Abstract:
Mangifera indica, a key fruit crop in Sri Lanka, supports both agriculture and rural livelihoods but suffers significant losses from pest infestations. Early pest detection is crucial to reduce damage, yet manual inspection is laborious and inefficient for large farms. Advances in artificial intelligence
enable automated pest detection, offering a more accurate and efficient approach to mango crop protection. This study presents a machine learning approach for automatically detecting pests on
mango leaves, providing farmers and agricultural experts with a practical tool for timely intervention. The study uses ten classes from the MangoPestClassification dataset, with 1,000 images per
class, comprising real-world images of healthy and pest-affected mango leaves captured under diverse environmental conditions, thereby representing realistic field scenarios. To prepare themango leaf images for classification, three feature extraction techniques were applied: Histogram of Oriented Gradients (HOG) to capture shape and edge information, Bag of Features (BoF) to
represent local descriptors, and Wavelet transforms to analyze texture and frequency components. These extracted features were then used to train and evaluate three machine learning classifiers:
Support Vector Machines (SVM), Random Forest, and Logistic Regression. To ensure robust and reliable performance, 10-fold cross-validation was employed, providing an accurate assessment
of the models’ classification capabilities. The experimental results indicated that with 200 images per class, the best performance was achieved using HOG+Wavelet with Random Forest at an
accuracy of 81.20%, while with 1000 images per class, HOG+PCA with SVM (RBF) reached 91.64%. Overall, HOG-based features combined with SVM (RBF) or Random Forest provide the most reliable framework for mango pest classification and establish a strong foundation for practical automated detection systems. The study highlights the potential of machine learning to enhance
pest monitoring in mango cultivation. By minimizing reliance on manual inspections and enabling early pest detection, the proposed method can contribute to increased yields, better fruit quality, and more sustainable mango farming practices in Sri Lanka.
