheat crop analysis. This exploratory phase helped establish the foundation and scope of the review. Subsequently, we performed a more focused and systematic search using domain-specific platforms including **ScienceDirect**, **IEEE Xplore**, **SpringerLink**, and **MDPI**. The following keywords were used in all searches: ("Hyperspectral Imaging" OR "HSI" AND "Deep Learning" AND "Wheat Crops"), with a publication date filter applied for the years 2015 to 2024.

2. **First Screening: Title, Abstract, and Methodology:** In this step, we reviewed the titles, abstracts, keywords, and methodology sections of the articles to determine if they met the inclusion criteria, which are:

* The study must be relevant to analyzing wheat crops from HSI data using deep learning techniques.

* The methodology must be clear, providing sufficient details for reproduction.

* The results must be clearly presented.

3. **Second Screening: Full Article Review:** More details are investigated in this step, where we thoroughly reviewed the content of the articles that met the selection criteria in the first step. Articles are excluded if they do not meet the inclusion criteria, such as lacking relevant details in the methodology, not applying deep learning to hyperspectral data, or being outdated. A total of 267 articles were discarded, and 194 were retained for further analysis.

4. **Analysis of Selected Articles and Information Extraction:** The final step involved analyzing and synthesizing the information from the selected articles. Key methodologies, results, and findings related to the application of deep learning models to wheat crop data were extracted. These included areas such as wheat classification, nutrient estimation, disease monitoring and detection, yield estimation, and other relevant tasks. This data was then used to identify trends, challenges, and future research opportunities in the field.

## 3. Hyperspectral imaging technology: Platforms and sensors

HSI is a transformative technology designed to analyze a wide spectrum of light, contrasting with traditional RGB cameras that assign primary colors (red, green, blue) to each pixel (Caballero et al., 2019). In HSI, the light striking each pixel is broken down into numerous spectral bands across a broad range of wavelengths. This process yields detailed information about the properties and composition of the imaged object, as different materials interact with light differently across the spectrum. By capturing narrow spectral bands over a continuous range, HSI produces spectra for all pixels in the scene (Kramer, 2023) (see Fig. 3).

HSI platforms and sensors provide a comprehensive view of objects by utilizing a wide range of the electromagnetic spectrum (Adão et al., 2017). These sensors operate by capturing reflected light through an imaging slit, functioning as line-scanning cameras or 'push-broom' sensors (Lu et al., 2020). This mechanism allows HSI to combine spectroscopy with imaging capability, enabling the collection of detailed data that goes beyond what traditional cameras can offer (Lodhi et al., 2018). These sensors have proven valuable in estimating the biophysical properties of landscapes, conducting thematic mapping, and analyzing surface matter and land cover (Vorovencii, 2010). They can be implemented in various ways: whiskbroom (point scanning), push-broom (line scanning), and snapshot cameras.

The effectiveness of these sensors hinges on the platforms they are installed on. Hyperspectral sensors can be mounted on different types of devices, including satellites, airplanes, UAVs, and close-quarters systems. Each platform offers distinct advantages depending on the
<figure><img src="image_4.png" /><figcaption>Fig. 2. Overview of the methodological strategy for articles search and selection in conducting this survey.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 3. AVIRISg (Airborne Visible Infrared Imaging Spectrometer next generation) HSI cube of Mount Vesuvius, Italy (Image credit: NASA/JPL) (Lundeen, 2022).</figcaption></figure>

resolution and scope of the data required. For example, satellites provide broad coverage but may sacrifice some spatial detail, making them ideal for large-scale monitoring projects. UAVs, on the other hand, are increasingly popular for more localized agricultural applications. These agile platforms can gather high-resolution data at specific intervals,

which is crucial for detailed analysis, such as crop disease monitoring and stress assessment.

In wheat crop applications, airplanes remain a popular choice for HSI, especially for mid-scale operations. However, the rise of UAVs in the past decade has revolutionized remote sensing, enabling precise, high-resolution imaging at much lower altitudes. UAVs are now widely considered as the most practical platforms for HSI in agriculture due to their flexibility and cost-effectiveness (Habib et al., 2016).

To evaluate the platforms and sensors used in HSI, Table 2 outlines the most widely used sensors, detailing their specifications and operational modes, while Table 3 showcases sensors specifically applied to wheat crop monitoring, highlighting their effectiveness in agricultural studies (Transon et al., 2018; Lodhi et al., 2018; Jia et al., 2020). The choice of platform and sensor is driven by the scale and precision needed for the study, with satellites suited for large-scale monitoring and UAVs providing high resolution for localized tasks such as nitrogen stress detection and height estimation. This approach allows for a tailored selection of sensors and platforms to meet the diverse needs of agricultural research, from broad landscape assessments to detailed crop-level analysis. In the next section, we will discuss the HSI datasets for wheat crop analysis.
<table><caption>**Table 2**
Spectral characteristics of remote sensing sensors. UAV: unmanned aerial vehicle.</caption><thead><tr><th></th><th>Sensor</th><th>Spectral Range (nm)</th><th>Number of Spectral Bands</th><th>Spectral Resolution (nm)</th></tr></thead><tbody><tr><td rowspan="3">Satellite</td><td>Hyperion</td><td>357-2576</td><td>220</td><td>10</td></tr><tr><td>PROBA-CHRIS</td><td>415-1050</td><td>19-63</td><td>34-17</td></tr><tr><td>AVIRIS</td><td>400-2500</td><td>224</td><td>10</td></tr><tr><td rowspan="3">Airplane</td><td>CASI</td><td>380-1050 (CASI-1500)</td><td>288</td><td>&lt; 3.5</td></tr><tr><td>AISA</td><td>400-970</td><td>244</td><td>3.3</td></tr><tr><td>HyMap</td><td>440-2500</td><td>128</td><td>15</td></tr><tr><td rowspan="2">UAV</td><td>Headwall Hyperspec</td><td>400-1000</td><td>270-324</td><td>6-2.5</td></tr><tr><td>UHD185-Firefly</td><td>450-950</td><td>138</td><td>4</td></tr></tbody></table>

<table><caption>**Table 3**
Hyperspectral sensors used in various wheat crop monitoring studies. UAV: unmanned aerial vehicle.</caption><thead><tr><th>Platform</th><th>Sensor</th><th>Spectral Range</th><th>Application</th><th>Reference</th></tr></thead><tbody><tr><td>Hand-held Spectrometer</td><td>ASD FieldSpec Spectroradiometer</td><td>350-2500 nm</td><td>Wheat Plant Nitrogen stress</td><td>Ranjan et al. (2012)</td></tr><tr><td>UAV</td><td>DJIS1000 UAV, SZDJI Technology</td><td>450-950 nm</td><td>Wheat Plant height estimation</td><td>Tao et al. (2020)</td></tr><tr><td>Aircraft</td><td>Micro-Hyperspec VNIR model,</td><td>400-1000 nm</td><td>Wheat Plant Phenotyping</td><td>Gonzalez-Dugo et al. (2015)</td></tr><tr><td>Airborne</td><td>Model: A-series, Micro-Hyperspec<br>Airborne sensor, VNIR Headwall</td><td>380-1000 nm</td><td>Wheat Grain yield prediction</td><td>Montesinos-López et al. (2017)</td></tr></tbody></table>

# 4. Datasets

HSI datasets are pivotal for advancing wheat crop analysis. They enable the development of robust DL models for tasks like disease detection, nutrient estimation, and yield prediction. These datasets vary in spectral range, spatial resolution, and application scope and are categorized as public or private.

## 4.1. Public datasets

Public datasets serve as foundational resources for wheat crop analysis. Table 4 provides a summary of publicly available datasets, such as GHISA (Thenkabail et al., 2018), which incorporate wheat as a distinct class among other crops. Disease-specific datasets like Soil-borne Wheat Mosaic Virus (Haagsma et al., 2019) and Fusarium Head Blight (Dhakal, 2021) enable targeted studies on pathogen detection and stress response. Additionally, stress phenotyping datasets, such as salt stress (Moghimi and Yang, 2018), provide insights into abiotic challenges, supporting the development of robust models for nutrient management and yield optimization. While these datasets enable benchmarking, many lack region-specific annotations or reflect outdated HSI technology, limiting their applicability to modern wheat farming challenges.

**GHISA** (Thenkabail et al., 2018): GHISA is a comprehensive collection of hyperspectral signatures of major global crops, developed by the US Geological Survey (USGS) with global collaboration. It covers around 65% of worldwide farmland, including crops like wheat, rice, corn, and soybeans. Hyperspectral data were collected using space-borne, aerial, and ground-based methods. For the Conterminous United States (CONUS), GHISA provides crop data at various growth stages (GHISACONUS Version 1), using Crop Observing-1 Hyperion hyperspectral data (2008–2015) and USDA Cropland Data Layer as references for key crops like wheat and cotton.

**DRUM** (Moghimi and Yang, 2018): This dataset includes hyperspectral pictures of four wheat lines, with one line subjected to salt (NaCl) treatment and control. The images, taken one day after salt application using a hyperspectral camera (PIKA II, Resonon), captured spectral responses in the visible and near-infrared range (400–900 nm) at high spatial resolution. Raw images were calibrated and transformed into reflectance values after removing 25 noisy spectral bands. Vegetation pixels were isolated from the background using vegetation indices and morphological operations. The dataset, primarily used for phenotyping wheat salt tolerance, is also suitable for developing algorithms for plant classification and spectral feature selection.

**Soilborne Wheat Mosaic Virus** (Haagsma et al., 2019): The Soil-borne dataset was gathered from both laboratory and field environments. The laboratory captured HSI of wheat leaves, while additional in situ images were taken from wheat fields and disease nurseries. The dataset is intended to assist in detecting Soilborne Wheat Mosaic Virus in wheat crops.

## 4.2. Private datasets

Private datasets focus on critical wheat diseases like Fusarium Head Blight (Bauriegel et al., 2011; Ma et al., 2019) and Yellow Rust (Zheng et al., 2018; Bohnenkamp et al., 2019; Zhang et al., 2019a), addressing gaps in public repositories Tables 5 and 6. These datasets often include high-resolution spectral data from controlled experiments or field trials, capturing disease progression, mycotoxin contamination, and physiological responses. However, restricted access and regional biases hinder their utility for cross-variety or cross-region generalization (Figueroa et al., 2018).

In Bauriegel et al. (2011) used wheat plants (cv. ‘Taifun’) for their experiments. The plants were grown under controlled conditions in a greenhouse in pots, with 16 grains in each pot. The plants were then artificially inoculated with a mixture of pathogens of the strain Fusarium Culmorum with a spore concentration of 250,000 spores ml⁻¹ on three successive days from the beginning of flowering. This controlled inoculation allowed us to study the development of Fusarium infection in wheat under laboratory conditions. They used HSI to analyze the plants and differentiate between spectra of diseased and healthy ear tissues in specific wavelength ranges. Consequently, the wavelength ranges most appropriate for the discrimination of head blight were determined, and the stage of grain development optimal for disease detection was also identified.

In Barbedo et al. (2015) introduced a wheat disease dataset consisting of 27 HSI with 803 kernels from four wheat varieties: BRS 194, Quartzo, BRS Parrudo, and BRS 179. The dataset includes images with one-third sound kernels, one-third diseased kernels, and the rest a balanced mix. The images were captured under consistent conditions to minimize variability, aiding in accurate algorithm calibration. The HSI system used for capturing the dataset includes an objective lens, spectrometer (operating in the 528–1785 nm range with 5–7 nm spectral resolution and 256 bands), a camera, conveyor, and illumination, using push-broom acquisition to gather the data line by line.

In addition (Whetton et al., 2018) presented a wheat disease dataset that includes hyperspectral measurements taken at four key growth stages of wheat and barley crops. The dataset was captured using a
<caption>**Table 4**</caption>

Public Wheat Crops Analysis datasets. Abbreviations: WL: Wavelength, SD: Spatial Dimensions, SB: Spectral Bands, NM: Not Mentioned, SWIR: Short-Wave Infrared, VNIR: Very Near Infrared, SWMV: Soilborne Wheat Mosaic Virus, WHS: Wheat HyperSpectral, CRD: Crown Rot Detection, Class: Classification, SSP: Salt Stress Phenotyping, HS: Hyperion Satellite, N-VNIR H: Nano VNIR Hyperspec.

<table><thead><tr><td>Dataset</td><td>Year</td><td>Source</td><td>WL (nm)</td><td>Classes</td><td>Samples</td><td>SD</td><td>SB</td><td>Task</td></tr></thead><tbody><tr><td>GHISA (Thenkabail et al., 2018)</td><td>2008, 2015</td><td>NASA HS</td><td>437–2345</td><td>11</td><td>NM</td><td>7000</td><td>242</td><td>Class</td></tr><tr><td>SWMV (Haagsma et al., 2019)</td><td>2019</td><td>A N-VNIR H</td><td>400–1000</td><td>2</td><td>180</td><td>NM</td><td>271</td><td>Disease Class</td></tr><tr><td>WHS (Dhakal, 2021)</td><td>2020–2021</td><td>NM</td><td>NM</td><td>2</td><td>300</td><td>NM</td><td>NM</td><td>Class</td></tr><tr><td>CRD (Liu, 2021)</td><td>2021</td><td>NM</td><td>SWIR–VNIR</td><td>2</td><td>1631</td><td>NM</td><td>NM</td><td>Disease detection</td></tr><tr><td>DRUM (Moghimi and Yang, 2018)</td><td>2018</td><td>PIKA II, Resonon</td><td>400–900</td><td>2</td><td>NM</td><td>NM</td><td>240</td><td>SSP Class</td></tr></tbody></table>

push-broom hyperspectral imager operating within the 400–1000 nm range and features averaged spectral data aligned with visual crop assessments. RGB images were also included to enrich the dataset. The paper examined how foliar health affects yield by assigning weights to different sections of the crop canopy, providing insights into the relationship between plant health and productivity.

In Ma et al. (2019) introduced a dataset consisting of two hyperspectral experiments conducted in 2018 and 2019, covering the wheat-filling stages in the 350–2500 nm range. The dataset from 2018 (Exp. 1) was collected from three experimental fields in Anhui, China, during the wheat grain filling stage. In 2019 (Exp. 2), data were gathered to capture the disease incidence across all domains. The dataset includes spectral reflectance data of healthy and Fusarium head blight-infected wheat ears, which were preprocessed and standardized to reduce noise and discrepancies between the two years.

In Zhang et al. (2019b) utilized a dataset of HSI of diseased winter wheat spikes. The experiment, conducted in Beijing, China, involved planting winter wheat in 2.0 m × 3.0 m plots and inoculating the plants with F. graminearum during the flag leaf and anthesis stages. Six diseased wheat spikes were tested every four days until the dough stage, with thirty spikes at five different growth phases selected for analysis. The dataset includes HSI captured using hyperspectral microscope imaging (HMI) equipment.

In the dataset Zhang et al. (2020) presented a wheat disease dataset with HSI data from wheat ears affected by Fusarium Head Blight (FHB) at different growth stages. The dataset included numerous narrow-band data points detailing reflectance across various wavelengths. They categorized the growth stages into late blooming, early filling, and a combination stage, and assessed FHB severity using indices like the Greenness Index (GI) and Normalized Disease Index (NDI). The data was split into training and test sets in a 3:1 ratio. Random Forest (RF) was used to identify critical wavelengths for detecting FHB, improving data processing and prediction accuracy.

In Huang et al. (2020) introduced a wheat disease dataset consisting of in situ hyperspectral data collected from stressed wheat ears. A total of 72 samples were gathered, with 24 used as verification data and 42 as training data for developing monitoring models. This dataset was essential for training and testing models designed to identify wheat scabs. The hyperspectral data captured the physical and physiological changes induced by stress, providing detailed spectral information on the wheat ears. The research utilized this data to analyze spectral reflectance characteristics, vegetation indices, and wavelet features for detecting scab-infected wheat.

Similarly, in Huang et al. (2007) introduced a wheat disease dataset consisting of multi-temporal push-broom hyperspectral imaging (PHI) obtained throughout a wheat field. The field was separated into three varieties: 98–100 (top left quadrant), Xuezao (bottom left quadrant), and Jing 411 (right half). The photos were color-coded into five illness categories: Very Serious, Serious, Middle-range, Low-range, and None. These groups were determined using the Disease Index (DI) computed from the images. The DI was measured using 120 sample locations from the field. The correlation coefficient between PHI-derived estimations of DI and actual measured DI was high. The collection comprised spectrum reflectance measurements as well as illness incidence statistics from particular sites in the field.

In Krishna et al. (2014) introduced a wheat disease dataset collected through field surveys conducted in the Indian states of Punjab and Haryana, where wheat is grown by farmers. The dataset utilized in their research was gathered in March 2013 and focused on wheat plants impacted by yellow rust disease at various severity levels. Hyperspectral reflectance data was recorded from these fields, spanning wavelengths between 350 and 2500 nm. Subsequent analysis and model building involved correlating the spectral reflectance observations with the severity of the yellow rust illness.

In another dataset (Zheng et al., 2018) introduced a wheat disease dataset created using canopy spectral measurements taken with an ASD FieldSpec spectrometer, covering a spectral range of 350 nm to 2500 nm. This dataset comprises measurements collected at a height of 1.3 meters above the ground under clear skies, capturing various stages of winter wheat development. Two experiments were conducted to obtain spectral reflectance data, focusing on different growth phases and assessing the intensity of yellow rust in infected samples. The dataset played a crucial role in developing and evaluating spectral indices and models for accurate detection and tracking of Wheat Yellow Rust.

In the dataset (Bohnenkamp et al., 2019), introduced by Bohnenkamp et al. HSI data from both UAVs and ground-based sources were used to identify and measure wheat yellow rust. Spectral data collected during field studies in 2018, spanning various wavelengths, were crucial for detecting disease symptoms and assessing plant reflectance. Ground-level canopy imaging was performed using a phytobike, while UAVs captured comprehensive field images from a height of 20 m. Additionally, the dataset provides information on wavebands, spatial resolution, and ground sampling distance (GSD), which were used to evaluate annotation techniques and prediction algorithms for yellow rust identification, enhancing the detection and classification of the disease in wheat fields.

In Zhang et al. (2019a) presented a wheat disease dataset with HSI blocks (64 × 64 × 125 pixels) labeled as “other”, “rust area”, or “healthy area”. A sliding-window technique was used to analyze the spectral and spatial data. This research, conducted at the China Academy of Agricultural Sciences, included four controlled wheat plots — two with yellow rust and two healthy — during a growing season with temperatures ranging from 5 °C to 24 °C.

In Guo et al. (2020) introduced a wheat disease dataset containing spectral data from wheat leaves. This dataset comprises 288 samples, with 141 classified as healthy and 147 as sick, divided into 96 for testing and 192 for training. Their research aims to classify leaf health and identify the most relevant wavebands from a total of 406 in the HSI data, tackling challenges related to data redundancy and collinearity.

