Machine Learning Models Based Crop Recommendation and Plant Disease Identification for Real Time Application

The production of crops contributes significantly to the economic health of every country. One of the most crucial elements of maintaining a country with a developed agricultural sector is plant disease identification. An agricultural sector that is healthy and productive and that doesn't waste money or other resources requires the quick and effective diagnosis of plant diseases. Various useful technologies to solve global challenges have been developed with the aid of machine learning. The use of several ML algorithms can help solve the agriculture problem. The creation of a website that incorporates both a crop recommendation system and a plant disease identification system is one of the two goals of this essay. The datasets might be accessed by anyone online. The dataset is trained once the characteristics required for Task 1 are retrieved using five distinct algorithms: logistic regression, decision tree, support vector machine (SVM), multilayer perceptron, and random forest. In suggested machine learning models, random forest exceeds all other techniques, achieving an accuracy rate of 99.92%.