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.
₹10000 (INR)
NON IEEE -2022