## 4.3. Discussion

While HSI datasets have been playing a crucial role in advancing wheat crop analysis research, several limitations affect their current usefulness. Many of these datasets are outdated, lacking recent data that reflects advancements in hyperspectral technology and the evolving challenges in agriculture. Furthermore, most datasets are region-specific, which limits their applicability in diverse environmental conditions. Wheat crops face unique environmental stressors depending on the region, and datasets without region-specific information may result
<caption>**Table 5**</caption>

Private datasets for early detection of Fusarium disease in wheat. Abbreviations: WL: Wavelength, SB: Spectral Bands, IB: Important Bands, NM: Not Mentioned. ASP: ASD FieldSpec Pro spectrometer, HPH: Headwall Photonics Hyperspec 1003B-10151, GP: Gilden Photonics camera, SO: Surface Optics SOC710VP.

<table><caption>**Table 6**</caption><thead><tr><th>Dataset</th><th>Source</th><th>WL (nm)</th><th>IB</th><th>Samples</th><th>SB</th><th>Classes</th></tr></thead><tbody><tr><td>2015 (Barbedo et al., 2015)</td><td>HPH</td><td>520-1785</td><td>1411</td><td>803</td><td>256</td><td>2</td></tr><tr><td>2018 (Whetton et al., 2018)</td><td>GP</td><td>400-1000</td><td>650-700</td><td>NM</td><td>NM</td><td>2</td></tr><tr><td>2019 (Ma et al., 2019)</td><td>ASP</td><td>350-2500</td><td>471, 696, 841, 963, 1069, 2272</td><td>214</td><td>367</td><td>2</td></tr><tr><td>2019 (Zhang et al., 2019b)</td><td>SO</td><td>400-1000</td><td>447, 539, 668, 673</td><td>86</td><td>128</td><td>2</td></tr><tr><td>2020 (Zhang et al., 2020)</td><td>SO</td><td>400-1000</td><td>560-678</td><td>378</td><td>293</td><td>2</td></tr><tr><td>2020 (Huang et al., 2020)</td><td>ASP</td><td>350-2500</td><td>350-1000</td><td>NM</td><td>NM</td><td>2</td></tr></tbody></table>

Private datasets for early detection of Yellow Rust in wheat. WL: Wavelength, SB: Spectral Bands, IB: Important Bands, NM: Not Mentioned, VNIR: Very Near Infrared. Source abbreviations: ASP: ASD FieldSpec Pro, SIS: Specim ImSpector V10E, SR: Senop Rikola, CS185: Cubert S185, HVN: Headwall VNIR.

<table><thead><tr><th>Dataset</th><th>Source</th><th>WL (nm)</th><th>IB</th><th>Samples</th><th>SB</th><th>Classes</th></tr></thead><tbody><tr><td>2014 (Krishna et al., 2014)</td><td>ASP</td><td>350-2500</td><td>428, 672, 1399</td><td>36</td><td>NM</td><td>2</td></tr><tr><td>2019 (Zheng et al., 2018)</td><td>ASP</td><td>350-1000</td><td>460-1000</td><td>84</td><td>217</td><td>2</td></tr><tr><td>2019 (Bohnenkamp et al., 2019)</td><td>SIS, SR</td><td>400-1000, 500-900</td><td>594-881</td><td>40</td><td>161</td><td>2</td></tr><tr><td>2019 (Zhang et al., 2019a)</td><td>CS185</td><td>450-950</td><td>NM</td><td>15000</td><td>125</td><td>2</td></tr><tr><td>2020 (Guo et al., 2020)</td><td>HVN, CS185</td><td>400-1000</td><td>538-895</td><td>288</td><td>406</td><td>2</td></tr></tbody></table>

in less accurate predictions when applied to different geographical areas.

Moreover, the majority of available datasets focus primarily on crop classification tasks and disease detection, neglecting other essential wheat crops analysis tasks such as stress monitoring and nutrient estimation, which are critical for precise wheat crop management. This absence of detailed data reduces the effectiveness of DL models in addressing real-world agricultural challenges. Consequently, researchers may find that these datasets do not fully support the development of comprehensive models for tasks like yield estimation and nutrient management.

For researchers requiring more specialized or current data, reliance on private datasets becomes necessary. However, private datasets are often costly and difficult to obtain, with access sometimes restricted to specific institutions or organizations. Acquiring these datasets can involve considerable time, effort, and financial investment, which creates significant barriers for many researchers. As a result, the ability to access high-quality, region-specific, and up-to-date data is often limited, hindering the development of advanced DL models.

While HSI datasets have advanced wheat crop analysis, their broader applicability is hindered by outdated or region-specific data, leading to poor generalization across diverse environments. For instance, models trained on European wheat varieties (GHISA) often fail in Asian or African fields due to spectral differences caused by soil, climate, or farming practices, with transfer learning approaches reporting 25% accuracy drops in cross-region disease detection. To address these challenges, we propose synthesizing spectral variations via GAN, adopting domain-invariant techniques such as DANN, and leveraging federated learning for collaborative training, strategies validated by recent advances such as spectral unmixing for feature alignment and variety-agnostic pretraining. Unlike broader reviews (Wang et al., 2021b; Kuswidiyanto et al., 2022), which lack actionable wheat-specific solutions, our work bridges gaps in addressing spectral variability and regional adaptability through tailored domain adaptation frameworks.

These limitations highlight the ongoing need for new, non region-specific, and disease-focused datasets that better capture the complexity of wheat crop health and environmental variability. Developing more comprehensive datasets will significantly enhance the training of DL models, improving their accuracy and applicability in real-world agricultural contexts.

## 5. Deep learning models for wheat crop analysis

Deep Learning has revolutionized HSI analysis, providing cutting-edge techniques to address the challenges posed by the high dimensionality and spectral redundancy inherent in HSI data. These advanced

computational models enable efficient feature extraction, classification, and segmentation, making them indispensable tools for analyzing complex spectral-spatial information. Each DL method offers distinct strengths and faces specific limitations (Paoletti et al., 2019; Signoroni et al., 2019).

The Fig. 4 illustrates the evolution of DL approaches for wheat crop analysis using hyperspectral data across four distinct eras from 2015 to 2024. Publication trends follow a non-linear trajectory with initial growth (2015–2019), decline (2020–2021), and pronounced resurgence (2022–2024), with 2019 and 2024 representing peak research output. During the CNN Dominance Era (2015–2017), convolutional architectures prevailed for spatial feature extraction, gradually diversifying to include RNNs and generative models addressing spectral dependencies and data limitations in the GAN & Advanced CNN Era (2017–2020). The figure demonstrates significant methodological advancement across the Multimodal Era (2020–2023) and particularly during the Transformer & SSM Era (2023–2024), marked by rapid adoption of sophisticated architectures. Transformer implementations expanded substantially in 2023, while State Space Models (Mamba) emerged as a dominant approach in 2024, collectively driving unprecedented research volume.

The objective of this section is to showcase the efficacy of these DL techniques in analyzing hyperspectral data for wheat crop applications. To provide a structured analysis, the methods are categorized based on learning paradigms, which includes: supervised, semi-supervised, and unsupervised learning, as shown in Fig. 5.

### 5.1. Supervised learning

Supervised learning is the most widely adopted paradigm in HSI analysis, relying on labeled datasets to achieve precise and reliable predictions. This paradigm included models such as CNNs, RNNs, DBNs, SAEs, Transformers and Mamba models that employ various mechanisms to effectively process hyperspectral data (Li et al., 2023).

#### 5.1.1. Convolutional neural networks (CNNs)

CNNs are widely used in HSI analysis due to their ability to efficiently process high-dimensional spectral-spatial data. By leveraging 2D and 3D convolutions, CNNs excel at capturing both spatial structures and spectral dependencies, making them well-suited for analyzing complex hyperspectral datasets. Recent advancements, such as 3D CNNs, have significantly improved feature extraction and classification accuracy. Fig. 6 illustrates an example architecture of a 3D CNN, where spectral and spatial information is jointly processed. By selecting the most informative spectral bands, the model reduces dimensionality while preserving crucial spectral-spatial characteristics, enhancing classification performance (Paoletti et al., 2018).
<figure><img src="image_6.png" /><figcaption>Fig. 4. Number of published articles on DL models by year on hyperspectral data in wheat crops.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 5. Taxonomy for deep learning methods for wheat crops from HSI data: **Supervised CNNs**: 1–9. Sadeghi-Tehran et al. (2021), Dhakal et al. (2023), Zhong et al. (2017b), Chen et al. (2016), Yang et al. (2018), Bing et al. (2019), Roy et al. (2019), Ashfaq and Khan (2023), Song et al. (2018) **RNNs**: 10. Wang et al. (2021b), 11–13. Kussul et al. (2020), Venkatesan and Prabu (2019), Luo (2018) **Transformer**: 14–17. Dang et al. (2023), Sigger et al. (2024), Liu et al. (2024b), Xie et al. (2023); **Mamba**: 18–23. Sheng et al. (2024), Yang et al. (2024b), Zhou et al. (2024), Huang et al. (2024a), He et al. (2024), Yang et al. (2024a); **DBNs**: 24–27. Li et al. (2019), Sellami and Farah (2019), Mughees and Tao (2018), Zhong et al. (2017a); **SAEs**: 28–29. Xing et al. (2016), Bai et al. (2024) **Semi-Supervised Pseudo-Labeling**: 30–34. Zhao et al. (2024c), Wang et al. (2021a), Wu and Prasad (2017), Fang et al. (2018), Zhang (2020) **Generative Models**: 35–41. Zhang et al. (2023a), He et al. (2017), Zhan et al. (2023), Goodfellow et al. (2020), Chen et al. (2019), Zhu et al. (2018), Liu et al. (2020); **Unsupervised DBNs**: 42–43. Yang et al. (2019), Li et al. (2022b). **SAEs**: 44–48. Liang et al. (2020), Mughees and Tao (2016), Afrin et al. (2024), Deng et al. (2023), Cao et al. (2024); **Diffusion Models**: 49–51. Pang et al. (2024), Zhang et al. (2023b), Polk et al. (2023).</figcaption></figure>

To address the challenges associated with high-dimensional hy-
perspectral data, various CNN-based methodologies have been pro-
posed. Chen et al. (2016) introduced a 3D convolutional architec-
ture that processes spatial and spectral dimensions simultaneously,
significantly improving feature representation. Building on this, Li
et al. (2017) further refined 3D CNNs for hyperspectral classification,
while Yang et al. (2018) incorporated recurrent connections into CNNs
to enhance sequential feature learning. To improve classification accu-
racy, Dhakal et al. (2023) proposed G-Boost, an ensemble CNN model
that integrates multiple feature extraction strategies for more robust
learning.

Several architectural enhancements have further improved CNN-
based hyperspectral classification. In Zhong et al. (2017b), Zhong et al.
developed the Spectral-Spatial Residual Network (SSRN), incorporating
identity mapping residual blocks to enhance feature learning depth
and mitigate vanishing gradient issues. Roy et al. (2019) introduced
HybridSN, combining 2D and 3D CNN layers to balance computational
efficiency with robust feature extraction, enabling improved spectral-
spatial integration. Additional advancements include Fang et al. (2019),

who proposed a deep hash neural network for hyperspectral classifica-
tion, and Song et al. (2018), who developed a feature fusion network
to enhance robustness in high-dimensional spectral data.

Recent trends in CNN-based hyperspectral analysis emphasize dee-
per architectures, lightweight models, and the integration of techniques
such as residual learning and ensemble methods. Notably, Lee and
Kwon (2017) developed a 12-layer 3D CNN with residual connections,
enabling effective hierarchical feature learning. These advancements
underscore the continuous evolution of CNN architectures to address
the unique challenges of hyperspectral data processing.

5.1.2. *Recurrent neural networks (RNNs)*

RNNs are particularly well-suited for HSI analysis due to their
ability to model sequential dependencies within spectral data. Unlike
CNNs, which primarily focus on spatial feature extraction, RNNs lever-
age feedback connections to capture relationships across spectral bands.
However, traditional RNNs suffer from limitations such as vanishing
and exploding gradients, which hinder their ability to learn long-
range dependencies effectively (Chung et al., 2014). To address these
challenges, Long Short-Term Memory (LSTM) networks (Graves, 2012)
<figure><img src="image_8.png" /><figcaption>Fig. 6. 3D CNN architecture optimized for spectral-spatial feature extraction (Paoletti et al., 2018).</figcaption></figure>

introduced gating mechanisms that enable the retention of information over extended sequences, improving spectral sequence modeling. Additionally, Gated Recurrent Units (GRUs) (Chung et al., 2014) streamlined LSTM architectures by combining forget and input gates, reducing computational complexity while maintaining robust sequential feature extraction.

RNN-based models have been widely employed in HSI classification and remote sensing applications. Mou et al. (2017a) demonstrated that treating hyperspectral pixels as sequential data enhances feature learning, improving classification accuracy. Similarly, Venkatesan and Prabu (2019) employed RNNs to effectively model spectral dependencies, incorporating specialized activation functions to stabilize training and enhance performance.

To further optimize spectral-spatial integration, Wang et al. (2021b) introduced Bi-CLSTM, a bidirectional LSTM variant that eliminates the need for sliding-window segmentation, improving classification accuracy by 1.5% over 3D CNNs on the Indian Pines dataset. Beyond classification, RNNs have also proven effective in broader remote sensing tasks. In Ndikumana et al. (2018), E. Ndikumana et al. applied RNN-based architectures for agricultural land cover mapping using Sentinel-1 radar data, demonstrating superior categorization performance by explicitly leveraging temporal dependencies in multitemporal datasets.

Salmela et al. (2021) introduced an LSTM-based RNN to model ultrafast nonlinear dynamics in optical fibers, effectively bypassing traditional numerical methods like the NLSE. This approach achieves faster and more accurate modeling by taking advantage of the RNN's ability to process sequential data and retain long-term dependencies via internal memory. However, the reliance of the method on simulation-generated training data raises concerns about its applicability to real-world scenarios.

Luo (2018) introduced a shorten spatial-spectral RNN with Parallel-GRU for HSI classification, which combines 3D convolutional layers and a novel Parallel-GRU architecture to efficiently capture both spatial and spectral features. The model leverages a shorten RNN structure to reduce computational complexity and improve training efficiency, while the Parallel-GRU architecture enhances robustness and performance by aggregating outputs from multiple GRU units. However, the model's reliance on carefully designed subgraphs and convolutional kernels may limit its generalization to diverse HSI datasets with varying spatial and spectral characteristics.

### 5.1.3. Deep belief networks (DBNs)

DBNs are hierarchical generative models composed of stacked Restricted Boltzmann Machines (RBMs) that learn layered representations of data through unsupervised pretraining. Their ability to extract

spectral-spatial features and reduce dimensionality makes them well-suited for HSI classification, particularly when labeled samples are limited (Hinton, 2006). By leveraging unsupervised feature learning, DBNs can effectively model complex spectral dependencies, improving classification accuracy and computational efficiency.

Several DBN-based methodologies have been developed to optimize HSI classification. Mughees and Tao (2018) proposed a Spectral-Adaptive Segmented DBN, which partitions spectral bands into localized subgroups, processes them with specialized DBNs, and integrates spatial features via hypersegmentation. This approach improves classification accuracy while reducing computational costs. Similarly, Li et al. (2019) introduced a DBN-based framework that incorporates multivariate optical sensors and a two-phase training strategy. The model utilizes unsupervised RBM pretraining to extract latent spectral-spatial features, followed by supervised fine-tuning to enhance classification performance.

Beyond general classification tasks, DBNs have demonstrated strong potential in domain-specific applications, such as identifying subtle spectral variations in wheat disease classification and analyzing hyperspectral data with limited labeled samples. P. Zhong et al. (2017a) developed a DBN model capable of capturing shared spectral-spatial patterns, leveraging binary latent variables to represent input features. Fig. 10 illustrates this DBN architecture, where gray nodes denote input features and yellow/red nodes represent binary latent states (0 and 1), showcasing the model's hierarchical learning capabilities.

A hybrid method proposed by Sellami and Farah (2019) introduces a graph-based supervised learning framework for HSI classification. This approach constructs a spectro-spatial graph, where nodes correspond to pixel spectral vectors and edges capture spatial relationships between neighboring pixels. To preserve the local manifold structure of the data, the method leverages a Restricted Boltzmann Machine for feature extraction. Classification is subsequently performed using a Deep Belief Network combined with Logistic Regression, achieving notable accuracy in HSI tasks. The study highlights the effectiveness of graph-based methods in addressing the complex challenges of hyperspectral data analysis.

### 5.1.4. Stacked autoencoder

SAEs have been effectively applied to supervised learning tasks in HSI analysis. By hierarchically extracting features, SAEs reduce the dimensionality of HSI data, capturing both spectral and spatial information essential for accurate classification (Wan et al., 2017). Fig. 7 depicts an autoencoder architecture for hyperspectral data. From which, each spectrum is the input and reconstruction target, with the code layer creating a compact feature representation. The trained encoder maps spectra to a lower-dimensional feature space (Windrim
<figure><img src="image_9.png" /><figcaption>Fig. 7. Optimized stacked autoencoder architecture for HSI (Windrim et al., 2019).</figcaption></figure>

et al., 2019). In supervised learning, SAEs are typically pre-trained in an unsupervised manner to learn robust feature representations from unlabeled data. Subsequently, these representations are fine-tuned using labeled data to optimize performance for specific tasks, such as land cover classification or object detection. This approach enhances the model's ability to generalize from limited labeled samples, a common scenario in HSI analysis (Bai et al., 2024).

Recent advancements in SAEs for HSI have introduced diverse architectures, including standard SAEs, convolutional SAEs, and hybrid designs. While basic SAEs focus on dimensionality reduction, advanced models integrate spectral and spatial feature extraction, and hybrid approaches often combine SAEs with techniques like genetic algorithms or supervised fine-tuning to optimize classification performance on complex HSI datasets. Liang et al. (2020) introduced an SAE model combined with a genetic algorithm (GA) for wavelength selection, achieving efficient detection of deoxynivalenol (DON) levels in wheat. Their SNV-GA-SAE model excelled for wheat flour samples, while the MSC-GA-SAE model performed well for wheat kernel classification. Bai et al. (2024) proposed a Two-stage, Multi-dimensional Convolutional Stacked Autoencoder framework, which employs spectral feature extraction through dimensionality reduction in the first stage and spatial feature extraction to capture local neighborhood patterns in the second stage. Both stages are fine-tuned in a supervised manner, optimizing classification performance while addressing data scarcity challenges. Xing et al. (2016) utilized Stacked Denoising Autoencoders to reconstruct inputs corrupted with noise during unsupervised pretraining, effectively capturing underlying data structures and enhancing classification accuracy through supervised fine-tuning for HSI tasks. The evolution of SAEs continues through hybrid architectures and advanced pre-training strategies, such as combining genetic algorithms with autoencoder models, which have demonstrated significant improvements in feature selection and classification accuracy (Singh and Karthikeyan, 2022).

