## 3 Materials and Methods
### Dataset  
The dataset used in this study was obtained from maize crops cultivated by the authors in an agricultural field at Ege University, Izmir, Turkey. The approximate coordinates of the field are 38.4567\({}^{\circ}\) N latitude and 27.2211\({}^{\circ}\) E longitude. Images were captured periodically throughout the plant growth cycle, specifically in June 2023 and September 2023, to ensure variability in lighting, environmental conditions, and pest damage progression. This real-world dataset enhances the model's generalization capability by incorporating naturally occurring variations found in agricultural settings. Unlike conventional datasets, where plant specimens are manually examined under controlled laboratory conditions, this dataset was constructed to reflect real-world agricultural scenarios, ensuring a more practical and scalable approach for automated image-based analysis. The images were acquired using a Samsung Galaxy S23 smartphone, equipped with a 50-megapixel sensor. The primary camera features an f/1.8 aperture, multi-frame image processing, and an advanced AI-driven image enhancement system, ensuring high-quality images under natural lighting conditions. These specifications allow for the accurate capture of fine-grained texture details, which are crucial for distinguishing different levels of pest-induced damage. The dataset primarily consists of maize leaves affected by _Zyginidia pullula_, a pest known to cause damage by feeding on plant tissues, leading to a reduction in photosynthetic capacity and potential yield loss [19]. Since publicly available datasets focusing specifically on pest-induced damage in maize crops are scarce, this study introduces a dedicated dataset to support machine learning-based classification models. The primary objective of this dataset is to facilitate the development of automated pest damage detection and severity assessment models. All images were collected in an open field under natural daylight, specifically on clear, sunny days to ensure consistent lighting. Although the acquisition was conducted under controlled weather conditions, the images still reflect natural variations in perspective, leaf orientation, and background elements that typically occur in agricultural fields. This approach enhances the generalizability and robustness of the model by simulating real-world conditions where automated detection systems would operate. One of the main challenges in constructing such datasets is the limited availability of labeled data for pest-induced damage assessment. To address this, a dataset tailored for feature extraction using both deep learning-based and handcrafted techniques was  
In the second stage, as illustrated in Figure 2, infected samples were further categorized based on severity: (b) low infection, (c) medium infection, and (d) high infection, representing the increasing impact of pest infestation. The specific criteria used to define these severity levels are summarized in Table 2, including visual symptoms and the estimated percentage of leaf area affected. Specifically, low infection refers to images displaying minor discoloration and localized damage. Medium infection includes images showing more extensive chlorophyll depletion and pest-induced tissue destruction. High infection corresponds to images with severe structural degradation, indicating significant crop damage. The labeling process was conducted manually by domain experts, ensuring a high level of precision. No dedicated annotation software was used; instead, each image was visually inspected and categorized based on expert judgment, then organized into class-specific folders. Although visual inspection may involve a degree of subjectivity, symptom-based criteria were used to ensure consistency, including thresholds based on discoloration, necrosis, and the estimated percentage of affected leaf area. While no standardized reference exists specifically for _Zyginidia pullula_, the labeling approach followed common practices used in field-based pest severity evaluation studies [20].