Insect Pest Identification Using Convolutional Neural Network (CNN) Techniques for Precision Agriculture

Abstract

Accurate and timely identification of insect pests is a critical component of effective pest management in agricultural systems. Traditional pest identification methods rely heavily on expert knowledge and manual inspection, which are time-consuming, labor-intensive, and prone to human error. Recent advances in artificial intelligence, particularly deep learning techniques such as Convolutional Neural Networks (CNNs), have shown great potential in automating insect pest identification with high accuracy. This study explores the application of CNN-based image classification models for identifying insect pest species in agroecosystems. High-resolution images of major agricultural pests were used to train and validate CNN architectures for feature extraction and classification. The results demonstrate that CNN models can effectively distinguish insect pests based on morphological characteristics such as shape, color, texture, and wing patterns. The integration of CNN-based pest identification systems can support real-time decision-making, reduce dependency on chemical pesticides, and enhance precision agriculture practices. This approach represents a promising tool for sustainable pest management and digital agriculture.