Insect Classification Using Deep Learning and Machine Learning: A Comparative Study for Agricultural Applications

Abstract

Insect pests pose a significant threat to global agricultural productivity, necessitating rapid and accurate identification methods. Traditional taxonomic approaches are labor-intensive and require expert knowledge, making automated solutions increasingly vital. This study explores the application of machine learning (ML) and deep learning (DL) techniques for insect classification, comparing their performance on a custom dataset of field-captured insect images. We implement traditional ML algorithms (e.g., Support Vector Machines, Random Forests) and DL models (e.g., Convolutional Neural Networks) to classify 10 common agricultural pests. Results demonstrate that DL models, particularly a fine-tuned ResNet-50, achieve superior accuracy (94.5%) compared to ML methods (85.2% with SVM). We discuss the implications for smart pest management, including real-time monitoring and reduced pesticide use, while highlighting challenges such as dataset size and computational requirements.