Grape Leaf Disease Recognition: A Deep Learning and Machine Learning Techniques Overview

Several approaches have been proposed in the published and existing scientific literature to assist farmers in decreasing the possibility of human error, but these approaches are expensive to execute since they rely on sensors and image-capturing equipment. Machine Learning’s (ML) subfield of Deep Learning (DL) has changed the course of study toward how to classify epidemics. DL systems have very great potential for accurately identifying grape leaf diseases. In this work, different existing approaches that accurately identify grape leaf diseases were studied and analyzed. The biggest challenge of the current research is to examine and evaluate ML and DL methodologies that can effectively analyze grape plant diseases using leaf images. Leaves are considered reliable indicators of plant health, and this research aims to address the urgent national concern of enhancing agricultural productivity. The emergence of computerized identification and categorization of disease technologies for grape trees is still in its infancy, according to analysts. Farmers need new, better systems for identifying and classifying grape plant leaf ailments to avoid financial catastrophe and increase fruit harvest. This study presents a thorough systematic analysis of DL and ML techniques for identifying grape leaf disease. This study is expected to be useful for the field of agricultural disease research in the early detection and management of illnesses affecting grape leaves.