### 5.1.5. Transformer

Transformers were initially introduced for Natural Language Processing (NLP) tasks, with self-attention as their foundational mechanism (Vaswani et al., 2017). This attention mechanism excels at capturing long-range dependencies within input sequences by effectively weighting the relationships between elements, regardless of their distance. The success of Transformer architectures in NLP has led to their adaptation across various domains, including HSI. In the context of HSI, Transformers demonstrate significant advantages over traditional DL methods by effectively capturing spectral-spatial dependencies and reducing the need for handcrafted feature extraction (He et al., 2021). Fig. 8 illustrates an example Transformer model proposed by Dang et al. (2023), which integrates spectral and spatial branches enhanced with attention mechanisms and Transformer encoder blocks. A feature fusion

layer, inspired by CrossViT34, seamlessly combines features from both branches, enabling robust and efficient spectral-spatial processing.

Several Transformer-based architectures have been developed for HSI analysis, demonstrating their adaptability in learning spectral-spatial relationships. Sigger et al. (2024) introduced DiffSpectralNet, which integrates diffusion models with Transformers to enhance hyperspectral classification by capturing complex spectral-spatial correlations. The model combines unsupervised feature extraction via denoising diffusion probabilistic models with supervised classification using a hierarchical Transformer, effectively addressing the high dimensionality of HSI data.

Liu et al. (2024b) proposed CESA-MCFormer, an architecture incorporating a Center-Enhanced Spatial Attention module and Morphological Convolution to minimize spectral and spatial redundancy while enhancing central pixel focus. Unlike conventional methods relying on dimensionality reduction techniques such as PCA, this low-parameter model achieves robust classification across multiple datasets while preserving spectral integrity.

Further improving Transformer-based HSI models, Xie et al. (2023) developed HyperSFormer, an advanced adaptation of the SegFormer architecture. The model leverages an improved Swin Transformer for encoding global spectral-spatial features and includes a decoder for precise classification. Key components of this architecture include the Hyper Patch Embedding module for effective feature extraction, the Transpose Padding Upsample module for accurate end-to-end classification, and the Adaptive Min Log Sampling strategy. Additionally, it employs a combination of dice and focal loss functions to address class imbalance and improve performance on challenging HSI datasets.

Transformer-based architectures are particularly promising for wheat disease detection and nutrient monitoring, due to their ability to model long-range dependencies across spectral and spatial dimensions (Xie et al., 2023). For instance, wheat yellow rust often presents subtle canopy-level spectral variations that manifest in scattered reflectance shifts between (520–680 nm) patterns that traditional CNNs may miss (Ren et al., 2021; Zidi et al., 2025). Models like HyperSFormer, with its spectral-spatial attention modules and patch-based encoding, are highly suited for identifying such irregularities across entire wheat plots. Additionally, in the case of nitrogen deficiency monitoring, where wheat leaves exhibit broadband reflectance reductions in the (700–900 nm) range, Transformer attention layers allow nuanced tracking of progressive stress symptoms (Wu et al., 2012). The capability of Transformers to generalize across diverse conditions and detect non-local spectral patterns supports their role in field-deployable wheat-specific precision farming systems.

### 5.1.6. Mamba-based models

Recently, Mamba has emerged as an efficient alternative to Transformer architectures for long-range dependency modeling. Building upon the Structured State Space (S4) model (Gu et al., 2021), Mamba
<figure><img src="image_10.png" /><figcaption>Fig. 8. The overall structure of the proposed model using the transformer architecture (Dang et al., 2023).</figcaption></figure>

introduces a novel sequence modeling mechanism that reduces computational costs while maintaining strong feature extraction capabilities. Unlike self-attention-based Transformers, Mamba leverages structured state-space models to dynamically adjust parameters according to input characteristics, improving computational efficiency and adaptability across diverse domains, including text analysis, audio processing, and real-time translation (Gu and Dao, 2023).

Given its lightweight architecture and innovative sequence modeling approach, Mamba has been widely explored for HSI classification, where it effectively captures spectral and spatial dependencies while maintaining efficiency. Sheng et al. (2024) introduced DualMamba, a framework that integrates global and local feature extraction, achieving high overall accuracy across multiple hyperspectral datasets while significantly reducing computational demands.

Further expanding Mamba's applications in HSI, Yang et al. (2024b) developed HSIMamba, incorporating data augmentation strategies to enhance robustness and classification performance. The model achieved an Overall Accuracy (OA) of 0.899% and a Kappa coefficient of 0.8857 on the Indian Pines dataset, outperforming traditional architectures in managing long spectral sequences.

To optimize computational efficiency, Zhou et al. (2024) designed a Mamba-based spectral-spatial integration framework, leveraging the efficiency of Mamba modules to enhance classification accuracy with minimal resource consumption. Meanwhile, Yang et al. (2024a) proposed GraphMamba, incorporating graph structure learning to address challenges such as spectral redundancy and complex spatial relationships, achieving superior classification performance across diverse hyperspectral datasets.

In addition, He et al. (2024) introduced the 3D-Spectral-Spatial Mamba model, which employs a novel sequence modeling approach based on the State Space Model (SSM). The framework integrates a Spectral-Spatial Token Generation module and a 3DSS mechanism to capture global spectral interactions while maintaining computational efficiency. Expanding Mamba's applications, Huang et al. (2024a) evaluated its effectiveness in hyperspectral agricultural tasks such as wheat classification, demonstrating the model's adaptability to real-world HSI applications, although specific applications in wheat agriculture remain underexplored in current literature.

Mamba's architectural advantages are particularly valuable in the context of real-time wheat field monitoring. Unlike Transformer models, which often demand high memory resources, Mamba-based frameworks such as HSIMamba and 3DSS-Mamba offer a lightweight solution suited to deployment on UAVs and edge devices for in-field analysis (Li et al., 2024). For example, early detection of diseases like Fusarium Head Blight, characterized by subtle reflectance changes in the (500-600 nm) range, requires efficient models capable of processing high-resolution HSI data with minimal latency (Rangarajan et al., 2022). Similarly, Mamba's ability to maintain long-range spectral awareness while optimizing for low computational overhead makes it a compelling candidate for monitoring nitrogen stress in wheat crops, notably observed in the (700-900 nm) spectral band. These traits enable

scalable, on-board inference for precision wheat agriculture, directly aligning with the demands of operational crop health surveillance systems (Liu et al., 2017).

## 5.2. Semi-supervised learning

Semi-supervised learning (SSL) methods offer a powerful approach by exploiting both labeled and unlabeled data to improve classification performance. These methods are especially valuable in HSI, where acquiring labeled samples is both costly and time-consuming. The used semi-supervised techniques for wheat crops analysis can be categorized into two main categories: pseudo-labeling and generative models.

### 5.2.1. Pseudo-labeling

Pseudo-labeling techniques play a crucial role in HSI by leveraging unlabeled data through iterative label refinement based on model predictions. This approach is particularly valuable in HSI, where obtaining labeled data is both costly and time-consuming, enabling more accurate and robust analysis of complex spectral information (Kotzagiannidis and Schönlieb, 2021).

Zhao et al. (2024c) introduced a semi-supervised framework for HSI classification that integrates pseudo-labeling with active learning. The model employs a progressive pseudo-label selection strategy, which refines labels iteratively by enforcing spatial-spectral consistency, linking high-confidence samples within semantically connected regions. It utilizes a hybrid classification backbone, combining 3D-CNNs for spectral-spatial feature extraction and vision transformers for global contextual understanding. Active learning further enhances the process by selecting high-value regions or samples for expert annotation based on temporal confidence variations, optimizing label quality while minimizing manual annotation.

Expanding on active learning strategies, Wang et al. (2021a) proposed an improved semi-supervised classification framework that integrates active DL with a random multi-graph algorithm. Instead of relying on manual labeling, this framework employs an anchor graph approach to iteratively generate pseudo-labels, expanding the training set. Initially, a CNN is trained on a small labeled dataset and then used to predict category probabilities for unlabeled data. Informative samples are selected through active learning strategies, such as entropy and best-versus-second-best (BVSB) metrics, and pseudo-labels are refined using a random multi-graph algorithm. The key contributions of this method include eliminating manual labeling dependency and incorporating anchor graphs for efficient pseudo-labeling, achieving state-of-the-art accuracy across multiple datasets. However, its effectiveness heavily relies on the quality of initial labels and the robustness of pseudo-labeling under noisy conditions.

Complementing these approaches, Wu and Prasad (2017) utilized semi-supervised learning to enhance feature learning and mitigate overfitting by deriving pseudo-labels from clustering algorithms. This strategy enables networks to leverage pseudo-labels for pretraining, leading
to better initialization and faster convergence during fine-tuning. The method also effectively handles unknown background classes, making it robust for real-world applications. Similarly, Fang et al. (2018) introduced a co-training framework where spectral and spatial ResNets were trained independently, refining pseudo-labels iteratively. Their model integrates 1D CNNs for spectral features and PCA-reduced 2D CNNs for spatial features, ensuring robustness through consistency regularization.

In Chen et al. (2024) proposed Prototype-Based Pseudo-Label Refinement (PPLR), a method designed to improve semi-supervised HSI classification by addressing noisy pseudo-labels through adaptive thresholds and class prototypes. The approach dynamically adjusts confidence thresholds for pseudo-labeling while refining labels using spectral-spatial prototypes derived from both labeled and unlabeled data. To enhance robustness, PPLR incorporates a center loss function to minimize intra-class feature distances, achieving state-of-the-art classification accuracies of 82.11% on the Indian Pines dataset with minimal labeled samples.

### 5.2.2. Generative models

Generative models learn underlying data distributions to generate synthetic samples, making them particularly impactful in semi-supervised learning scenarios where labeled data is scarce. These models leverage both labeled and unlabeled data, significantly enhancing classification performance in HSI (He et al., 2017).

The integration of generative models, particularly Generative Adversarial Networks (GANs), has led to substantial advancements in HSI classification. These models are extensively used for data augmentation, enabling the creation of synthetic hyperspectral datasets that improve the robustness and accuracy of classification models. Such techniques have been especially beneficial in agricultural applications, such as wheat disease detection (Goodfellow et al., 2020). By reducing dependency on extensively labeled datasets, GANs provide practical and efficient solutions for hyperspectral analysis. Fig. 9 illustrates the architecture proposed by Benjdira et al. (2020), where a generator maps target images to semantic labels, and a discriminator differentiates between real and generated label-image pairs during adversarial training.

Recent GAN-based methodologies have introduced specialized architectures to tackle unique challenges in HSI classification. Standard GANs focus on generating general synthetic hyperspectral data for augmentation (Chen et al., 2019), while Conditional GANs apply additional constraints, such as spectral labels, to improve control over generated samples (Liu et al., 2020). More advanced models include Capsule GAN (Xue, 2020), MDGAN (Gao et al., 2019), and 3DBF-GAN (He et al., 2017), each optimized for handling the high-dimensional spectral-spatial nature of HSI.

Several studies have extended the application of GANs to agricultural and hyperspectral quality control tasks. Li et al. (2022d) introduced a Deep Convolutional GAN (DCGAN)-based framework to address dataset imbalance in wheat crop analysis. By generating synthetic data for unsound wheat kernels, the approach improved classification accuracy from 79.17% to 96.67%, mitigating overfitting and providing a robust solution for agricultural quality control.

Further improving GAN efficiency, Wang et al. (2020) developed the Adaptive DropBlock-Enhanced GAN, which incorporates a dynamic regularization mechanism to remove feature blocks adaptively. This approach enhances model generalization and achieves superior classification accuracy across wheat crop datasets, outperforming traditional methods. Additionally, Zhang et al. (2023a) introduced the Generative Adversarial-driven Cross-aware Network (GACNet), a hybrid framework integrating a semi-supervised GAN for data augmentation with a Cross-aware Attention Network for wheat variety identification. By refining spectral, spatial, and texture features, GACNet effectively addresses data scarcity, redundancy, and noise interference in HSI.

Further advancements in GAN-based HSI classification include the work of Zhan et al. (2023), who proposed a semi-supervised GAN employing Spectral Angle Distance as a loss function. This method improves GAN convergence and generates realistic spectral samples, significantly enhancing classification performance on benchmark HSI datasets.

## 5.3. Unsupervised learning

Unsupervised learning plays a crucial role in HSI by uncovering hidden patterns and structures within data without requiring labeled samples. This makes it particularly suitable for large-scale datasets where manual annotation is impractical. This section categorizes unsupervised methods into two key areas: preprocessing techniques and clustering methods.

Preprocessing methods, such as dimensionality reduction (Huang et al., 2019) and denoising (Pang et al., 2024), enhance data quality by transforming high-dimensional hyperspectral data into more manageable and informative representations. Techniques such as Deep Belief Networks (DBNs) (Li et al., 2014, 2022b) and Stacked Autoencoders (SAEs) (Windrim et al., 2019; Afrin et al., 2024) are widely employed for feature extraction, noise elimination, and spectral-spatial integration, providing robust solutions for unsupervised feature learning.

Clustering methods focus on identifying intrinsic data structures to enable effective classification. Advanced techniques, including diffusion models (Zhang et al., 2023b) and unsupervised clustering algorithms such as Deep Variational Information Clustering (DVIC) (Polk et al., 2023) and Change Vector Analysis (Johnson and Kasischke, 1998), address challenges such as spectral mixing, segmentation, and change detection. These approaches leverage probabilistic frameworks and geometric analysis to improve spectral and spatial feature extraction, often achieving performance comparable to supervised methods while significantly reducing reliance on labeled data.

A key application of unsupervised learning in HSI is Hyperspectral Change Detection (HCD), which analyzes temporal variations in HSI to monitor environmental or agricultural changes, such as shifts in crop health or disease progression (Li et al., 2021). Unlike supervised approaches, unsupervised HCD frameworks (Liu et al., 2016) eliminate the need for labeled training data by leveraging spectral-temporal divergence metrics, making them particularly robust for large-scale applications like precision agriculture. These methods have been successfully applied to tracking wheat disease outbreaks by detecting subtle spectral anomalies associated with fungal infections (Terentev et al., 2022), demonstrating their utility in early disease detection and mitigation strategies.

### 5.3.1. Preprocessing methods

DBNs, composed of multiple layers of RBMs, are highly effective for unsupervised feature learning, making them a valuable tool in HSI analysis. These networks excel at reducing the dimensionality of hyperspectral data while retaining critical spectral and spatial features (Li et al., 2014).

Recent advancements have leveraged DBNs to enhance HSI analysis. Li et al. (2022b) proposed a manifold-based multi-DBN method, incorporating hierarchical initialization to capture local geometric structures and a discrimination manifold layer to improve feature separability. This approach achieved notable classification accuracies of 78.25%, 90.48%, and 97.35% on the IP, SA, and Botswana datasets, respectively, surpassing state-of-the-art methods. Efforts to advance DBNs for HSI have focused on optimizing learning efficiency and feature representation, with techniques such as manifold-based feature extraction and local global integration demonstrating their capability to handle complex hyperspectral datasets.

SAEs are widely recognized for their excellence in unsupervised feature learning, especially in HSI. These methods compress high-dimensional data into manageable representations, preserving crucial
<figure><img src="image_11.png" /><figcaption>Fig. 9. The architectural framework delineating the initial Generative Adversarial Network (GAN) comprises two fundamental components: (a) the generator and (b) the discriminator (Benjdira et al., 2020).</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Fig. 10. Graphical representation of the DBN for HSI classification (Zhong et al., 2017a).</figcaption></figure>

spectral and spatial features. Their adaptability to complex data structures has made SAEs instrumental in applications like dimension reduction, noise elimination, and spectral-spatial integration.

Notable advancements in SAEs for HSI include Afrin et al. (2024) lossy compression method that achieves high Peak Signal-to-Noise Ratios and 99% classification accuracy on benchmark datasets. Additionally, Mou et al. (2017b) developed a deep residual Conv–Deconv network for spectral–spatial feature extraction, demonstrating SAE’s potential in unsupervised object detection. Guerri et al. (2024) highlights that SAEs excel in reducing dimensionality while preserving critical spectral–spatial details, aiding tasks such as classification and target detection in HSI.

Significant advances in unsupervised feature learning for hyperspectral data include the work of Windrim et al. (2019), who proposed

SID-SAE and SA-SAE—specialized autoencoders designed for dimensionality reduction. These methods integrate remote-sensing spectral similarity measures into their loss functions, with SID-SAE employing Spectral Information Divergence to assess probabilistic similarity and SA-SAE using Spectral Angle to emphasize shape over magnitude. By preserving critical spectral properties, these methods improve class separability and support clustering and classification tasks, particularly in scenarios with limited labeled data. To evaluate their effectiveness, the low-dimensional features were clustered using k-means, with the number of clusters corresponding to the dataset classes, and clustering performance measured using the Adjusted Rand Index. Additionally, the unsupervised features were assessed with supervised classifiers such as Support Vector Machines, k-Nearest Neighbors, and Decision Trees, highlighting the robustness of these methods across diverse evaluation strategies.
Pang et al. (2024) proposed HIR-Diff for unsupervised HSI restoration. Using pre-trained diffusion models with data fidelity terms, total variation priors, and low-rank approximation (SVD and RRQR), HIR-Diff addresses tasks like denoising and super-resolution, achieving efficient and accurate restoration. The model has a robust generative approach, is data-efficient (no paired training data), is adaptable across tasks with high computational costs, and has dataset-specific parameter tuning.

### 5.3.2. Clustering methods (classification)

Clustering methods for HSI focus on grouping pixels based on spectral-spatial similarities, enabling effective classification without labeled data. Recent advancements have integrated DL techniques such as diffusion models and SAEs into unsupervised segmentation frameworks, enhancing feature extraction and clustering accuracy. Additionally, anomaly change detection methods have emerged as a key approach for identifying temporal changes in hyperspectral data, further advancing the capabilities of unsupervised learning in dynamic environments. These techniques improve the robustness and scalability of clustering methods, facilitating more efficient analysis of high-dimensional HSI datasets.

Notable advancements in diffusion models for HSI include Zhang et al. (2023b), introducing the DiffUCD framework for hyperspectral change detection. This approach employs a Semantic Correlation Diffusion Model to extract spectral-spatial features and a Cross-Temporal Contrastive Learning mechanism to align spectral representations across time. SCDM enhances meaningful feature representation while addressing environmental changes and imaging inconsistencies. Meanwhile, CTCL mitigates pseudo-changes and improves feature invariance by leveraging abundant unlabeled samples, eliminating reliance on labeled data, and reducing the need for labor-intensive annotation. DiffUCD demonstrates the potential of diffusion models in remote sensing, achieving competitive results against supervised techniques while enhancing labeling efficiency.

Polk et al. (2023) developed D-VIC, an unsupervised clustering algorithm for HSI that integrates high-dimensional geometry and spectral abundance. Using diffusion distances and pixel purity, D-VIC identifies cluster modes representative of material structures in spectrally mixed HSIs. A modular spectral unmixing step combines HySime, AVMAX, and a non-negative least-squares solver to effectively estimate material abundances and purities. Experimental results reveal that D-VIC outperforms state-of-the-art methods on benchmark datasets, demonstrating robustness to hyperparameter tuning and scalability to large HSIs. However, the method remains computationally intensive because of spectral unmixing and diffusion-difference computations.

Mughees and Tao (2016) introduced a method that integrates DL with unsupervised segmentation. SAEs were used to extract spectral features, which were then combined with spatial information using a boundary adjustment technique and a maximum voting approach, achieving significant improvements in spectral-spatial classification accuracy on standard hyperspectral datasets.

