## 2 Related Works  
The detection and classification of plant diseases play a crucial role in improving crop yield and maintaining agricultural sustainability. Recent advances in computer vision and deep learning have significantly contributed to automating this process, with convolutional neural networks (CNNs) emerging as the dominant approach. Several studies have proposed various deep learning models for disease detection in different crops, including tomato, maize, chili, bean, cucumber, and grape plants [5; 6; 7].  
For instance, ref. [5] introduced a Modified InceptionResNet-V2 (MIR-V2) architecture, incorporating a transfer learning approach to classify seven distinct types of tomato leaf diseases, achieving an accuracy of 98.92%. Similarly, ref. [8] developed a predictive model integrating feature extraction techniques such as shape, texture, and color, coupled with classifiers including Random Forest, SVM, and ANN. Their results demonstrated the effectiveness of machine learning models in plant disease classification.  
Other studies, such as [9], leveraged the PlantVillage dataset for maize disease classification, while [10] utilized deep learning architectures, including Mask R-CNN, UNet, and PSPNet, to detect lesions in coffee plant leaves. Moreover, refs. [11; 12] employed deep transfer learning techniques for the classification of corn disease types, achieving high accuracy and usability in real-world applications. These studies highlight the potential of CNN-based models for plant disease detection.  
Although deep learning has significantly advanced plant disease classification, research focusing specifically on pest-induced damage assessment remains scarce. Most existing approaches primarily classify diseases based on visual symptoms such as discoloration, necrotic lesions, and fungal infections [2; 13; 14; 15; 16]. However, pest damage differs significantly from plant diseases, as it manifests through progressive degradation of leaf structure, including chlorophyll depletion and mesophyll cell destruction [17; 18]. This structural complexity makes automated detection of pest damage more challenging than disease classification.  
Few studies have attempted to address pest-induced plant damage using machine learning models. For instance, ref. [15] explored the identification of chili pest and disease using deep learning feature extraction combined with SVM, RF, and ANN classifiers, reporting superior performance over conventional methods. Similarly, ref. [6] proposed meta-architectures integrating VGG-Net and ResNet to classify tomato diseases and pests.  
Figure 1: An example of a maize leaf affected by _Zyginidia pullula_, displaying characteristic necrotic spots and chlorophyll depletion caused by pest infestation.  
However, most of these studies focused on pest detection rather than damage assessment and severity quantification.  
Despite the significant economic impact of insect pests on global crop production, there remains a notable gap in automated pest damage quantification. This study aims to bridge this research gap by developing a hybrid machine learning approach for detecting and assessing _Zyginidia pullula_-induced damage in maize leaves. By integrating CNN-based feature extraction with traditional hand-crafted features and machine learning classifiers, this research presents a novel framework for the quantification and classification of pest damage severity, setting it apart from prior studies that primarily focus on disease identification.