## 1 Introduction  
Agricultural productivity is constantly challenged by various environmental stressors, among which pest infestations are a leading cause of crop yield reduction. Invertebratepests alone are responsible for up to 20% of major grain crop losses worldwide, a percentage expected to rise due to ongoing climate change [1]. Maize (_Zea mays_), a staple crop essential to global food security, is particularly vulnerable to pests. Among these, _Zyginidia pullula_, a leafhopper species, significantly impacts maize productivity by feeding on the mesophyll cells of maize leaves, causing chlorophyll depletion, necrotic lesions, and impaired photosynthetic activity. Severe infestations can lead to premature leaf senescence, ultimately reducing overall crop yield and economic viability.  
Traditional pest monitoring methods typically rely on manual field inspections, which are not only time-consuming and labor-intensive but also prone to human error [2]. These limitations underscore the urgent need for automated, scalable, and reliable pest damage detection systems. In recent years, advancements in computer vision and artificial intelligence (AI) have revolutionized various agricultural applications, particularly in the detection and classification of plant diseases and pest-induced damage. Machine learning (ML)-based approaches, especially deep learning models, offer a promising alternative by enabling real-time, high-accuracy assessment of crop health through image analysis [3].  
Despite significant progress in ML-driven plant disease and pest detection, most existing studies focus on identifying the presence or absence of pests or diseases, often through binary classification [4]. While machine learning and deep learning techniques have been widely used for crop damage assessment, limited attention has been given to classifying pest-induced damage severity, especially in maize crops. In particular, there is a lack of studies addressing small-scale pests such as _Zyginidia pullula_, whose symptoms are often subtle and hard to detect. This gap highlights the necessity for a robust and automated framework capable of quantifying varying severity levels of pest damage.  
This study proposes a novel hybrid machine learning approach to detect and assess pest-induced damage in maize leaves using a self-compiled dataset. The methodology integrates both traditional and deep learning-based feature extraction techniques, leveraging Gabor filters, Gray Level Co-occurrence Matrix (GLCM), Hue-Saturation-Value (HSV) color space, and Convolutional Neural Networks (CNNs), specifically ResNet-50, DenseNet-201, and EfficientNet-B2. Image preprocessing and segmentation are performed using Contrast Limited Adaptive Histogram Equalization (CLAHE) and U2 Net, respectively, followed by Principal Component Analysis (PCA) for dimensionality reduction. Classification is conducted using Support Vector Machines (SVM), Random Forest (RF), and Artificial Neural Networks (ANN).  
The primary contributions of this study are:  
* The development of an automated framework for pest-induced damage detection and severity assessment in maize crops, addressing a critical gap in the literature.
* The integration of both traditional hand-crafted and deep learning-based features to improve classification performance.
* A comparative evaluation of different machine learning classifiers to determine the most effective approach for maize pest damage classification.  
To illustrate the dataset used in this study, Figure 1 presents an example of a maize leaf affected by _Zyginidia pullula_. The visible damage includes necrotic spots and chlorophyll depletion, which are characteristic symptoms of pest infestation. If left undetected, such damage can severely affect plant health, leading to reduced photosynthetic activity and eventual yield loss.  
The proposed approach aims to enhance precision agriculture by facilitating early detection and targeted intervention strategies, ultimately minimizing crop losses and improving agricultural sustainability.