## 5 Discussion  
This study introduces a novel approach for detecting and assessing the damage caused by _Zyginidia pullula_ in maize plants by integrating deep learning-based and handcrafted feature extraction techniques. The experimental results indicate that hybrid feature extraction methods significantly enhance classification accuracy compared to individual feature sets. Among the evaluated models, the combination of ResNet50, HSV, GLCM, and Gabor features, when used with an Artificial Neural Network (ANN), achieved the highest classification accuracy of 92.55%. Furthermore, for severity classification, the best-performing model reached an accuracy of 83.78%, demonstrating the effectiveness of multi-feature fusion in pest damage detection. While recent studies have effectively used deep learning models for pest and disease detection in maize, most focus either on general pest classification or disease type identification, without addressing the severity of damage. For instance, approaches such as [32] or [33] targeting diseases like Maize Lethal Necrosis (MLN) and Maize Streak Virus (MSV) emphasize classifying visual symptoms but overlook multi-level damage assessment. Some studies [34] further employ adaptive thresholding on CNN class activation maps to estimate severity without expert input; however, these rely on pixel-level estimations and lack structured severity categories. In contrast, our study introduces a hybrid feature extraction approach that combines deep and hand-crafted features to classify the severity of damage caused specifically by _Zyginidia pullula_. By integrating expert-defined visual criteria with interpretable severity levels, the proposed method addresses a critical gap in pest damage quantification for maize, offering both precision and practicality for real-world applications. Unlike conventional studies that primarily focus on disease classification in plants, this research specifically addresses pest-induced damage assessment, a relatively unexplored area in agricultural image analysis. While previous studies have successfully employed Convolutional Neural Networks (CNNs) for plant disease detection, a significant gap remains in the quantification of pest-induced damage, particularly for small-scale pests such as _Zyginidia pullula_, where the damage is often subtle and not visually distinct. The findings of this study show that hybrid feature extraction offers superior performance over single-feature-based approaches, aligning with previous research in plant disease classification but extending its applicability to pest impact assessment. The primary contribution of this research lies in the development of an automated framework capable of accurately detecting and quantifying pest-induced damage levels in maize leaves. By integrating handcrafted and deep learning-based features, a more comprehensive representation of infected regions was achieved, leading to improved classification accuracy. The results also underscore the importance of feature fusion, revealing that combining multiple feature extraction techniques enhances model robustness and reliability. These insights contribute to precision agriculture by facilitating early pest detection and enabling more effective pest management strategies. In future studies, a comparative evaluation of different segmentation networks such as UNet, DeepLab, and PSPNet could be conducted to further investigate the impact of segmentation choice on overall model performance. Although the dataset used in this study is relatively limited in size, it addresses a critical gap in the literature, as no publicly available dataset focusing on _Zyginidia pullula_-induced damage in maize was found. Therefore, a custom dataset was created under real field conditions and labeled by expert-defined visual criteria. To ensure the reliability of the results despite the dataset size, several precautions were taken, including stratified five-fold cross-validation and dimensionality reduction through PCA.