Enhancing crop health using deep learning techniques

Farmers have faced several difficulties as a result of the traditional techniques to identify leaf diseases, which have led to significant losses. Thankfully, technological developments have allowed us to effectively create leaf disease monitoring devices that help with early disease diagnosis. This paper investigates the realm of crop pest as they cause significant economic, social and environmental losses globally, making precise identification crucial for pest control in agriculture. Effective pest and disease management requires accurate and timely detection and classification of these pests and diseases. Deep learning techniques, especially convolutional neural networks (CNNs) and YOLOv8 algorithms, have emerged as highly accurate and efficient tools for image-based pest and disease detection. Using CNN and YOLOv8 algorithms, this project aims to create a robust and efficient crop pest detection and classification system. To accomplish this goal, a large dataset of labelled images of various crop pests and diseases will be collected and preprocessed. Following that, using the prepared dataset, a CNN architecture will be designed and trained with the goal of extracting and analyzing key features from pest and disease images. Furthermore, the YOLOv8 object detection algorithm will be used to detect and classify pests and diseases in images. The trained CNN and YOLOv8 models will be integrated into an easy-to-use interface that will allow farmers and agricultural professionals to detect and identify crop pests and diseases in their fields. The proposed system's performance will be measured using metrics such as accuracy, precision, recall, and mean average precision (mAP). The successful development of this crop pest detection and classification system will provide farmers with a valuable tool for timely and accurate pest and disease identification, ultimately contributing to improved crop health, yields, and agricultural productivity.