Machine Learning Techniques for Potato (Solanum Tuberosum) Leaf Diseases Detection and Classification

Development in agriculture must be given top priority in a nation like India, where the bulk of the population makes their living from it. A significant financial loss in crop production occurs every season due to maladies and pests. One of the biggest problems that farmers deal with is diseases that infect plant leaves, especially those of potatoes. A popular crop in agriculture, potatoes are grown in more than 150 countries and will produce 376 million tons globally in 2021. Even with this exposure, the output of potatoes is greatly decreased by potato leaf diseases. Delivering the amount of food required to feed the world's expanding population is therefore quite difficult. In the agriculture industry, potato diseases have resulted in production losses and financial losses. It will also have an impact on farmers' incomes, as many farmers now take their own lives due to misfortunes they encounter in the field. Planned disease control measures can be aided by the identification of specific plant diseases. In potato crops, leaf disease detection and classification are also mitigated by computer technologies. To ensure food security for the world's growing population, this study offers a comprehensive overview of the many leaf diseases that affect potato crops. It also highlights the use of scientific and computation methods in the identification and categorization of potato leaf disease, as well as the ongoing challenges associated with it.