Published Paper Details:

Precision agriculture is an emerging area that applies modern information technology and machine learning to create news ways of identifying and diagnosing plant diseases to promote sustainable farming practices. This paper aims to review the application of machine learning and deep learning techniques in plant disease detection and classification in precision agriculture. It also proposes a different approach in classifying relevant literature which is based on the employed methodology - classification or object detection, and reviews the literature on datasets available for plant disease detection and classification. This work comprises a comprehensive analysis within the scope of object detection and classification of plant diseases utilising the PlantDoc dataset. The conclusion reached in this research is that YOLOV5 is the best object detection algorithm and that ResNet50 and MobileNetv2 models are the best image classifier models relative to the time cost of training the models and the accuracy of produced images.