Machine Learning Approaches for Crop Suitability Assessment and Disease Prediction

Abstract

A sizable portion of farmers, especially in India, lack the expertise needed to choose crops and apply fertilizer in an intelligent manner. While machine learning algorithms have achieved significant strides in automating the identification of diseases, several problems have prevented Deep Learning from reaching its full potential. These include the need for high-quality training data, processing power limitations, and the models’ restricted generalizability, all of which make application of the models difficult. An accessible and open-source web application has been created to tackle these problems and maybe improve agricultural yield