Image-Based Detection and Classification of Plant Diseases using Deep Learning: State-of-the-Art Review

Plant diseases are assumed one of the primary cause regulating food manufacturing and reducing deficits in crop yield, and it is crucial that plant disease have rapid spotting and identification. Since the demand for food is rising quickly due to population growth, the main issue for any country is plant disease mechanization in agricultural science. Recent developments in deep learning techniques have found use in plant disease recognition, offering a robust tool with exceedingly precise results. Our main goal is to employ deep learning mechanisms to better understand the diseases and how to identify them quickly. In this regard, we analysed 94 publications chosen from the last six years (2016-2022) that show how CNN's philosophy has evolved over this period with various approaches to treating plant diseases. Furthermore, a full description of numerous crops, diseases connected to them, various data sets relating to plant diseases, existing CNN models, and customized CNN architectures is provided. The results of this state-of-the-art review can be implemented to comprehend the cutting-edge trends in the application of Deep Learning (CNNs) to detect plant diseases as well as to pinpoint any research gaps that require the scientific community's attention