Classifying pests in crop images using deep learning

Abstract

Pest control is essential for agricultural success, and rapid and accurate pest identification through computer vision and machine learning enables effective pest management. This paper proposes an approach to evaluate nine customizations of the IP102 dataset. Considering the extensive range of sub-datasets, a comparative analysis was conducted between different deep learning models, including ResNet and AlexNet Convolutional Neural Networks (CNNs), and Vision Transform (ViT). We carried out tests considering training from scratch and fine-tuning. Our experimental results demonstrate that ViT outperforms CNN models for the problem investigated and benefits significantly from data augmentation strategies. Our study provides valuable insights for efficient pest classification, paving the way for future research and advancements in precision agriculture.