Deep-Machine Learning Integration for Enhanced Pest Detection in Mangifera indica (Mango)

Abstract

Mango (Mangifera indica), the third most widely consumed fruit in Sri Lanka after banana and papaya, has experienced steady growth in cultivation and yield; however, pest and disease infestations still cause substantial yield losses and degrade fruit quality. Since most pests inhabit mango leaves, early and accurate leaf-based detection is crucial for effective crop management, yet traditional manual inspection remains time consuming, labor-intensive, and often unreliable due to pest evolution and limited expert availability. To address these challenges, this study proposes an automated pest detection framework that integrates deep learning–based feature extraction with machine learning classification. The research uses ten classes from the MangoPestClassification dataset, consisting of real-world images of healthy and pest-affected mango leaves captured under diverse environmental conditions to represent realistic field scenarios. Pre-trained convolutional neural network (CNN) models, VGG19, ResNet50, InceptionV3, DenseNet121, MobileNetV2, and UNet, are employed to extract discriminative image features. These features are then classified using Logistic Regression, Random Forest, and Support Vector Machine (SVM) classifiers. This hybrid deep-learning–machine-learning approach ensures both strong feature representation and efficient classification. A 10-fold cross-validation strategy is applied to ensure reliable performance evaluation. Experimental results show high accuracy and robust ness across model combinations. ResNet50 features with Logistic Regression achieved 96.65±1.06% accuracy using 200 images per class, while ResNet50 features with an SVM (RBF kernel) yielded 99.09±0.30% accuracy with 1,000 images per class. Overall, the findings highlight that ResNet-based feature extraction combined with either SVM (RBF) or Logistic Regression offers an effective and practical solution for automated mango pest detection, reducing dependence on manual monitoring.