Potato Disease Detection Using Machine Learning

Production within the agricultural sector was a major factor in the Indian economy. It increased the GDP of our nation by over 17–18%. In India, farming is among the most common profession. The economy of our nation is heavily reliant on farming and the goods that are produced there. With the largest net cropped territory in the entire world, India was ranked #1, next to the US and China. Another of the main crops is the potato. Throughout the past few years, potato farming has become increasingly popular. However, several illnesses are impeding potato output, raising the cost of potato cultivation for farmers. Nonetheless, a few potato illnesses are impeding potato yield, raising farmers' expenses. These machine-learning techniques are highly helpful for quickly monitoring huge fields. In this study, we employed potato leaves to identify diseasecausing factors such as late and early-onset blight, as well as to determine the region of the leaf that was affected. Our primary objective is to use cutting-edge machine learning technology to identify potato infection from leaf photos. To identify leaf diseases and attain up to 95.0% efficiency for the machine learning approach, we are going to use a hybrid model that combines SVM and Decision Trees.