VGG16-PotatoGuard: A Deep Learning Approach to Detecting Leaf Diseases in Potatoes

Potato cultivation plays a pivotal and exceedingly significant role in global agriculture, serving as an indispensable staple food source for millions of people around the world. Nevertheless, the sustained well-being of potato crops faces incessant threats from a myriad of diseases, exerting a pronounced and adverse influence on both yield and quality. In recent years, the domain of machine learning, particularly the realm of deep learning, has emerged as a formidable arsenal of tools for the early identification and diagnosis of potato leaf diseases, simultaneously ensuring a remarkable level of precision and accuracy in the executed model. This comprehensive review paper undertakes the task of presenting a thorough and all-encompassing overview of the state-of-the-art within the sphere of machine learning applications specifically designed for the classification of Potato Leaf Disease (PLD). While a multitude of models may be conceivably deployed for this purpose, this review paper meticulously directs its focus toward the exploration and examination of deep learning-based projects. The extensive research conducted in this domain, albeit following various trajectories, collectively aids in arriving at a resounding consensus on the development of an efficacious model and a highly effective technique to combat and mitigate such devastating diseases that cast a shadow on the health and vitality of the potato plant. All of these meticulously considered steps are meticulously examined and weighed in the tapestry of research endeavors.