In hyperspectral anomaly change detection, several methods aim to improve accuracy and reduce reliance on labeled data by tackling challenges in feature extraction, anomaly representation, and computational efficiency. For instance, BCG-Net (Hu et al., 2023), an unsupervised multi-task framework, enhances detection accuracy in both binary and multiclass tasks. Similarly, DSFA (Du et al., 2019) integrates DL with slow feature analysis to emphasize changes, surpassing other models in binary detection performance.

Building on these methods, Hu et al. (2022) introduces HyperNet, a self-supervised framework designed for hyperspectral change detection. HyperNet features a spatial-spectral attention module for precise pixel-level extraction and a focal cosine loss function that optimizes training by balancing easy and hard samples. Unlike patch-based models, it processes entire images, improving generalization across datasets. However, HyperNet faces challenges in computational complexity and sensitivity to noisy data, which require further exploration.

## 5.4. Discussion

DL models have significantly advanced the field of HSI analysis, offering powerful tools for classification, segmentation, and interpretation across diverse datasets. These models, while diverse in architecture and function, share the common goal of enhancing data analysis and accuracy, particularly in applications such as wheat crop health monitoring and disease detection. Their performance, as summarized in Tables 7 and 8, highlights both their strengths and limitations in various HSI tasks.

CNNs stand out as the most widely used DL models for HSI analysis, with their ability to automatically extract intricate spectral and spatial features from hyperspectral data cubes. CNNs demonstrate exceptional classification accuracy, achieving up to 99.9% on datasets such as PU and SA. Their strength lies in the layered structure, which efficiently captures both spatial and spectral hierarchies, making CNNs ideal for tasks such as land use classification and disease detection in wheat crops. However, while CNNs excel in general classification tasks, their reliance on large labeled datasets can be a limitation in scenarios where annotated data are scarce.

Moving beyond static spatial-spectral analysis, RNNs offer a unique advantage in handling sequential data by capturing temporal dependencies between spectral bands. This capability is particularly useful in agricultural applications where spectral data vary over time, such as in the tracking of wheat crop growth or monitoring environmental stressors. RNNs perform well with accuracies between 88.6% and 97.5%, but they often struggle with computational efficiency and scalability when processing long sequences, especially in high-dimensional hyperspectral data.

In contrast, GANs address a critical challenge in HSI analysis: the limited availability of labeled data. By generating synthetic samples, GANs improve data augmentation and enable more robust training for classification models. However, the performance of GANs can vary widely, with accuracies ranging from 75.6% to 99.8%. While effective in data augmentation and enhancing model robustness, GANs are sensitive to training instability and often require careful tuning to prevent overfitting, particularly in complex agricultural datasets like those involving wheat crops.

DBNs and SAEs both excel in unsupervised feature learning, offering powerful methods for dimensionality reduction while retaining essential spectral and spatial features. DBNs, with accuracies ranging from 78.2% to 98.9%, are particularly useful in scenarios with limited labeled data, allowing for effective feature extraction without extensive supervision. SAEs also perform well in tasks such as the detection of DON levels in wheat kernels, achieving up to 98.5% accuracy. These models are instrumental in automated feature extraction, but their reliance on multi-layered architectures can lead to longer training times and higher computational costs. Diffusion models have also emerged as promising tools for HSI analysis, particularly for image restoration and anomaly detection tasks. By simulating noise addition and removal processes, diffusion models effectively capture spectral-spatial dependencies in hyperspectral data, enhancing the quality of low-resolution or noisy images.

Dimensionality reduction plays a key role in improving the efficiency of DL models for wheat HSI analysis. Among the various techniques, Principal Component Analysis (PCA) is the most widely adopted, as it effectively reduces spectral redundancy while preserving critical information, making it particularly useful for CNNs and Transformers. Band selection methods, such as mutual information-based selection, are also employed in disease detection to retain the most relevant spectral bands. Additionally, autoencoders (SAEs, DBNs) have shown promise in extracting features in an unsupervised manner, especially when labeled data is scarce. While techniques like t-SNE are useful for data visualization, their computational complexity limits their practical application. Given its efficiency and reliability, PCA
remains the preferred choice for dimensionality reduction in wheat
crop analysis.

Among the state-of-the-art models, transformer architectures have emerged as particularly effective in HSI classification because of their ability to model extensive spectral relationships across bands. Achiev- ing up to 99.8% accuracy, transformer models are particularly well suited for complex tasks like wheat crop classification, where sub- tle variations across nonadjacent bands can be critical. The Mamba module further enhances the robustness of HSI analysis by integrat- ing spatial and spectral information more effectively. With accuracy rates exceeding 99% in some cases, Mamba models are highly ef- fective for wheat crop monitoring and disease detection, offering a computationally efficient alternative to traditional models.

Transformers' ability to detect subtle spectral shifts (e.g., early Fusarium infections at 500-600 nm) makes them powerful for precision diagnostics, but their quadratic complexity delays field interventions—a critical flaw when rapid UAV-based monitoring is required to prevent crop loss (Agrawal and Arafat, 2024). Mamba's linear efficiency enables real-time workflows, but its inability to resolve overlapping spectral signatures (e.g., distinguishing nitrogen deficiency from drought stress in 700-900 nm bands) risks misdiagnosis in complex field conditions, undermining farmer trust. For agriculture, this forces a trade-off: Transformers' precision at the cost of actionable timeliness versus Mamba's speed at the cost of reliability—a gap demanding domain-specific architectures that embed agricultural priors (e.g., soil-health constraints) into lightweight models (Huang et al., 2024b; Zhao et al., 2024a).

Future advancements in DL models for HSI analysis should pri-
oritize computationally efficient and lightweight architectures capa-
ble of effectively scaling to large datasets, as highlighted for CNNs,
RNNs, and SAEs. The integration of attention mechanisms in RNNs and
Transformer-based architectures could significantly enhance the cap-
ture of long-range spectral-spatial dependencies, improving real-time
processing and generalization across diverse agricultural applications.
Hybrid approaches, such as combining DBNs with other DL paradigms
and innovations such as advanced loss functions and domain-specific
attention modules in Transformers, may further optimize performance
and robustness. Additionally, the exploration of SAE and DBN models
with applications in large-scale agricultural monitoring, real-time anal-
ysis, and improved spectral-spatial integration represents a promising
avenue for innovation in HSI analysis.

In summary, while CNNs and Transformers provide the highest accuracy and robustness in HSI tasks, other models like RNNs, GANs, DBNs, and SAEs each contribute uniquely depending on the application. CNNs and Transformers dominate in classification and spatial-spectral analysis, whereas RNNs and GANs are better suited for sequential data processing and augmentation. Using the strengths of each model, researchers can address the unique challenges of HSI analysis in agriculture, particularly for the monitoring of wheat crop health and the prediction of yields. The selection of the appropriate model depends on the specific requirements of the task, such as data availability, computational efficiency, and the complexity of the spectral-spatial relationships in the dataset.

6. Applications of HSI in wheat crops

Extensive research on HSI has demonstrated the high reliability of DL algorithms, particularly in agricultural applications for wheat crops. The pie chart in Fig. 11 illustrates the distribution of research efforts across four key categories. Classification dominates at 68.0%, underscoring its fundamental role as the backbone of HSI-based wheat analysis. Disease Detection and Monitoring follows at 13.1%, reflecting its critical yet complementary function in ensuring crop health. Nutrient Estimation, at 11.8%, signifies a notable focus on optimizing wheat growth and productivity. Finally, Yield Estimation, accounting for 7.2%, represents a more specialized but still relevant area of research. This distribution highlights classification as the primary driver

<figure><img src="image_13.png" /><figcaption>Fig. 11. Number of published articles by year on DL with hyperspectral data in wheat crops.</figcaption></figure>

of research, while disease monitoring and nutrient estimation serve
as essential supporting components. Despite receiving comparatively
less attention, yield estimation remains integral to a comprehensive
approach to wheat cultivation. The following applications will be con-
sidered in this section: Wheat Crop Classification, Wheat Crop Disease
Detection and Monitoring, Wheat Crop Nutrient Estimation, and Wheat
Crop Yield Estimation.

6.1. Wheat crop classification

Agricultural crop classification has made substantial use of HSI, which enables precise crop discrimination through detailed spectral information. Classifying wheat harvests and differentiating between wheat varieties, grain types, and physiological features are among the applications where HSI has been instrumental (Madhavan et al., 2023). By analyzing subtle spectral variations, HSI allows for identification of chlorophyll content, leaf structure, and other physiological traits specific to each wheat variety (Sethy, 2022; Aha, 2023). These advancements have led to the development of robust DL models that significantly improve classification accuracy.

Table 9 summarizes key research efforts on HSI-based wheat clas-
sification, showcasing the diverse range of deep learning techniques
applied in this domain. Studies have demonstrated the effectiveness of
CNNs, multi-layer perceptrons (MLPs), and hybrid DL approaches in
achieving high classification accuracy. As research continues to evolve,
integrating large-scale datasets and optimizing model architectures
remains crucial for improving scalability and performance.

Dreier et al. (2022) demonstrated the effectiveness of deep CNNs for wheat grain classification, showing that these models outperform traditional approaches in recognizing wheat grain variations. Similarly, Sethy (2022) employed a Neural Network Pattern Recognizer to classify wheat grains using a dataset from the University of California, Irvine's machine learning repository, achieving a classification accuracy of 96.7%. Their results highlight the robustness of machine learning techniques in distinguishing wheat varieties with high precision.

In addition to spectral-based classification, geometric characteristics have also been utilized to enhance classification performance. Lingwal et al. (2021) demonstrated that the shape and structural characteristics of wheat grains could be effectively leveraged to improve classification accuracy. Expanding on this, Sabanci et al. (2017) developed an MLP-based computer vision system to classify wheat grains into bread and durum varieties, integrating high-resolution imaging to capture spectral and textural differences, thereby improving the reliability of wheat-type identification.

Further advances in DL-based hyperspectral classification were in-
troduced by Hu et al. (2015), who designed a five-layer deep CNN
<caption>**Table 7**</caption>

A summary of the results obtained from DL methods, including CNN, RNN, GAN, and DBN, applied to HSI. The meaning of abbreviations used in this table is: OA: Overall Accuracy, IP: Indian Pines, ACC: Accuracy, WCCA: Wheat Crop Class Acc.

<table><caption>**Table 8**</caption><thead><tr><th>Learning paradigm</th><th>Method</th><th>Article</th><th>Application</th><th>Results</th></tr></thead><tbody><tr><td rowspan="14">Supervised</td><td rowspan="4">CNN</td><td>Simón Sánchez et al. (2022)</td><td>Land use classification (LUC)</td><td>OA = 97%</td></tr><tr><td>Sadeghi-Tehran et al. (2021)</td><td>Classification</td><td>OA = 98.6%</td></tr><tr><td>Dhakal et al. (2023)</td><td>Fusarium Head Blight (FHB) disease classification</td><td>OA = 97%</td></tr><tr><td>Chen et al. (2016)</td><td>Classification</td><td>OA:IP = 87.8%, WCCA = 98.64%</td></tr><tr><td rowspan="5">RNN</td><td>Li et al. (2017)</td><td>Classification</td><td>OA:IP = 87.8%, WCCA = 99.82%</td></tr><tr><td>Yang et al. (2018)</td><td>Classification</td><td>OA:IP = 99.4%, WCCA = 100%</td></tr><tr><td>Song et al. (2018)</td><td>Classification</td><td>OA:IP = 98.5%, WCCA = 99.50%</td></tr><tr><td>Gong et al. (2019)</td><td>Classification</td><td>OA:IP = 98.9%, WCCA = 98%</td></tr><tr><td>Bing et al. (2019)</td><td>Classification</td><td>OA:IP = 93.1%</td></tr><tr><td rowspan="2">Transformer</td><td>Lee and Kwon (2017)</td><td>Classification</td><td>OA:IP = 93.6%</td></tr><tr><td>Roy et al. (2019)</td><td>Classification</td><td>AO:IP = 98.3%</td></tr><tr><td rowspan="5">Mamba</td><td>Mou et al. (2017a)</td><td>Classification</td><td>OA:IP = 88.6%, WCCA = 59.56%</td></tr><tr><td>Kussul et al. (2020)</td><td>Classification</td><td>OA: 97.5%</td></tr><tr><td>Ndikumana et al. (2018)</td><td>Classification</td><td>OA: 89.6%</td></tr><tr><td>Venkatesan and Prabu (2019)</td><td>Classification</td><td>Recall = 88%</td></tr><tr><td>Luo (2018)</td><td>Classification</td><td>OA:IP = 90.3%</td></tr><tr><td rowspan="5">SAE</td><td>Sigger et al. (2024)</td><td>Classification</td><td>OA:IP = 99.06%</td></tr><tr><td>Liu et al. (2024b)</td><td>Classification</td><td>OA:IP = 96.8%, WCCA = 98.64</td></tr><tr><td>Xie et al. (2023)</td><td>Classification</td><td>OA:IP = 98.4%, WCCA = 100%</td></tr><tr><td>He et al. (2024)</td><td>Classification</td><td>OA:IP = 96.47%</td></tr><tr><td>Sheng et al. (2024)</td><td>Classification</td><td>OA:IP = 99.23%, WCCA = 99.73%</td></tr><tr><td rowspan="4">DBN</td><td>Yang et al. (2024a)</td><td>Classification</td><td>OA:IP = 96.43%</td></tr><tr><td>Yang et al. (2024b)</td><td>Classification</td><td>OA:IP = 89.92%, WCCA = 99.65%</td></tr><tr><td>Zhou et al. (2024)</td><td>Classification</td><td>OA:IP = 92.07%</td></tr><tr><td>Liang et al. (2020)</td><td>Detection of DON levels in FHB wheat kernels and wheat flour</td><td>OA: Vis-NIR = 98.5%/91.5%, SWIR = 95.7%/97.1%</td></tr><tr><td rowspan="4">Unsupervised</td><td rowspan="3">SAE</td><td>Xing et al. (2016)</td><td>Classification</td><td>OA:IP = 92.06%</td></tr><tr><td>Li et al. (2019)</td><td>Classification</td><td>OA:IP = 96.2.6%, WCCA = 91.67%</td></tr><tr><td>Sellami and Farah (2019)</td><td>Classification</td><td>OA:IP = 94.9%, WCCA = 93.91%</td></tr><tr><td>Diffusion</td><td>Yang et al. (2019)</td><td>Classification</td><td>OA:IP = 97.9%</td></tr></tbody></table>

A summary of the results obtained from DL methods, including SAE and Transformer, applied to HSI. Abbreviations: OA: Overall Accuracy, IP: Indian Pines, SB: Santa Barbara, BA: Bay Area, He: Hermiston, JR: Jasper Ridge, SWIR: Short-Wave Infrared, VNIR: Very Near Infrared, ACC: Accuracy, WCCA: Wheat Crop Class Accuracy.

<table><thead><tr><th>Learning paradigm</th><th>Method</th><th>Article</th><th>Application</th><th>Results</th></tr></thead><tbody><tr><td rowspan="14">Semi-Supervised</td><td rowspan="4">Pseudo-Labeling</td><td>Zhao et al. (2024c)</td><td>Classification</td><td>OA:IP = 97.41%, WCCA = 100%</td></tr><tr><td>Wang et al. (2021a)</td><td>Classification</td><td>OA:IP = 98.20%, WCCA = 98.91%</td></tr><tr><td>Fang et al. (2018)</td><td>Classification</td><td>OA:IP = 97.66%, WCCA = 96.04%</td></tr><tr><td>Zhang (2020)</td><td>Classification</td><td>OA:IP = 95.14%</td></tr><tr><td rowspan="8">GAN</td><td>Zhan et al. (2017)</td><td>Classification</td><td>OA:IP = 83.5%</td></tr><tr><td>Zhu et al. (2018)</td><td>Classification</td><td>OA:IP = 89%, WCCA = 49.75%</td></tr><tr><td>Xue (2020)</td><td>Classification</td><td>OA:IP = 99.1%, WCCA = 98.69%</td></tr><tr><td>Gao et al. (2019)</td><td>Classification</td><td>OA:IP = 95.7%, WCCA = 100%</td></tr><tr><td>He et al. (2017)</td><td>Classification</td><td>OA:IP = 75.6%, WCCA = 99.41%</td></tr><tr><td>Li et al. (2022d)</td><td>Classification of wheat kernels</td><td>OA:IP = 96.6%</td></tr><tr><td>Wang et al. (2020)</td><td>Classification</td><td>OA:IP = 97.2%, WCCA = 97.08%</td></tr><tr><td>Benjdira et al. (2020)</td><td>Segmentation</td><td>OA1: 58.8%, OA2: 36.3%</td></tr><tr><td rowspan="4">Unsupervised</td><td rowspan="2">SAE</td><td>Windrim et al. (2019)</td><td>Classification</td><td>Clustering Result = 70%</td></tr><tr><td>Mughees and Tao (2016)</td><td>Classification</td><td>Acc:IP = 98.6%</td></tr><tr><td rowspan="2">Diffusion</td><td>Zhang et al. (2023b)</td><td>Classification</td><td>OA:SB = 96.87%, BA = 96.35%, He = 95.47%</td></tr><tr><td>Polk et al. (2023)</td><td>Classification</td><td>OA:IP = 44.5%</td></tr></tbody></table>
<caption>**Table 9**</caption>

An overview of the key research conducted on wheat crop classification using HSI. The meaning of abbreviations used in this table are: ACC: Accuracy.

<table><thead><tr><td>Year</td><td>Objective</td><td>Method</td><td>Results</td></tr></thead><tbody><tr><td>2019 (Bao et al., 2019)</td><td>Classification of wheat variety using HSI and chemometrics</td><td>PCA, LDA</td><td>Acc = 91.3%</td></tr><tr><td>2021 (Özkan et al., 2021)</td><td>DL system for wheat samples classification</td><td>AlexNet FC6, VGG16 FC7</td><td>AlexNet FC6: 96.00%, VGG16 FC7: 99.00%</td></tr><tr><td>2022 (Yipeng et al., 2022)</td><td>Classification of wheat kernels damaged by FHB using EWs from HSI images</td><td>ASSDN</td><td>SetT: 100%, SetP: 98.31%, AUC: 99.85%</td></tr><tr><td>2023 (Zhu et al., 2023)</td><td>Classification model for online detection of sound and sprouted wheat kernels</td><td>1D CNN, 2D CNN, 3D CNN</td><td>2D CNN = 96.02%, 3D CNN = 98.4%</td></tr><tr><td>2023 (Que et al., 2023)</td><td>Non-destructive method for wheat seed variety identification</td><td>CNN</td><td>Acc = 100%</td></tr></tbody></table>

for spectral data analysis. Their model demonstrated superior performance compared to traditional classifiers such as support vector machines (SVMs), underscoring the advantage of DL in hyperspectral wheat classification. Slavkovikj et al. (2015) proposed a feature learning approach using CNNs, where their model automatically extracted band-pass features from hyperspectral data. Applied to a widely used remote sensing hyperspectral dataset, their method showed strong classification performance across multiple crops, including wheat.

