Precision Agriculture: ML and DL-Based Detection and Classification of Agricultural Pests

Abstract

Precision agriculture has become a vital strategy in modern farming, leveraging advanced technologies to enhance crop productivity and sustainability. One critical aspect of precision agriculture is the timely and accurate detection and classification of agricultural pests, which significantly impact crop health and yield. This study examines the application of machine learning (ML) and deep learning (DL) techniques, particularly convolutional neural networks (CNNs), for detecting and classifying agricultural pests. This research presents a comprehensive approach that utilizes CNN-based models to identify and categorize various pest species from images captured of farm fields. The methodology involves collecting and annotating a diverse dataset comprising images of multiple pest species and non-pest objects to ensure robust model training and validation. The CNN architecture is designed to extract intricate features from the images, enabling the model to differentiate between pest and non-pest instances effectively.