# Research Progress in Intelligent Diagnosis Key Technology for Orchard Nutrients
## 2 Rapid Detection Technology for Nutrients  
### Rapid Detection Technology for Tree Nutrients  
At present, research on the rapid detection technology for tree nutrients was mainly based on RGB images, multispectral, hyperspectral, and combined spectra, and focused on preprocessing algorithms and regression models. Based on RGB images, [PERSON] et al. explored different vegetation indices (VIs) extracted from aerial RGB images acquired in different flights to differentiate the nutritional and water statuses of Hass avocado plantations [1]. [PERSON] et al. applied a color factor combination regression model to Salix subowensis Cheng images to characterize the canopy distribution of SPAD, achieving the visualization of the chlorophyll content in the distribution of the entire plant [5]. [PERSON] et al. proposed a prediction model of nitrogen content in apple leaves based on combined color features to predict the nitrogen content at the flowering, young fruit, and fruit expansion stages [6]. [PERSON] et al. proposed a model that utilizes image processing technology and machine learning techniques to enhance the accuracy of potassium detection in apple leaves based on MLR-LDA-SVM [7]. A specific comparison was presented in Table 1.  
Based on multispectral data, [PERSON] et al. proposed a novel methodology to determine leaf nutrient concentrations in citrus trees using unmanned aerial vehicle (UAV) multispectral imagery and artificial intelligence [8]. [PERSON] et al. used UAV multispectral indices to perform ordinary linear regression, multivariate stepwise regression, and ridge regression inversion models the on leaf NPK content [9]. [PERSON] et al. designed a portable crop chlorophyll detection device based on ambient light correction [10]. [PERSON] et al. collected citrus canopy spectral data, used a multispectral shadow index to remove canopy shadows and soil background, and predicted the chlorophyll content based on vegetation indices and texture features [11]. To overcome the influence of the environment and canopy structure on reflectivity, [PERSON] et al. used a 3D radiative transfer model and UAV multispectral imagery to estimate the canopy-scale chlorophyll content in apple orchards [12]. A specific comparison was presented in Table 2.  
Based on hyperspectral data, [PERSON] et al. proposed a potassium content prediction method for citrus leaves based on hyperspectral and deep transfer learning [13]. [PERSON] established a non-destructive monitoring model of the functional nitrogen content in citrus leaves during the expansion and transformation stages based on hyperspectral data [14]. [PERSON] et al. attempted to quantify soluble carbohydrates and starch in dried and powdered tissues of almond by visible-to-shortwave infrared reflectance spectroscopy [4]. [PERSON] et al. established a novel approach for rapidly estimating the status of NPK in apple tree leaves based on visible/near-infrared spectroscopy coupled with machine learning [15]. [PERSON] et al. proposed a novel method for estimating the leaf chlorophyll content in citrus [16]. [PERSON] et al. presented a rapid and non-destructive approach for the estimation and mapping of nitrogen content in apple trees at the leaf and canopy levels using hyperspectral imaging [17]. A specific comparison was presented in Table 3.