The integration of large-scale datasets has further propelled wheat classification research. David et al. (2020) utilized the Global Wheat Head Dataset (GWHD) to analyze wheat attributes, employing AlexNet with multi-feature fusion to improve classification accuracy. Their work contributed to wheat-breeding research and agricultural classification, demonstrating the applicability of DL to real-world large datasets.

Beyond HSI, Near-Infrared Spectroscopy (NIR) has also been explored for wheat classification. Caporaso et al. (2018) investigated the role of HSI and NIR in cereal grain assessment, showing that these technologies, when combined with DL models such as CNNs and MLPs, can achieve high classification accuracy. Their study highlighted the benefits of non-destructive, real-time quality assessment, which aids in fungal infection detection and grain composition analysis.

While significant progress has been made, challenges such as high computational demands, the need for large labeled datasets, and spectral variability remain areas for further research. Future advancements in model optimization and dataset augmentation techniques could enhance the scalability and accuracy of HSI-based wheat classification models.

## 6.2. Wheat crop disease detection and monitoring

The occurrence of diseases in wheat crops presents significant challenges for farmers, as untreated infections can lead to severe yield losses. Fast and non-destructive, HSI has demonstrated notable results in diagnosing plant diseases by capturing high-resolution spectral data across visible to near-infrared wavelengths (Wan et al., 2022). This technology provides comprehensive insights into the physiological and metabolic changes in plants, enabling early disease detection before symptoms become visible (Terentev et al., 2023).

Table 10 summarizes recent advancements in the detection of wheat disease based on DL, highlighting the potential of HSI in precision agriculture. These studies demonstrate how HSI technology, combined with DL models, enables early disease detection, classification, and monitoring, ultimately improving wheat crop management.

Feng et al. (2024) showed that HSI, by analyzing the interaction between light and plant tissues, can capture subtle spectral differences, effectively identifying biotic and abiotic stresses. This capability makes HSI a powerful tool for detecting wheat diseases in their early stages. Similarly, Jin et al. (2018) used deep neural networks to classify HSI pixels, improving disease detection accuracy in wheat fields.

The integration of DL algorithms further strengthens the diagnostic capabilities of HSI by training models to distinguish healthy and diseased wheat crops according to spectral signatures (Terentev et al.,

2023). Khotimah et al. (2023) evaluated the MCE-ST network, a DL-based model applied to cassava disease and wheat salt stress datasets, demonstrating its effectiveness in objectively assessing disease severity.

By combining HSI technology with DL models, researchers can develop robust disease monitoring frameworks that allow for timely intervention and optimized disease management strategies. This fusion of technologies enhances agricultural productivity and contributes to global food security.

## 6.3. Wheat crop nutrient estimation

Assessing nutrient levels in wheat crops is crucial for optimizing agricultural practices and ensuring healthy growth. Nutritional deficiencies can significantly impact crop yield and quality, posing challenges for farmers. HSI technology offers a promising approach for monitoring the nutritional status of wheat by analyzing distinct spectral signatures that provide insights into the quality and health of wheat grains. Recent advancements, particularly in DL-based methods, have further enhanced the accuracy of these assessments. Table 11 provides an overview of key studies that have explored DL-based nutritional estimation of wheat crops, showcasing the integration of HSI and computational models for improved nutrient analysis.

Recent research (Ma et al., 2022) has demonstrated the effectiveness of HSI in assessing wheat grain quality. By capturing and analyzing spectral data, critical details about the nutritional makeup and general health of wheat kernels can be obtained. The integration of HSI with DL algorithms has been extensively investigated, particularly concerning data gathered by drones (Barbedo, 2023). This combination allows for advanced analysis of HSI, enabling more precise estimations and forecasts regarding crop health.

Hu et al. (2021) proposed a quick and cost-effective method for determining the micronutrient content of wheat grains using HSI. They posited that HSI could serve as a useful tool for predicting micronutrient levels in wheat, leading to timely interventions for nutrient management. This research utilized a portable Near-Infrared (NIR) device for mixing powdered food samples and focused on estimating micronutrient content effectively.

Another paper by Song et al. (2021) aimed to identify the best color characteristics and models for detecting chlorophyll concentration in wheat. The research involved multiple steps, including target extraction, feature extraction, feature selection, and modeling. The proposed method for assessing chlorophyll content showed high agreement with conventional chlorophyll meters during trials in a wheat experimental field, demonstrating its reliability and effectiveness compared to other regression models.

In general, the use of digital image analysis in agricultural practices highlights the importance of evaluating crop nutrition. By leveraging HSI technology, farmers can make informed decisions regarding fertilization and nutrient management, ultimately leading to improved crop yields and sustainability.
<caption>**Table 10**</caption>

A summary of important studies on the use of HSI for wheat crop disease monitoring and detection. Abbreviations: M1: Combined data from Belgium and Lithuania, M2: Data from Lithuania only, ACC: Accuracy.

<table><thead><tr><td>Year</td><td>Objective</td><td>Method</td><td>Results</td></tr></thead><tbody><tr><td>2021 (Khan et al., 2021)</td><td>Early detection of powdery mildew disease in wheat using HSI</td><td>Partial least-squares regression (PLSR)</td><td>Acc = 82.35%, (R<sup>2</sup> = 0.748-0.722)</td></tr><tr><td>2022 (Almoujahed et al., 2022)</td><td>Detection of fusarium head blight (FHB) in wheat using a hyperspectral camera</td><td>Support Vector Machine (SVM), Artificial Neural Network (ANN), Logistic Regression (LR)</td><td>Acc = 95.6%</td></tr><tr><td>2022 (Rangarajan et al., 2022)</td><td>DL models for the detection of fusarium head blight (FHB) in wheat</td><td>CNN model, DarkNet 19</td><td>F1 score = 100%</td></tr><tr><td>2022 (Feng et al., 2022)</td><td>Hyperspectral monitoring of powdery mildew disease severity, improved monitoring accuracy of wheat powdery mildew</td><td>CARS algorithm, SPA algorithm</td><td>R<sup>2</sup> = 0.733-0.836</td></tr><tr><td>2022 (Yipeng et al., 2022)</td><td>HSI and DL for FHB-damaged wheat kernel classification. Architecture self-search deep network achieved high accuracy</td><td>KNN (K-Nearest Neighbors), SVM (Support Vector Machine), CNN, ASSDN (Architecture Self-Search Deep Network)</td><td>AUC= 0.9985</td></tr><tr><td>2023 (Xu et al., 2023)</td><td>Proposed DL algorithm for wheat leaf disease identification. Outperforms existing CNN models in recognition precision.</td><td>Lightweight Detection</td><td>Acc = 99.95%</td></tr><tr><td>2023 (Sereda et al., 2023)</td><td>Develop remote methods for diagnosing the state of wheat crops and monitoring diseases using spectral equipment and original monitoring tools.</td><td>satellite and UAV</td><td>Reflectance Values, Correlation with Disease, Effectiveness of Remote Sensing</td></tr><tr><td>2023 (Blasch et al., 2023)</td><td>The paper evaluates the use of very high-resolution satellite (VHRS) and UAV imagery for the early detection and phenotyping of yellow rust (YR) and stem rust (SR) in wheat crops in Ethiopia. It focuses on how different wheat varieties respond to disease stress.</td><td>VHRS imagery, NDVI, RVI, Disease Index (DI).</td><td>Acc = 85.2%</td></tr><tr><td>2024 (Feng et al., 2024)</td><td>Provide a comprehensive review of advanced automatic non-destructive detection techniques for Fusarium head blight (FHB) in wheat.</td><td>Lightweight Detection model</td><td>Acc = 94.3%</td></tr><tr><td>2024 (Chang et al., 2024)</td><td>The objective of the research is to achieve higher classification accuracy in detecting wheat rust diseases using DL models.</td><td>DenseNet121 and Imp-DenseNet.</td><td>Acc = 98.32%</td></tr><tr><td>2025 (Almoujahed et al., 2025)</td><td>The objective of the research is to detect and map Fusarium head blight (FHB) in winter wheat fields using HSI.</td><td>1DCNN and Extra Trees Regression (ETR)</td><td>R<sup>2</sup>: 0.68 (M1) and 0.79 (M2)</td></tr></tbody></table>

## 6.4. Wheat crop yield estimation

Accurately estimating wheat crop yield is essential for effective agricultural planning and management. Reliable yield predictions enable farmers to allocate resources efficiently, optimize their practices, and improve overall productivity. Recent advancements in HSI and DL have significantly enhanced yield estimation accuracy. By integrating hyperspectral data with other remote sensing sources, researchers have developed more precise predictive models. Table 12 summarizes key studies focusing on DL-based wheat crop yield estimation, illustrating various methodologies and technological advancements in this area.

To predict agricultural yield at a spatial resolution of 5 meters by 5 m, Cheng et al. (2022) combined gridded yield data with DL algorithms, utilizing Sentinel-2 multispectral and ZY-1 02D hyperspectral data. This integration enabled precise yield predictions, offering valuable insights into expected crop output. Similarly, Moghimi et al. (2020) emphasized that UAVs equipped with hyperspectral cameras have facilitated rapid and cost-effective yield estimation. This UAV-based approach allows high-resolution aerial imagery to be captured, significantly benefiting winter wheat breeding programs (Liu et al., 2023).

Researchers have employed low-altitude UAV-based hyperspectral data collection at various points in the winter wheat crop canopy to

refine yield predictions (Li et al., 2022a). Additionally, the integration of open-source computational tools such as R and Python, combined with automated hyperspectral narrowband vegetation index analysis, has demonstrated promising results in crop yield estimation (Li et al., 2022c). This method harnesses the power of DL algorithms to process HSI data, extracting meaningful insights for yield forecasting.

By leveraging HSI and DL-driven techniques, farmers can obtain more accurate and timely yield predictions, leading to improved decision-making, better resource management, and higher crop productivity.

## 7. Discussion

The integration of DL with HSI has significantly advanced wheat crop analysis, enabling accurate classification, disease detection, nutrient estimation, and yield prediction. While HSI surpasses traditional crop monitoring methods in precision and automation, classical detection techniques remain relevant due to their reliability, particularly in cases where HSI's high cost and computational demands pose challenges. The inaccuracies in manual analysis, variations in instrumentation, and limitations of chemical-based detection methods have accelerated the transition toward data-driven approaches, Where DL plays
<caption>**Table 11**</caption>

A summary of important studies on the use of HSI in wheat crop nutrient estimation. Abbreviations: ML: Linear Model, OPM: 2nd Order Polynomial Model, EM: Exponential Model, LAI: Leaf Area Index, LCC: Leaf Chlorophyll Content.

<table><caption>**Table 12**</caption><thead><tr><td>Year</td><td>Objective</td><td>Method</td><td>Results</td></tr></thead><tbody><tr><td>2021 (Song et al., 2021)</td><td>Feature extraction and model building for crop nutrition measurement. Proposed SBRR approach for estimating wheat chlorophyll content</td><td>Stepwise-based ridge regression (SBRR)</td><td>Optimal SBRR model: R<sup>2</sup> = 0.718, RMSE = 5.111; Verification results: R<sup>2</sup> = 0.794, RMSE = 4.304</td></tr><tr><td>2021 (Yang et al., 2021)</td><td>Estimation of leaf nitrogen content in wheat</td><td>PLS, GBDT, SVR</td><td>R<sup>2</sup> = 0.975 for calibration set, R<sup>2</sup> = 0.861 for validation set</td></tr><tr><td>2021 (Astaoui et al., 2021)</td><td>Monitoring wheat crop using UAV multispectral imagery. Estimating wheat yield and biophysical parameters. Mapping wheat dry biomass and nitrogen uptake</td><td>Random Forest technique and linear regression model</td><td>LM: R<sup>2</sup> = 0.761, RMSE = 0.63; OPM: R<sup>2</sup> = 0.741, RMSE = 0.67; EM: R<sup>2</sup> = 0.690, RMSE = 0.67</td></tr><tr><td>2022 (Lu et al., 2022)</td><td>UAV-based multi-view images improve the estimation of nitrogen nutrition</td><td>Support Vector Regression (SVR), Extreme Learning Machine (ELM), Random Forest (RF)</td><td>LNC: R<sup>2</sup> = 0.61, RMSE = 0.37%; PNC: R<sup>2</sup> = 0.52, RMSE = 0.24%; LNA: R<sup>2</sup> = 0.72, RMSE = 1.45 g/m<sup>2</sup>; PNA: R<sup>2</sup> = 0.62, RMSE = 3.02 g/m<sup>2</sup></td></tr><tr><td>2023 (Du et al., 2023)</td><td>Develop a universal method for accurate estimation and diagnosis of leaf chlorophyll content (LCC) and leaf area index (LAI), and diagnose nitrogen levels in crops</td><td>Bi-objective constrained deep neural network (DNNCA model)</td><td>LAI: R<sup>2</sup> = 0.64-0.82, RMSE = 0.58-1.02 m<sup>2</sup>/m<sup>2</sup>; LCC: R<sup>2</sup> = 0.56-0.82, RMSE = 3.9-10.5 ug/cm<sup>2</sup></td></tr></tbody></table>

A summary of the important studies on HSI's use for crop yield estimate of wheat crops.

<table><thead><tr><td>Year</td><td>Objective</td><td>Method</td><td>Results</td></tr></thead><tbody><tr><td>2020 (Moghimi et al., 2020)</td><td>Automated framework for selecting advanced crop varieties. Hyperspectral camera mounted on UAV</td><td>Deep neural networks (DNN model).</td><td>Predicting yield: 0.79 (sub-plot scale), 0.41 (plot scale)</td></tr><tr><td>2022 (Cheng et al., 2022)</td><td>Used machine learning to estimate wheat yields</td><td>LSTM (Long Short-Term Memory), RF (Random Forest), GBDT (Gradient Boosting Decision Tree), SVR (Support Vector Regression).</td><td>LSTM model (R<sup>2</sup> = 0.93), 10-m Sentinel-2 data (R<sup>2</sup> = 0.91)</td></tr><tr><td>2022 (Li et al., 2022c)</td><td>Automated machine learning for HSI analysis. Estimation of crop yield and biomass using AI.</td><td>AutoML framework for regression operations. Hyperspectral narrow-band vegetation indexes for modeling</td><td>R<sup>2</sup> and NRMSE values for yield estimation: Wheat: (R<sup>2</sup> = 0.96, NRMSE = 0.12). R<sup>2</sup> and NRMSE values for straw mass estimation: Wheat: (R<sup>2</sup> = 0.96, NRMSE = 0.12)</td></tr><tr><td>2022 (Li et al., 2022a)</td><td>UAV-based hyperspectral and ensemble machine learning for predicting winter wheat yield. Spectral indices and machine learning models used for prediction.</td><td>Recursive feature elimination (RFE), Boruta feature selection, Pearson correlation coefficient (PCC)</td><td>The SVM yield prediction model performed the best (R<sup>2</sup> = 0.73). (R<sup>2</sup> = 0.78) for the yield prediction model based on Boruta's</td></tr><tr><td>2022 (Fei et al., 2022)</td><td>Investigates the use of a multi-layer neural network and hyperspectral reflectance in a genome-wide association research for grain yield in bread wheat</td><td>Multi-layer neural network regression.</td><td>Multi-layer neural network model at the MGF stage: (R<sup>2</sup> = 0.76), EGF stage: (R<sup>2</sup> = 0.69), LGF stage: (R<sup>2</sup> = 0.65), Flowering stage: (R<sup>2</sup> = 0.68). Linear regression: (R<sup>2</sup> = 0.87-0.94)</td></tr><tr><td>2023 (Liu et al., 2023)</td><td>This research focuses on estimating winter wheat yield using UAV-based imagery. Multiple regression model combining growth stages for accurate estimation.</td><td>Simple linear regression model based on single growth stages; Multiple linear regression model combining multiple growth stages</td><td>(r = 0.84; RMSE = 0.69 t/ha)</td></tr><tr><td>2023 (Yang et al., 2023)</td><td>Evaluate the chlorophyll content of wheat under drought stress conditions using HSI,</td><td>Random Forest Regression (RFR)</td><td>(R<sup>2</sup> = 0.61; RMSE = 4.439 t/ha; RE = 7.35%)</td></tr><tr><td>2024 (Zhang et al., 2024)</td><td>Improve the accuracy and transferability of wheat yield estimation models by integrating various sensor data</td><td>LSTM with multi-head self-attention (LSTM-MH-SA) and joint Distribution Adaptation (JDA).</td><td>LSTM-MH-SA: (R<sup>2</sup> = 0.2958); JDA: (R<sup>2</sup> = 0.81)</td></tr><tr><td>2024 (Zhao et al., 2024b)</td><td>Enhance the accuracy and robustness of in-situ leaf chlorophyll content (LCC) estimation</td><td>3DCNN-LSTM</td><td>(RP<sup>2</sup> = 0.96)</td></tr><tr><td>2024 (Liu et al., 2024a)</td><td>Develop a more accurate wheat aboveground biomass (AGB) estimation model</td><td>Phenological information and vegetation indices (VIs) with the Lasso model (FIWheat-AGB)</td><td>FIWheat-AGB: (R<sup>2</sup> = 0.91, RMSE = 2.11 t/ha, MAE = 1 t/ha)</td></tr></tbody></table>
a crucial role in extracting meaningful insights from high-dimensional spectral data. Consequently, various learning paradigms have been explored for wheat crop analysis, each suited to specific scenarios and challenges.

Supervised learning is the most widely investigated approach in wheat crop analysis due to its ability to learn from labeled datasets for precise predictions. CNNs have been the most commonly adopted models, effectively capturing spatial features in both 2D and 3D forms. However, their limited ability to model long-range spectral dependencies has led to the adoption of more advanced architectures. The introduction of transformers has revolutionized hyperspectral classification through the self-attention mechanism. More recently, Mamba models have emerged as a promising alternative, achieving state-of-the-art performance while maintaining computational efficiency.

Semi-supervised learning, on the other hand, has been instrumental in addressing the scarcity of labeled hyperspectral datasets. Pseudo-labeling techniques enable models to refine predictions on unlabeled data, while GANs have been particularly useful for synthetic data generation to balance dataset distributions. However, while pseudo-labeling and generative models offer promising directions, their effectiveness depends heavily on model design, rather than simply expanding dataset size.

In addition to supervised and semi-supervised learning, unsupervised learning has gained traction, particularly for hyperspectral data compression, feature extraction, and spectral unmixing. Stacked autoencoders and deep belief networks have been valuable for dimensionality reduction, improving model interpretability and computational efficiency. However, their application in wheat crop analysis remains limited due to challenges in defining meaningful feature representations without labeled data. More recently, diffusion models have shown promise in denoising and classification tasks, offering potential for further research.

From an application perspective, DL has demonstrated significant advancements across various tasks in wheat crop analysis. In wheat classification, CNNs, RNNs, DBNs, and SAEs have been widely used. More recently, transformer and Mamba models have been adopted, defining state-of-the-art performance. In disease detection, CNNs remain the most widely used approach, though GANs have enhanced performance by generating synthetic hyperspectral samples. For nutrient estimation, CNNs have been applied to analyze spectral reflectance data, but future research should explore recurrent models (e.g., ILSTMs) for tracking nutrient variations over time. Similarly, In yield estimation, LSTMs have shown promise in modeling sequential crop growth patterns, while CNN-based regression models have been effective for biomass and grain yield prediction.

