ABSTRACT
The agriculture industry significantly contributes to economic and population growth through the provision of high-quality food. Plant diseases decrease crop yields, which directly influences food production systems and leads to economic losses. Insufficient diagnosis and treatment of plant diseases can significantly affect agricultural productivity. Accurate identification is essential in order to prevent a severe decline in agricultural yield. Automatic detection models are crucial for accurate plant disease prediction and effective agricultural disease management. Deep Learning (DL) models are in great demand right now for identifying plant diseases because of how well they can anticipate and improve farmers’ crop yields. This study examines various DL algorithms for visual data-based plant disease detection and classification in great detail. Firstly, a brief overview is provided on various plant disease identification and classification systems developed by researchers, which utilize DL algorithms. Subsequently, a comprehensive analysis is undertaken to get deeper insights into the constraints of current algorithms and propose an innovative methodology for categorizing various forms of plant diseases, thereby contributing to the enhancement of agricultural output.
