Agriculture and its related industries are without a doubt the most important sources of income in India.
Furthermore, the agriculture sector makes a significant contribution to the country's GDP (GDP). The vastness
of the rural area is a gift to the country.. In any event, the harvest yield per hectare is appallingly low in
comparison to international standards. This could be one of the reasons for a higher rate of self-destruction
among India's periphery ranchers. For ranchers, this examine provides a sensible and easy-to-apprehend yield
expectation framework. The suggested framework provides ranchers with a network through a flexible
application. The use of GPS aids in the identification of the client's location. As information, the client provides
the region and soil type. AI computations permit for the choice of the maximum nice harvest listing or the
prediction of harvest yield for a client-decided on crop. Machine Learning computations such as Support Vector
Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), Multivariate Linear Regression (MLR),
and K-Nearest Neighbor (KNN) are used to predict crop production. Among these, the Random Forest produced
the best results, with a precision of 95%. In addition, the framework suggests the optimal time to use composts
to aid increase production.
Software And Hardware