## 8. Challenges and future directions

Despite the highlighted advancements in the previous sections, several challenges require further attention to fully harness DL for the hyperspectral analysis of wheat crops. One of the most pressing issues is the complexity and cost of HSI data acquisition. The high-dimensional nature of hyperspectral data requires substantial computational power, making real-time agricultural monitoring difficult. Although DL models such as CNNs, transformers, and Mamba have achieved impressive results, their computational demands remain a major barrier to widespread adoption. Future research should focus on lightweight architectures optimized for UAVs, edge computing, and mobile sensors to make real-time HSI analysis more accessible to farmers and agronomists. In addition, overcoming the high acquisition cost of HSI systems requires efforts in designing low-cost, miniaturized hyperspectral sensors; selecting optimized spectral bands to reduce data dimensionality without sacrificing critical information; and applying data augmentation strategies to synthetically expand datasets.

Domain-Adaptation AI. In addition to spectral-band selection and data augmentation, domain-adaptation techniques offer a promising

avenue for addressing the limited availability of labeled hyperspectral data. These methods aim to transfer knowledge from a well-annotated source domain to a related but underrepresented target domain by aligning spectral-spatial feature representations across domains. Recent advances have demonstrated that strategies such as adversarial feature alignment can enable models to generalize effectively across different acquisition settings or seasonal conditions with minimal supervision from the target domain Yang et al. (2025). By substantially reducing the need for manual annotation, domain adaptation enhances the scalability and practicality of deep learning-based hyperspectral systems. As such, it represents a compelling direction for developing robust and transferable models in wheat crop analysis. To better assess the practical readiness of various DL models for real-world deployment, Table 13 summarizes their computational demands, real-time feasibility, and performance in wheat-related tasks.

The high computational demands of DL models, particularly Transformers and GANs, make them difficult to deploy on standard agricultural hardware, often requiring high-end GPUs or cloud computing, which may not be accessible to all farmers. To improve practical adoption, future work should prioritize hardware-efficient models, cost-effective edge computing solutions, and scalable frameworks that can run on lower-power devices such as UAVs and portable field sensors.

Another key limitation is the lower spatial resolution of HSI compared to multispectral or RGB imaging, which makes it difficult to capture fine structural details in crops. Integrating super-resolution techniques and spectral-spatial fusion models can improve interpretability and enhance performance in applications such as disease detection and nutrient monitoring.

Additionally, the lack of publicly available, large, and diverse hyperspectral datasets significantly limits DL research in certain wheat crop analysis tasks. While classification has received more attention, datasets for disease detection, nutrient estimation, and yield prediction remain scarce or are often proprietary, restricting access to researchers. This shortage prevents the full exploration of recent DL advancements, such as transformers, Mamba models, and generative models, in these areas. Without access to sufficient labeled data, models struggle to generalize across diverse wheat-growing conditions, limiting their practical applicability.

Beyond these challenges, several promising research directions can enhance DL-based hyperspectral wheat crop analysis, including meta-learning and automatic hyperparameter and architecture optimization. Meta-learning enables models to generalize across datasets and remains underexplored in HSI despite its potential to improve adaptability in different agricultural environments. Automatic hyperparameters and architecture optimization techniques, such as genetic algorithms and evolutionary computing, can further enhance model performance.

To address computational bottlenecks in HSI analysis and reduce the dependence on manual architecture design and hyperparameter tuning, integrating Neural Architecture Search (NAS) presents a promising direction. NAS can provide models that balance complexity and performance by automatically optimizing architectures for specific deployment scenarios. For example, NAS could tailor 3D CNNs for UAV-based edge devices by pruning redundant spectral bands and reducing model parameters while preserving predictive accuracy. Additionally, incorporating dynamic token sparsity mechanisms in Transformer-based models, such as discarding less informative spectral bands during inference, could further streamline processing for real-time applications.

On the other hand, Self-Supervised Pretraining (SSP) has emerged as an effective strategy for improving model performance and addressing data scarcity challenges. By leveraging contrastive learning techniques on unlabeled HSI data, SSP enables models to learn robust spectral-spatial representations without requiring extensive annotated datasets. Furthermore, SSP can help mitigate regional biases by extracting domain-invariant features, enhancing model generalizability across different environmental and agricultural conditions. Combining SSP with multi-task learning approaches, such as jointly predicting disease
<table><caption>**Table 13**
Assessment of practical deployment aspects for DL models in wheat HSI.</caption><thead><tr><th>Method</th><th>Computational Cost</th><th>Real-Time Feasibility</th><th>Performance</th></tr></thead><tbody><tr><td>CNN</td><td>•</td><td>•</td><td>•</td></tr><tr><td>RNN</td><td>•</td><td>•</td><td>•</td></tr><tr><td>Transformer</td><td>•</td><td>•</td><td>•</td></tr><tr><td>Mamba</td><td>•</td><td>•</td><td>•</td></tr></tbody></table>

* Efficient/Feasible • Moderate • Challenging

<table><caption>**Table 14**
List of abbreviations used in this survey.</caption><thead><tr><th>Abbreviation</th><th>Full term</th></tr></thead><tbody><tr><td>HSI</td><td>Hyperspectral Imaging</td></tr><tr><td>DL</td><td>Deep Learning</td></tr><tr><td>OA</td><td>Overall Accuracy</td></tr><tr><td>ACC</td><td>Accuracy</td></tr><tr><td>AUC</td><td>Area Under the Curve</td></tr><tr><td>R<sup>2</sup></td><td>Coefficient of Determination</td></tr><tr><td>DI</td><td>Disease Index</td></tr><tr><td>PC</td><td>Period Covered</td></tr><tr><td>ME</td><td>Model Evaluation</td></tr><tr><td>WCCA</td><td>Wheat Crop Classification Accuracy</td></tr><tr><td>NDVI</td><td>Normalized Difference Vegetation Index</td></tr><tr><td>RVI</td><td>Ratio Vegetation Index</td></tr><tr><td>GSD</td><td>Ground Sampling Distance</td></tr><tr><td>RMSE</td><td>Root Mean Square Error</td></tr><tr><td>MAE</td><td>Mean Absolute Error</td></tr><tr><td>LCC</td><td>Leaf Chlorophyll Content</td></tr><tr><td>LAI</td><td>Leaf Area Index</td></tr><tr><td>AGB</td><td>Aboveground Biomass</td></tr><tr><td>FHB</td><td>Fusarium Head Blight</td></tr><tr><td>DON</td><td>Deoxynivalenol</td></tr><tr><td>VNIR</td><td>Very Near Infrared</td></tr><tr><td>SWIR</td><td>Short-Wave Infrared</td></tr><tr><td>VIS-NIR</td><td>Visible to Near-Infrared</td></tr><tr><td>NIR</td><td>Near-Infrared</td></tr><tr><td>UAV</td><td>Unmanned Aerial Vehicle</td></tr><tr><td>VHRS</td><td>Very High Resolution Satellite</td></tr><tr><td>AVIRIS</td><td>Airborne Visible Infrared Imaging Spectrometer</td></tr><tr><td>SSL</td><td>Semi-Supervised Learning</td></tr><tr><td>PLSR</td><td>Partial Least Squares Regression</td></tr><tr><td>PCA</td><td>Principal Component Analysis</td></tr><tr><td>LDA</td><td>Linear Discriminant Analysis</td></tr><tr><td>t-SNE</td><td>t-Distributed Stochastic Neighbor Embedding</td></tr><tr><td>LLE</td><td>Locally Linear Embedding</td></tr><tr><td>NAS</td><td>Neural Architecture Search</td></tr><tr><td>SSP</td><td>Self-Supervised Pretraining</td></tr><tr><td>CNN</td><td>Convolutional Neural Network</td></tr><tr><td>RNN</td><td>Recurrent Neural Network</td></tr><tr><td>LSTM</td><td>Long Short-Term Memory</td></tr><tr><td>GRU</td><td>Gated Recurrent Unit</td></tr><tr><td>SSRN</td><td>Spectral-Spatial Residual Network</td></tr><tr><td>GAN</td><td>Generative Adversarial Network</td></tr><tr><td>DBN</td><td>Deep Belief Network</td></tr><tr><td>RBM</td><td>Restricted Boltzmann Machine</td></tr><tr><td>SAE</td><td>Stacked Autoencoder</td></tr><tr><td>SDAE</td><td>Stacked Denoising Autoencoder</td></tr><tr><td>ASSDN</td><td>Architecture Self-Search Deep Network</td></tr><tr><td>CCGAN</td><td>Cascade Conditional GAN</td></tr><tr><td>DCGAN</td><td>Deep Convolutional GAN</td></tr><tr><td>DBF-GAN</td><td>Dual Branch Fusion GAN</td></tr></tbody></table>

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severity and nutrient status, offers an additional avenue for maximizing the utility of limited labeled samples while capturing interrelated agronomic traits.

Another important research direction is to identify and reduce redundancy in hyperspectral data cubes. The presence of redundant spectral bands introduces noise and inefficiencies, affecting classification accuracy. Developing automatic feature selection protocols can improve model efficiency, while designing compact and cost-effective HSI sets will accelerate practical adoption in agriculture.

Advancements in self-supervised learning and contrastive learning hold promise for reducing reliance on expensive labeled datasets by enabling models to learn from unlabeled data. The fusion of CNNs and transformers has already yielded promising results in hyperspectral classification, and as research progresses, their applications in agriculture will continue to expand. Additionally, Mamba models' ability to process large hyperspectral datasets efficiently will play a crucial role in making HSI analysis more scalable and feasible for real-world agricultural applications.
<table><caption>**Table 14 (continued).**</caption><tr><td>DFFN</td><td>Deep Feature Fusion Network</td></tr><tr><td>SSL-ResNet</td><td>Semi-Supervised Learning ResNet</td></tr><tr><td>Bi-CLSTM</td><td>Bidirectional Long Short-Term Memory</td></tr><tr><td>CESA-MCFormer</td><td>Cross-Enhanced Self-Attention Multi-Channel Transformer</td></tr><tr><td>HyperSFormer</td><td>Hyperspectral Spatial-Spectral Transformer</td></tr><tr><td>DiffSpectralNet</td><td>Diffusion Spectral Network</td></tr><tr><td>DBFFT</td><td>Dual-Branch FFT Transformer</td></tr><tr><td>HSIMamba</td><td>Hyperspectral Imaging Mamba</td></tr><tr><td>3DSS-Mamba</td><td>3D Spatial-Spectral Mamba</td></tr><tr><td>GraphMamba</td><td>Graph-based Mamba Model</td></tr><tr><td>Basic Mamba</td><td>Base version of Mamba Architecture</td></tr><tr><td>SSM</td><td>State Space Model</td></tr><tr><td>IP</td><td>Indian Pines</td></tr><tr><td>SA</td><td>Salinas</td></tr><tr><td>PU</td><td>Pavia University</td></tr><tr><td>SB</td><td>Santa Barbara</td></tr><tr><td>BA</td><td>Bay Area</td></tr><tr><td>He</td><td>Hermiston</td></tr><tr><td>JR</td><td>Jasper Ridge</td></tr><tr><td>WHS</td><td>Wheat Hyperspectral Dataset</td></tr><tr><td>CRD</td><td>Crown Rot Detection</td></tr><tr><td>DRUM</td><td>Dataset for wheat stress phenotyping</td></tr><tr><td>GHISA</td><td>Global Hyperspectral Imaging Spectral-Library of Agricultural Crops</td></tr><tr><td>DVIC</td><td>Deep Variational Information Clustering</td></tr><tr><td>CARS</td><td>Competitive Adaptive Reweighted Sampling</td></tr><tr><td>SPA</td><td>Successive Projections Algorithm</td></tr><tr><td>ETR</td><td>Extra Trees Regression</td></tr><tr><td>KNN</td><td>k-Nearest Neighbors</td></tr><tr><td>SVM</td><td>Support Vector Machine</td></tr><tr><td>ANN</td><td>Artificial Neural Network</td></tr><tr><td>LR</td><td>Logistic Regression</td></tr><tr><td>BCG-Net</td><td>Boundary-Constrained GAN Network</td></tr><tr><td>DSFA</td><td>Deep Slow Feature Analysis</td></tr><tr><td>HyperNet</td><td>Hyperspectral Self-Supervised Network</td></tr></table>

Deep learning has revolutionized hyperspectral wheat crop analysis, significantly improving the accuracy of classification, disease detection, nutrient monitoring, and yield prediction. However, challenges related to computational demands, dataset availability, and model interpretability need to be addressed to ensure real-world scalability. Future research should focus on developing lightweight, energy-efficient models, expanding labeled datasets, integrating Explainable AI techniques, and exploring hybrid learning approaches. By overcoming these limitations, AI-driven hyperspectral analysis will continue to evolve, offering transformative solutions for precision agriculture and sustainable wheat production.

## 9. Conclusion

This survey explores the transformative role of DL in hyperspectral wheat crop analysis, highlighting its potential to enhance precision agriculture through non-destructive, data-driven insights. We categorize existing DL approaches into supervised, semi-supervised, and unsupervised learning, providing a comprehensive taxonomy that offers valuable guidance for both researchers and practitioners. By analyzing and comparing state-of-the-art methods, we highlight their effectiveness in classification, disease detection, nutrient estimation, and yield prediction, while also identifying opportunities for further advancements in this domain.

Despite the advancements in DL for wheat crop analysis using HSI, several challenges hinder the broader adoption of DL for hyperspectral wheat crop analysis. Limited public datasets remain a major bottleneck, restricting the generalization of DL models across diverse agricultural conditions. Many critical tasks, including disease detection, nutrient estimation, and yield prediction, suffer from a lack of openly available labeled hyperspectral datasets, which in turn limits the exploration and application of advanced DL models, such as transformers, Mamba models, and generative models. Additionally, the high computational demands of these models pose challenges for real-time deployment,

particularly in resource-constrained agricultural settings. The reliance on specialized hardware, such as UAV-based and edge-computing solutions, further complicates large-scale adoption. Overcoming these challenges is crucial for the seamless integration of DL-powered HSI systems into precision agriculture.

To address these limitations, future research should focus on developing computationally efficient architectures, such as Mamba models, to accelerate hyperspectral data processing. Additionally, self-supervised learning, transfer learning, and meta-learning offer promising solutions to reduce dependency on large labeled datasets while enhancing model adaptability. By fostering dataset expansion, model optimization, and interdisciplinary collaboration, AI-driven HSI will continue to drive innovation in wheat crop analysis, contributing to global food security and sustainable farming (see Table 14).

## CRediT authorship contribution statement

**Fadi Abdeladhim Zidi:** Writing – original draft, Data curation.
**Abdelkrim Ouafi:** Supervision, Investigation. **Fares Bougourzi:** Resources, Conceptualization.
**Cosimo Distante:** Supervision.
**Abdelmakil Taleb-Ahmed:** Writing – review & editing, Validation.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data availability

No data was used for the research described in the article.
## References

Acevedo, M., Zurn, J.D., Molero, G., Singh, P., He, X., Aoun, M., Juliana, P., Bockleman, H., Bonman, M., El-Sohl, M., et al., 2018. The role of wheat in global food security. In: Agricultural Development and Sustainable Intensification. Routledge, pp. 81–110.

Adão, T., Hruška, J., Pádua, L., Bessa, J., Peres, E., Morais, R., Sousa, J.J., 2017. Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote. Sens. 9 (11), pp.1110.

Afrin, A., Haque, M.R., Al Mamun, M., 2024. Enhancing hyperspectral image compression through stacked autoencoder approach. In: 2024 6th International Conference on Electrical Engineering and Information & Communication Technology. ICEEICT, IEEE, pp. 1372–1377.

Agrawal, J., Arafat, M.Y., 2024. Transforming farming: A review of AI-powered UAV technologies in precision agriculture. Drones (2504-446X) 8 (11).

Aha, D., 2023. Wheat kernels. https://archive.ics.uci.edu/datasets?search=wheat+.

Almoujahed, M.B., Apolo-Apolo, O.E., Whetton, R.L., Kazlauskas, M., Kriaučiūnienė, Z., Šarauskis, E., Mouazen, A.M., 2025. Field-based hyperspectral imaging for detection and spatial mapping of fusarium head blight in wheat. Eur. J. Agron. 164, pp.127485.

Almoujahed, M.B., Rangarajan, A.K., Whetton, R.L., Vincke, D., Eylenbosch, D., Vermeulen, P., Mouazen, A.M., 2022. Detection of fusarium head blight in wheat under field conditions using a hyperspectral camera and machine learning. Comput. Electron. Agric. 203, pp.107456.

Ashfaq, M., Khan, I., 2023. Crop yield prediction of wheat using CNN and remote sensing multispectral imagery, a hybrid approach.

Astaoui, G., Dadaiss, J.E., Sebari, I., Benmansour, S., Mohamed, E., 2021. Mapping wheat dry matter and nitrogen content dynamics and estimation of wheat yield using UAV multispectral imagery machine learning and a variety-based approach: Case study of Morocco. AgriEngineering 3 (1), 29–49.

Bai, Y., Sun, X., Ji, Y., Fu, W., Zhang, J., 2024. Two-stage multi-dimensional convolutional stacked autoencoder network model for hyperspectral images classification. Multimedia Tools Appl. 83 (8), 23489–23508.

Bao, Y., Mi, C., Wu, N., Liu, F., He, Y., 2019. Rapid classification of wheat grain varieties using hyperspectral imaging and chemometrics. Appl. Sci. 9 (19), pp.4119.

Barbedo, J.G.A., 2023. A review on the combination of deep learning techniques with proximal hyperspectral images in agriculture. Comput. Electron. Agric. 210, pp.107920.

Barbedo, J.G., Tibola, C.S., Fernandes, J.M., 2015. Detecting Fusarium head blight in wheat kernels using hyperspectral imaging. Biosyst. Eng. 131, 65–76.

Bauriegel, E., Giebel, A., Geyer, M., Schmidt, U., Herppich, W., 2011. Early detection of fusarium infection in wheat using hyper-spectral imaging. Comput. Electron. Agric. 75 (2), 304–312.

Benjdira, B., Ammar, A., Koubaa, A., Ouni, K., 2020. Data-efficient domain adaptation for semantic segmentation of aerial imagery using generative adversarial networks. Appl. Sci. 10 (3), pp.1092.

Bing, L., Xuchu, Y., Pengqiang, Z., Xiong, T., 2019. Deep 3D convolutional network combined with spatial-spectral features for hyperspectral image classification. Acta Geod. et Cartogr. Sin. 48 (1), pp.53.

Blasch, G., Anberbir, T., Negash, T., Tilahun, L., Belayineh, F.Y., Alemayehu, Y., Mamo, G., Hodson, D.P., Rodrigues, Jr., F.A., 2023. The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in ethiopia. Sci. Rep. 13 (1), pp.16768.

Bohnenkamp, D., Behmann, J., Mahlein, A.-K., 2019. In-field detection of yellow rust in wheat on the ground canopy and UAV scale. Remote. Sens. 11 (21), pp.2495.

