Precision Agriculture and Object Detection: Deep Learning Models for Crop Disease Management

Abstract

This study presents a comprehensive dataset designed for the visual detection of crop diseases, comprising 43,267 images of 12 crop species across 15 disease classes. The dataset was developed over 14 months of dedicated human effort. To evaluate its effectiveness, several plant disease detection and classification algorithms were tested. The models generated by these algorithms were deployed on mobile devices and specialized hardware, enabling practical applications ranging from drones to Android smartphones, with on-device detection capabilities.

The results highlight the performance of deep learning techniques, with the YOLOv4 algorithm achieving a mean average precision (mAP) of 71.04%, while the VGG model attained 92% precision and 90% accuracy. These findings demonstrate the potential of deep learning in enhancing crop monitoring, offering significant support for pest and disease control in vegetable crops. This work underscores the role of advanced technologies in promoting sustainable agricultural practices.