Plant Diseases Detection Using Machine Learning and Image Processing: Review

An agricultural productivity boost that is sustainable requires the early and accurate identification of plant diseases. Traditionally, human experts have been used to diagnose plant diseases. But it is expensive, time consuming, and in some cases impractical. Currently, machine learning (ML) and deep learning (DL) methods based on image processing techniques have been introduced to classify leaf lesions caused by diseases. Thus, research applying image processing and machine learning techniques for plant diseases has become a hot research topic. In this report, we propose a review of a set of studies carried out in the field of plant disease detection using machine learning and image processing techniques such as segmentation and feature extraction. The aim of this review is to provide useful knowledge and recommendations to researchers and practitioners to develop reliable and efficient methods for detecting plant diseases by processing images using deep learning techniques in particular. To this end, we have conducted an empirical evaluation of select educational curricula focused on the detection of plant diseases.