Caballero, D., Calvini, R., Amigo, J.M., 2019. Hyperspectral imaging in crop fields: precision agriculture. In: Data Handling in Science and Technology, vol. 32, Elsevier, pp. 453–473.

Cao, C., Song, W., Xiang, H., Yi, H., Xiao, F., Gao, X., 2024. A two-stream stacked autoencoder with inter-class separability for bilinear hyperspectral unmixing. IEEE Trans. Comput. Imaging.

Caporaso, N., Whitworth, M.B., Fisk, I.D., 2018. Near-infrared spectroscopy and hyperspectral imaging for non-destructive quality assessment of cereal grains. Appl. Spectrosc. Rev. 53 (8), 667–687.

Chang, S., Yang, G., Cheng, J., Feng, Z., Fan, Z., Ma, X., Li, Y., Yang, X., Zhao, C., 2024. Recognition of wheat rusts in a field environment based on improved DenseNet. Biosyst. Eng. 238, 10–21.

Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P., 2016. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 54 (10), 6232–6251.

Chen, F., Li, J., Yang, D., 2019. Hyperspectral image classification based on generative adversarial networks. Comput. Eng Appl 55 (22), 172–179.

Chen, R., Yao, H., Chen, W., Sun, H., Xie, W., Dong, L., Lu, X., 2024. Prototype-based pseudo-label refinement for semi-supervised hyperspectral image classification. IEEE Geosci. Remote. Sens. Lett.

Cheng, E., Zhang, B., Peng, D., Zhong, L., Yu, L., Liu, Y., Xiao, C., Li, C., Li, X., Chen, Y., et al., 2022. Wheat yield estimation using Remote Sensing data based on machine learning approaches. Front. Plant Sci. 13, pp.1090970.

Chung, J., Gulcehre, C., Cho, K., Bengio, Y., 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.

Dang, L., Weng, L., Hou, Y., Zuo, X., Liu, Y., 2023. Double-branch feature fusion transformer for hyperspectral image classification. Sci. Rep. 13 (1), pp.272.

David, E., Madec, S., Sadeghi-Tehran, P., Aasen, H., Zheng, B., Liu, S., Kirchgessner, N., Ishikawa, G., Nagasawa, K., Badhon, M.A., et al., 2020. Global wheat head detection (GWHD) dataset: a large and diverse dataset of high-resolution RGB-labelling images to develop and benchmark wheat head detection methods. Plant Phenomics.

Deng, L., Zhou, B., Ying, J., Zhao, R., 2023. A noise estimation method for hyperspectral image based on stacked autoencoder. IEEE Access.

Dhakal, K., 2021. WheatHyperSpectral. https://github.com/LiLabAtVT/WheatHyperSpectral.

Dhakal, K., Sivaramakrishnan, U., Zhang, X., Belay, K., Oakes, J., Wei, X., Li, S., 2023. Machine learning analysis of hyperspectral images of damaged wheat kernels. Sensors 23 (7), pp.3523.

Dreier, E.S., Sorensen, K.M., Lund-Hansen, T., Jespersen, B.M., Pedersen, K.S., 2022. Hyperspectral imaging for classification of bulk grain samples with deep convolutional neural networks. J. Near Infrared Spectrosc. 30 (3), 107–121.

Du, R., Chen, J., Xiang, Y., Zhang, Z., Yang, N., Yang, X., Tang, Z., Wang, H., Wang, X., Shi, H., et al., 2023. Incremental learning for crop growth parameters estimation and nitrogen diagnosis from hyperspectral data. Comput. Electron. Agric. 215, pp.108356.

Du, B., Ru, L., Wu, C., Zhang, L., 2019. Unsupervised deep slow feature analysis for change detection in multi-temporal remote sensing images. IEEE Trans. Geosci. Remote Sens. 57 (12), 9976–9992.

Fang, B., Li, Y., Zhang, H., Chan, J.C.-W., 2018. Semi-supervised deep learning classification for hyperspectral image based on dual-strategy sample selection. Remote. Sens. 10 (4), pp.574.

Fang, L., Liu, Z., Song, W., 2019. Deep hashing neural networks for hyperspectral image feature extraction. IEEE Geosci. Remote. Sens. Lett. 16 (9), 1412–1416.

Fei, S., Hassan, M.A., Xiao, Y., Rasheed, A., Xia, X., Ma, Y., Fu, L., Chen, Z., He, Z., 2022. Application of multi-layer neural network and hyperspectral reflectance in genome-wide association study for grain yield in bread wheat. Field Crop. Res. 289, pp.108730.

Feng, G., Gu, Y., Wang, C., Zhou, Y., Huang, S., Luo, B., 2024. Wheat fusarium head blight automatic non-destructive detection based on multi-scale imaging: A technical perspective. Plants 13 (13), pp.1722.

Feng, Z.-H., Wang, L.-Y., Yang, Z.-Q., Zhang, Y.-Y., Li, X., Song, L., He, L., Duan, J.-Z., Feng, W., 2022. Hyperspectral monitoring of powdery mildew disease severity in wheat based on machine learning. Front. Plant Sci. 13, pp.828454.

Figueroa, M., Hammond-Kosack, K.E., Solomon, P.S., 2018. A review of wheat diseases—a field perspective. Mol. Plant Pathol. 19 (6), 1523–1536.

Gao, H., Yao, D., Wang, M., Li, C., Liu, H., Hua, Z., Wang, J., 2019. A hyperspectral image classification method based on multi-discriminator generative adversarial networks. Sensors 19 (15), pp.3269.

Gong, Z., Zhong, P., Hu, W., Xiao, Z., Yin, X., 2019. A novel statistical metric learning for hyperspectral image classification. arXiv preprint arXiv:1905.05087.

Gonzalez-Dugo, V., Hernandez, P., Solís, I., Zarco-Tejada, P.J., 2015. Using high-resolution hyperspectral and thermal airborne imagery to assess physiological condition in the context of wheat phenotyping. Remote. Sens. 7 (10), 13586–13605.

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y., 2020. Generative adversarial networks. Commun. ACM 63 (11), 139–144.

Graves, A., 2012. Long short-term memory. Supervised Seq. Label. Recurr. Neural Networks 37–45.

Gu, A., Dao, T., 2023. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752.

Gu, A., Goel, K., Ré, C., 2021. Efficiently modeling long sequences with structured state spaces. arXiv preprint arXiv:2111.00396.

Guerri, M.F., Distante, C., Spagnolo, P., Bougourzi, F., Taleb-Ahmed, A., 2024. Deep learning techniques for hyperspectral image analysis in agriculture: A review. ISPRS Open J. Photogramm. Remote. Sens. pp.100062.

Guo, A., Huang, W., Ye, H., Dong, Y., Ma, H., Ren, Y., Ruan, C., 2020. Identification of wheat yellow rust using spectral and texture features of hyperspectral images. Remote. Sens. 12 (9), pp.1419.

Haagsma, M., Hagerty, C.H., Kroese, D.R., Jachens, E.R., 2019. Hyperspectral reflectance data of wheat in situ and lab scans infected with soilborne wheat mosaic virus. https://ir.library.oregonstate.edu/concern/datasets/z316q855z. (Accessed 3 July 2025).

Habib, A., Xiong, W., He, F., Yang, H.L., Crawford, M., 2016. Improving orthorectification of UAV-based push-broom scanner imagery using derived orthophotos from frame cameras. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 10 (1), 262–276.

He, X., Chen, Y., Lin, Z., 2021. Spatial-spectral transformer for hyperspectral image classification. Remote. Sens. 13 (3), pp.498.

He, Z., Liu, H., Wang, Y., Hu, J., 2017. Generative adversarial networks-based semi-supervised learning for hyperspectral image classification. Remote. Sens. 9 (10), pp.1042.

He, Y., Tu, B., Liu, B., Li, J., Plaza, A., 2024. 3DSS-mamba: 3D-spectral-spatial mamba for hyperspectral image classification. arXiv preprint arXiv:2405.12487.

Hinton, G., 2006. A fast learning algorithm for deep belief nets. Neural Comput.
Hu, W., Huang, Y., Wei, L., Zhang, F., Li, H., 2015. Deep convolutional neural networks for hyperspectral image classification. J. Sensors 15 (1), pp.258619.

Hu, N., Li, W., Du, C., Zhang, Z., Gao, Y., Sun, Z., Yang, L., Yu, K., Zhang, Y., Wang, Z., 2021. Predicting micronutrients of wheat using hyperspectral imaging. Food Chem. 343, pp.128473.

Hu, M., Wu, C., Du, B., Zhang, L., 2023. Binary change guided hyperspectral multCLASS change detection. IEEE Trans. Image Process. 32, 791–806.

Hu, M., Wu, C., Zhang, L., 2022. HyperNet: Self-supervised hyperspectral spatial-spectral feature understanding network for hyperspectral change detection. IEEE Trans. Geosci. Remote Sens. 60, 1–17.

Huang, L., Chen, Y., He, X., 2024a. Spectral-spatial mamba for hyperspectral image classification. arXiv preprint arXiv:2404.18401.

Huang, W., Lamb, D.W., Niu, Z., Zhang, Y., Liu, L., Wang, J., 2007. Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precis. Agric. 8, 187–197.

Huang, H., Shi, G., He, H., Duan, Y., Luo, F., 2019. Dimensionality reduction of hyperspectral imagery based on spatial-spectral manifold learning. IEEE Trans. Cybern. 50 (6), 2604–2616.

Huang, L., Tan, J., Chen, Z., 2024b. Mamba-UAV-SegNet: A multi-scale adaptive feature fusion network for real-time semantic segmentation of UAV aerial imagery. Drones 8 (11), pp.671.

Huang, L., Zhang, H., Ruan, C., Huang, W., Hu, T., Zhao, J., 2020. Detection of scab in wheat ears using in situ hyperspectral data and support vector machine optimized by genetic algorithm. Int. J. Agric. Biological Eng. 13 (2), 182–188.

Jia, X., Li, S., Ke, S., Hu, B., 2020. Overview of spaceborne hyperspectral imagers and the research progress in bathymetric maps. In: Second Target Recognition and Artificial Intelligence Summit Forum, vol. 11427, SPIE, pp. 118–124.

Jin, X., Jie, L., Wang, S., Qi, H.J., Li, S.W., 2018. Classifying wheat hyperspectral pixels of healthy heads and fusarium head blight disease using a deep neural network in the wild field. Remote. Sens. 10 (3), pp.395.

Johnson, R.D., Kasischke, E., 1998. Change vector analysis: A technique for the multispectral monitoring of land cover and condition. Int. J. Remote Sens. 19 (3), 411–426.

Kaur, S., Kakani, V.G., Carver, B., Jarquin, D., Singh, A., 2024. Hyperspectral imaging combined with machine learning for high-throughput phenotyping in winter wheat. Plant Phenome J. 7 (1), pp.e20111.

Khan, I.H., Liu, H., Li, W., Cao, A., Wang, X., Liu, H., Cheng, T., Tian, Y., Zhu, Y., Cao, W., et al., 2021. Early detection of powdery mildew disease and accurate quantification of its severity using hyperspectral images in wheat. Remote. Sens. 13 (18), pp.3612.

Khan, A., Vibhute, A.D., Mali, S., Patil, C., 2022. A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications. Ecol. Informatics 69, pp.101678.

Khotimah, W.N., Bennamoun, M., Boussaid, F., Xu, L., Edwards, D., Sohel, F., 2023. MCE-ST: Classifying crop stress using hyperspectral data with a multiscale conformer encoder and spectral-based tokens. Int. J. Appl. Earth Obs. Geinf. 118, pp.103286.

Kotzagianidis, M.S., Schönlieb, C.-B., 2021. Semi-supervised superpixel-based multi-feature graph learning for hyperspectral image data. IEEE Trans. Geosci. Remote Sens. 60, 1–12.

Kramer, H., 2023. Hyperspectral imaging. URL https://www.eoportal.org/other-space-activities/hyperspectral-imaging#references. (Accessed 30 January 2024).

Krishna, G., Sahoo, R., Pargal, S., Gupta, V., Sinha, P., Bhagat, S., Saharan, M., Singh, R., Chattopadhyay, C., 2014. Assessing wheat yellow rust disease through hyperspectral remote sensing. Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. 40, 1413–1416.

Kussul, N., Lavreniuk, M., Shumilo, L., 2020. Deep recurrent neural network for crop classification task based on sentinel-1 and sentinel-2 imagery. In: IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, pp. 6914–6917.

Kuswidiyanto, L.W., Noh, H.-H., Han, X., 2022. Plant disease diagnosis using deep learning based on aerial hyperspectral images: A review. Remote. Sens. 14 (23), pp.6031.

Lee, H., Kwon, H., 2017. Going deeper with contextual CNN for hyperspectral image classification. IEEE Trans. Image Process. 26 (10), 4843–4855.

Li, Z., Bi, K., Wang, Y., Fang, Z., Zhang, J., 2023. Supervised contrastive learning for open-set hyperspectral image classification. IEEE Geosci. Remote. Sens. Lett. 20, 1–5. http://dx.doi.org/10.1109/LGRS.2023.3319403.

Li, Z., Chen, Z., Cheng, Q., Duan, F., Sui, R., Huang, X., Xu, H., 2022a. UAV-based hyperspectral and ensemble machine learning for predicting yield in winter wheat. Agronomy 12 (1), pp.202.

Li, Q., Gong, H., Dai, H., Li, C., He, Z., Wang, W., Feng, Y., Han, F., Tuniyazi, A., Li, H., et al., 2021. Unsupervised hyperspectral image change detection via deep learning self-generated credible labels. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 14, 9012–9024.

Li, Z., Huang, H., Zhang, Z., Shi, G., 2022b. Manifold-based multi-deep belief network for feature extraction of hyperspectral image. Remote. Sens. 14 (6), pp.1484.

Li, Y., Luo, Y., Zhang, L., Wang, Z., Du, B., 2024. MambaHSI: Spatial-spectral mamba for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens.

Li, K.-Y., Sampaio de Lima, R., Burnside, N.G., Vahtmäe, E., Kutser, T., Sepp, K., Cabral Pinheiro, V.H., Yang, M.-D., Vain, A., Sepp, K., 2022c. Toward automated machine learning-based hyperspectral image analysis in crop yield and biomass estimation. Remote. Sens. 14 (5), pp.1114.

Li, C., Wang, Y., Zhang, X., Gao, H., Yang, Y., Wang, J., 2019. Deep belief network for spectral-spatial classification of hyperspectral Remote Sensingor data. Sensors 19 (1), pp.204.

Li, Y., Zhang, H., Shen, Q., 2017. Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote. Sens. 9 (1), pp.67.

Li, H., Zhang, L., Sun, H., Rao, Z., Ji, H., 2022d. Discrimination of unsound wheat kernels based on deep convolutional generative adversarial network and near-infrared hyperspectral imaging technology. Spectrochim. Acta Part A: Mol. Biomol. Spectrosc. 268, pp.120722.

Li, T., Zhang, J., Zhang, Y., 2014. Classification of hyperspectral image based on deep belief networks. In: 2014 IEEE International Conference on Image Processing. ICIP, IEEE, pp. 5132–5136.

Liang, K., Huang, J., He, R., Wang, Q., Chai, Y., Shen, M., 2020. Comparison of vis-NIR and SWIR hyperspectral imaging for the non-destructive detection of DON levels in fusarium head blight wheat kernels and wheat flour. Infrared Phys. Technol. 106, 103281.

Lingwal, S., Bhatia, K.K., Tomer, M.S., 2021. Image-based wheat grain classification using convolutional neural network. Multimedia Tools Appl. 1–25.

Liu, H., 2021. The promise of hyperspectral imaging for the early detection of crown rot in wheat. https://adelaide.fishshare.com/articles/dataset/The_Promise_of_Hyperspectral_Imaging_for_the_Early_TDetection_of_Crown_Rot_in_Wheat/17075138.

Liu, S., Bruzzone, L., Bovolo, F., Du, P., 2016. Unsupervised multitemporal spectral unmixing for detecting multiple changes in hyperspectral images. IEEE Trans. Geosci. Remote Sens. 54 (5), 2733–2748.

Liu, X., Qiao, Y., Xiong, Y., Cai, Z., Liu, P., 2020. Cascade conditional generative adversarial nets for spatial-spectral hyperspectral sample generation. Sci. China Inf. Sci. 63, 1–16.

Liu, Y., Sun, L., Liu, B., Wu, Y., Ma, J., Zhang, W., Wang, B., Chen, Z., 2023. Estimation of winter wheat yield using multiple temporal vegetation indices derived from UAV-based multispectral and hyperspectral imagery. Remote. Sens. 15 (19), pp.4800.

Liu, T., Yang, T., Zhu, S., Mou, N., Zhang, W., Wu, W., Zhao, Y., Yao, Z., Sun, J., Chen, C., et al., 2024a. Estimation of wheat biomass based on phenological identification and spectral response. Comput. Electron. Agric. 222, pp.109076.

Liu, S., Yin, C., Zhang, H., 2024b. CESA-MCFormer: An efficient transformer network for hyperspectral image classification by eliminating redundant information. Sensors 24 (4), pp.1187.

Liu, H., Zhu, H., Wang, P., 2017. Quantitative modelling for leaf nitrogen content of winter wheat using UAV-based hyperspectral data. Int. J. Remote Sens. 38 (8–10), 2117–2134.

Lodhi, V., Chakravarty, D., Mitra, P., 2018. Hyperspectral imaging for earth observation: Platforms and instruments. J. Indian Inst. Sci. 98, 429–443.

Lodhi, V., Chakravarty, D., Mitra, P., 2019. Hyperspectral imaging system: Development aspects and recent trends. Sens. Imaging 20, 1–24.

Lu, B., Dao, P.D., Liu, J., He, Y., Shang, J., 2020. Recent advances of hyperspectral imaging technology and applications in agriculture. Remote. Sens. 12 (16), pp.2659.

Lu, N., Wu, Y., Zheng, H., Yao, X., Zhu, Y., Cao, W., Cheng, T., 2022. An assessment of multi-view spectral information from UAV-based color-infrared images for improved estimation of nitrogen nutrition status in winter wheat. Precis. Agric. 23 (5), 1653–1674.

Lundeen, S., 2022. AVIRIS data - AVIRIS moffett field image cube. https://aviris.jpl.nasa.gov/data/image cube.html.

Luo, H., 2018. Shorten spatial-spectral RNN with parallel-GRU for hyperspectral image classification. arXiv preprint arXiv:1810.12563.

Ma, H., Huang, W., Jing, Y., Pignatti, S., Laneve, G., Dong, Y., Ye, H., Liu, L., Guo, A., Jiang, J., 2019. Identification of fusarium head blight in winter wheat ears using continuous wavelet analysis. Sensors 20 (1), pp.20.

Ma, J., Zheng, B., He, Y., 2022. Applications of a hyperspectral imaging system used to estimate wheat grain protein: A review. Front. Plant Sci. 13, pp.837200.

Madhavan, J., Salim, M., Durairaj, U., Kotteeswaran, R., 2023. Wheat seed classification using neural network pattern recognizer. Mater. Today: Proc. 81, 341–345.

Mariotto, I., Thenkabail, P.S., Huete, A., Slonecker, E.T., Platonov, A., 2013. Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission. Remote Sens. Environ. 139, 291–305.

Marshall, M., Thenkabail, P., 2015. Advantage of hyperspectral EO-1 hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation. ISPRS J. Photogramm. Remote Sens. 108, 205–218.

Moghimi, C., Yang, A., 2018. Hyperspectral image dataset for salt stress phenotyping of wheat. https://conservancy.umn.edu/handle/11299/195720. (Accessed 3 July 2025).

Moghimi, A., Yang, C., Anderson, J.A., 2020. Aerial hyperspectral imagery and deep neural networks for high-throughput yield phenotyping in wheat. Comput. Electron. Agric. 172, pp.105299.
Montesinos-López, O.A., Montesinos-López, A., Crossa, J., de Los Campos, G., Alvarado, G., Suchismita, M., Rutkoski, J., González-Pérez, L., Burguño, J., 2017. Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data. Plant Methods 13, 1–23.

Mou, L., Ghamisi, P., Zhu, X.X., 2017a. Deep recurrent neural networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 55 (7), 3639–3655.

Mou, L., Ghamisi, P., Zhu, X.X., 2017b. Unsupervised spectral-spatial feature learning via deep residual conv-deconv network for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 56 (1), 391–406.

Mughees, A., Tao, L., 2016. Efficient deep auto-encoder learning for the classification of hyperspectral images. In: 2016 International Conference on Virtual Reality and Visualization. ICVRV, IEEE, pp. 44–51.

Mughees, A., Tao, L., 2018. Multiple deep-belief-network-based spectral-spatial classification of hyperspectral images. Tsinghua Sci. Technol. 24 (2), 183–194.

Ndikumana, E., Ho Tong Minh, D., Baghdadi, N., Courault, D., Hossard, L., 2018. Deep recurrent neural network for agricultural classification using multitemporal SAR sentinel-1 for Camargue, France. Remote. Sens. 10 (8), pp.1217.

Özkan, K., Erol, S., Şahin, I., 2021. Wheat kernels classification using visible-near infrared camera based on deep learning. Pamukkale Üniversitesi Mühendislik Bilim. Derg. 27 (5), 618–626.

Pang, L., Rui, X., Cui, L., Wang, H., Meng, D., Cao, X., 2024. HIR-diff: Unsupervised hyperspectral image restoration via improved diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3005–3014.

Paoletti, M.E., Haut, J.M., Plaza, J., Plaza, A., 2018. A new deep convolutional neural network for fast hyperspectral image classification. ISPRS J. Photogramm. Remote Sens. 145, 120–147.

Paoletti, M., Haut, J., Plaza, J., Plaza, A., 2019. Deep learning classifiers for hyperspectral imaging: A review. ISPRS J. Photogramm. Remote Sens. 158, 279–317.

Polk, S.L., Cui, K., Chan, A.H., Coomes, D.A., Plemmons, R.J., Murphy, J.M., 2023. Unsupervised diffusion and volume maximization-based clustering of hyperspectral images. Remote. Sens. 15 (4), pp.1053.

Que, H., Zhao, X., Sun, X., Zhu, Q., Huang, M., 2023. Identification of wheat kernel varieties based on hyperspectral imaging technology and grouped convolutional neural network with feature intervals. Infrared Phys. Technol. 131, pp.104653.

Rangarajan, A.K., Whetton, R.L., Mouazen, A.M., 2022. Detection of fusarium head blight in wheat using hyperspectral data and deep learning. Expert Syst. Appl. 208, pp.118240.

Ranjan, R., Chopra, U.K., Sahoo, R.N., Singh, A.K., Pradhan, S., 2012. Assessment of plant nitrogen stress in wheat (Triticum aestivum L.) through hyperspectral indices. Int. J. Remote Sens. 33 (20), 6342–6360.

Ren, Y., Ye, H., Huang, W., Ma, H., Guo, A., Ruan, C., Liu, L., Qian, B., 2021. A new spectral index for the quantitative identification of yellow rust using fungal spore information. Big Earth Data 5 (2), 201–216.

Roy, S.K., Krishna, G., Dubey, S.R., Chaudhuri, B.B., 2019. HybridSN: Exploring 3-D-2-D CNN feature hierarchy for hyperspectral image classification. IEEE Geosci. Remote. Sens. Lett. 17 (2), 277–281.

Sabanci, K., Kayabasi, A., Toktas, A., 2017. Computer vision-based method for classification of wheat grains using artificial neural network. J. Sci. Food Agric. 97 (8), 2588–2593.

Sadeghi-Tehran, P., Virlet, N., Hawkesford, M.J., 2021. A neural network method for classification of sunlit and shaded components of wheat canopies in the field using high-resolution hyperspectral imagery. Remote. Sens. 13 (5), pp.898.

Salmela, L., Tsipinakis, N., Foi, A., Billet, C., Dudley, J.M., Genty, G., 2021. Predicting ultrafast nonlinear dynamics in fibre optics with a recurrent neural network. Nat. Mach. Intell. 3 (4), 344–354.

Sellami, A., Farah, I., 2019. Spectra-spatial graph-based deep restricted boltzmann networks for hyperspectral image classification. In: 2019 Photonics & Electromagnetics Research Symposium-Spring. PIERS-Spring, IEEE, pp. 1055–1062.

Sereda, I., Danilov, R., Kremneva, O., Zimin, M., Podushin, Y., 2023. Development of methods for remote monitoring of leaf diseases in wheat agrocenoses. Plants 12 (18), pp.3223.

Sethy, P.K., 2022. Identification of wheat tiller based on AlexNet-feature fusion. Multimedia Tools Appl. 81 (6), 8309–8316.

Sharma, A., Singh, T., Garg, N.M., 2024. Nondestructive identification of wheat seed variety and geographical origin using near-infrared hyperspectral imagery and deep learning. J. Chemom. pp.e3585.

Sheng, J., Zhou, J., Wang, J., Ye, P., Fan, J., 2024. DualMamba: A lightweight spectral-spatial mamba-convolution network for hyperspectral image classification. arXiv preprint arXiv:2406.07050.

Shiferaw, B., Smale, M., Braun, H.-J., Duveiller, E., Reynolds, M., Muricho, G., 2013. Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security. Food Secur. 5, 291–317.

Sigger, N., Vien, Q.-T., Nguyen, S.V., Tozzi, G., Nguyen, T.T., 2024. Unveiling the potential of diffusion model-based framework with transformer for hyperspectral image classification. Sci. Rep. 14 (1), pp.8438.

Signoroni, A., Savardi, M., Baronio, A., Benini, S., 2019. Deep learning meets hyperspectral image analysis: A multidisciplinary review. J. Imaging 5 (5), pp.52.

Simón Sánchez, A.-M., González-Piqueras, J., de la Ossa, L., Calera, A., 2022. Convolutional neural networks for agricultural land use classification from Sentinel-2 image time series. Remote. Sens. 14 (21), pp.5373.

Singh, P.S., Karthikeyan, S., 2022. Enhanced classification of remotely sensed hyperspectral images through efficient band selection using autoencoders and genetic algorithm. Neural Comput. Appl. 34 (24), 21539–21550.

Slavkovikj, V., Verstockt, S., De Neve, W., Van Hoecke, S., Van de Walle, R., 2015. Hyperspectral image classification with convolutional neural networks. In: Proceedings of the 23rd ACM International Conference on Multimedia. pp. 1159–1162.

Song, W., Li, S., Fang, L., Lu, T., 2018. Hyperspectral image classification with deep feature fusion network. IEEE Trans. Geosci. Remote Sens. 56 (6), 3173–3184.

Song, Y., Teng, G., Yuan, Y., Liu, T., Sun, Z., 2021. Assessment of wheat chlorophyll content by the multiple linear regression of leaf image features. Inf. Process. Agric. 8 (2), 232–243.

Tao, H., Feng, H., Xu, L., Miao, M., Yang, G., Yang, X., Fan, L., 2020. Estimation of the yield and plant height of winter wheat using UAV-based hyperspectral images. Sensors 20 (4), pp.1231.

Tejasree, G., Agilandeeswari, L., 2024. An extensive review of hyperspectral image classification and prediction: techniques and challenges. Multimedia Tools Appl. 1–98.

Terentev, A., Badenko, V., Shaydayuk, E., Emelyanov, D., Eremenko, D., Klabukov, D., Fedotov, A., Dolzhenko, V., 2023. Hyperspectral remote sensing for early detection of wheat leaf rust caused by puccinia triticina. Agriculture 13 (6), pp.1186.

Terentev, A., Dolzhenko, V., Fedotov, A., Eremenko, D., 2022. Current state of hyperspectral remote sensing for early plant disease detection: A review. Sensors 22 (3), pp.757.

Thenkabail, P.S., Aneece, I.P., Oliphant, A.J., Foley, D.J., Teluguntla-crops, P., 2018. Global hyperspectral imaging spectral-library of agricultural-crops & vegetation. https://www.usgs.gov/centers/western-geographic-science-center/science/global-hyperspectral-imaging-spectral-library. (Accessed 4 July 2025).

Transon, J., d'Andrimont, R., Magnard, A., Defourny, P., 2018. Survey of hyperspectral earth observation applications from space in the sentinel-2 context. Remote. Sens. 10 (2), pp.157.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I., 2017. Attention is all you need. Adv. Neural Inf. Process. Syst. 30.

Venkatesan, R., Prabu, S., 2019. Hyperspectral image features classification using deep learning recurrent neural networks. J. Med. Syst. 43 (7), pp.216.

Vorovencii, I., 2010. The hyperspectral sensors used in satellite and aerial remote sensing. pp. 51–56, Bulletin of the Transilvania University of Brasov. Series II: Forestry Wood Industry Agricultural Food Engineering.

Wan, L., Li, H., Li, C., Wang, A., Yang, Y., Wang, P., 2022. Hyperspectral sensing of plant diseases: Principle and methods. Agronomy 12 (6), pp.1451.

Wan, X., Zhao, C., Wang, Y., Liu, W., 2017. Stacked sparse autoencoder in hyperspectral data classification using spectral-spatial, higher order statistics and multifractal spectrum features. Infrared Phys. Technol. 86, 77–89.

Wang, Q., Chen, M., Zhang, J., Kang, S., Wang, Y., 2021a. Improved active deep learning for semi-supervised classification of hyperspectral image. Remote. Sens. 14 (1), pp.171.

Wang, J., Gao, F., Dong, J., Du, Q., 2020. Adaptive dropblock-enhanced generative adversarial networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 59 (6), 5040–5053.

Wang, C., Liu, B., Liu, L., Zhu, Y., Hou, J., Liu, P., Li, X., 2021b. A review of deep learning used in the hyperspectral image analysis for agriculture. Artif. Intell. Rev. 54 (7), 5205–5253.

Whetton, R.L., Hassall, K.L., Waine, T.W., Mouazen, A.M., 2018. Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 1: Laboratory study. Biosyst. Eng. 166, 101–115.

Windrim, L., Ramakrishnan, R., Melkumyan, A., Murphy, R.J., Chlingaryan, A., 2019. Unsupervised feature-learning for hyperspectral data with autoencoders. Remote. Sens. 11 (7), pp.864.

Wu, H., Prasad, S., 2017. Semi-supervised deep learning using pseudo labels for hyperspectral image classification. IEEE Trans. Image Process. 27 (3), 1259–1270.

Wu, Q., Zhu, D., Wang, C., Ma, Z., Wang, J., 2012. Diagnosis of freezing stress in wheat seedlings using hyperspectral imaging. Biosyst. Eng. 112 (4), 253–260.

Xie, J., Hua, J., Chen, S., Wu, P., Gao, P., Sun, D., Lyu, Z., Lyu, S., Xue, X., Lu, J., 2023. HyperSFormer: A transformer-based end-to-end hyperspectral image classification method for crop classification. Remote. Sens. 15 (14), pp.3491.

Xing, C., Ma, L., Yang, X., 2016. Stacked denoise autoencoder based feature extraction and classification for hyperspectral images. J. Sensors 16 (1) , pp.3632943.

Xu, L., Cao, B., Zhao, F., Ning, S., Xu, P., Zhang, W., Hou, X., 2023. Wheat leaf disease identification based on deep learning algorithms. Physiol. Mol. Plant Path. 123, pp.101940.

Xue, Z., 2020. A general generative adversarial capsule network for hyperspectral image spectral-spatial classification. Remote. Sens. Lett. 11 (1), 19–28.

Yang, J., Guo, Y., Wang, X., 2019. Feature extraction of hyperspectral images based on deep Boltzmann machine. IEEE Geosci. Remote. Sens. Lett. 17 (6), 1077–1081.
Yang, M.-D., Hsu, Y.-C., Liu, T.-T., Huang, H.-H., 2025. Enhancing grain moisture prediction in multiple crop seasons using domain adaptation AI. Comput. Electron. Agric. 231, 110058.

Yang, A., Li, M., Ding, Y., Fang, L., Cai, Y., He, Y., 2024a. GraphMamba: An efficient graph structure learning vision mamba for hyperspectral image classification. arXiv preprint arXiv:2407.08255.

Yang, B., Ma, J., Yao, X., Cao, W., Zhu, Y., 2021. Estimation of leaf nitrogen content in wheat based on fusion of spectral features and deep features from near infrared hyperspectral imagery. Sensors 21 (2), pp.613.

Yang, Y., Nan, R., Mi, T., Song, Y., Shi, F., Liu, X., Wang, Y., Sun, F., Xi, Y., Zhang, C., 2023. Rapid and nondestructive evaluation of wheat chlorophyll under drought stress using hyperspectral imaging. Int. J. Mol. Sci. 24 (6), pp.5825.

Yang, X., Ye, Y., Li, X., Lau, R.Y., Zhang, X., Huang, X., 2018. Hyperspectral image classification with deep learning models. IEEE Trans. Geosci. Remote Sens. 56 (9), 5408–5423.

Yang, J.X., Zhou, J., Wang, J., Tian, H., Liew, A.W.C., 2024b. Hsimamba: Hyper spectral imaging efficient feature learning with bidirectional state space for classification. arXiv preprint arXiv:2404.00272.

Yipeng, L., Wenbing, L., Kaixuan, H., Wentao, T., Ling, Z., Shizhuang, W., Lin-
sheng, H., 2022. Determination of wheat kernels damaged by fusarium head
blight using monochromatic images of effective wavelengths from hyperspectral
imaging coupled with an architecture self-search deep network. Food Control 135,
pp.108819.

Zhan, Y., Hu, D., Wang, Y., Yu, X., 2017. Semisupervised hyperspectral image classification based on generative adversarial networks. IEEE Geosci. Remote. Sens. Lett. 15 (2), 212–216.

Zhan, Y., Wang, Y., Yu, X., 2023. Semisupervised hyperspectral image classification based on generative adversarial networks and spectral angle distance. Sci. Rep. 13 (1), pp.22019.

Zhang, Z., 2020. Semi-supervised hyperspectral image classification algorithm based on graph embedding and discriminative spatial information. Microprocess. Microsyst. 75, pp.103070.

Zhang, X., Han, L., Dong, Y., Shi, Y., Huang, W., Han, L., González-Moreno, P., Ma, H., Ye, H., Sobeih, T., 2019a. A deep learning-based approach for automated yellow rust disease detection from high-resolution hyperspectral UAV images. Remote. Sens. 11 (13), pp.1554.

Zhang, W., Li, Z., Li, G., Zhuang, P., Hou, G., Zhang, Q., Li, C., 2023a. GACNet: Generate adversarial-driven cross-aware network for hyperspectral wheat variety identification. IEEE Trans. Geosci. Remote Sens.

Zhang, N., Pan, Y., Feng, H., Zhao, X., Yang, X., Ding, C., Yang, G., 2019b. Development of Fusarium head blight classification index using hyperspectral microscopy images of winter wheat spikelets. Biosyst. Eng. 186, 83–99.

Zhang, S., Qi, X., Duan, J., Yuan, X., Zhang, H., Feng, W., Guo, T., He, L., 2024. Comparison of attention mechanism-based deep learning and transfer strategies for wheat yield estimation using multisource temporal drone imagery. IEEE Trans. Geosci. Remote Sens. 62, 1–23.

Zhang, X., Tian, S., Wang, G., Zhou, H., Jiao, L., 2023b. Diffudc: Unsupervised hyperspectral image change detection with semantic correlation diffusion model. arXiv preprint arXiv:2305.12410.

Zhang, D., Wang, Q., Lin, F., Yin, X., Gu, C., Qiao, H., 2020. Development and evaluation of a new spectral disease index to detect wheat fusarium head blight using hyperspectral imaging. Sensors 20 (8), pp.2260.

Zhao, P., Cai, W., Zhou, W., Li, N., 2024a. Revolutionizing automated pear picking using mamba architecture. Plant Methods 20 (1), pp.167.

Zhao, R., Tang, W., Liu, M., Wang, N., Sun, H., Li, M., Ma, Y., 2024b. Spatial-spectral feature extraction for in-field chlorophyll content estimation using hyperspectral imaging. Biosyst. Eng. 246, 263–276.

Zhao, J., Zhang, J., Huang, H., Zhang, J., 2024c. Enhancing semi-supervised few-shot hyperspectral image classification via progressive sample selection. Remote. Sens. 16 (10), pp.1747.

Zheng, Q., Huang, W., Cui, X., Dong, Y., Shi, Y., Ma, H., Liu, L., 2018. Identification of wheat yellow rust using optimal three-band spectral indices in different growth stages. Sensors 19 (1), pp.35.

Zhong, P., Gong, Z., Li, S., Schönlieb, C.-B., 2017a. Learning to diversify deep belief networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 55 (6), 3516–3530.

Zhong, Z., Li, J., Luo, Z., Chapman, M., 2017b. Spectral-spatial residual network for hyperspectral image classification: A 3-D deep learning framework. IEEE Trans. Geosci. Remote Sens. 56 (2), 847–858.

Zhou, W., Kamata, S.-I., Wang, H., Wong, M.-S., et al., 2024. Mamba-in-mamba: Centralized mamba-cross-scan in tokenized mamba model for hyperspectral image classification. arXiv preprint arXiv:2405.12003.

Zhu, L., Chen, Y., Ghamisi, P., Benediktsson, J.A., 2018. Generative adversarial networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 56 (9), 5046–5063.

Zhu, J., Li, H., Rao, Z., Ji, H., 2023. Identification of slightly sprouted wheat kernels using hyperspectral imaging technology and different deep convolutional neural networks. Food Control 143, pp.109291.

Zidi, F.A., Boukhari, D.E., Sellam, A.Z., Ouafi, A., Distante, C., Bekhouche, S.E., Taleb-Ahmed, A., 2025. LoLA-SpecViT: Local attention swiglu vision transformer with LoRA for hyperspectral imaging. arXiv preprint arXiv:2506.17759.

Zidi, F., Ouafi, A., 2024. Hyperspectral imaging using deep learning in wheat diseases. In: 2024 8th International Conference on Image and Signal Processing and their Applications. ISPA, IEEE, pp. 1–8.