Crop Prediction System Using Machine Learning


Yash Bhandari (Corresponding Author), Aruna Gawade,
Department of Computer Engineering, D.J.Sanghvi College Of Engineering, Mumbai, India.



One of the most significant professions in India is agriculture. Agriculture is the primary industry for more than half the population. Additionally, the rising suicide rate over time is evident. The weather, family troubles, and debt are the main causes of this. We may also remark that farmers frequently lack knowledge of the crops that would be best for their soil’s quality, nutrition, and rainfall potential. Early identification of the variables causing a fall in output can be aided by previous crop yield predictions. Additionally, it may help with the right application of fertiliser and pesticides, the selection of the best crop kinds, and early forecasting, which offers farmers the opportunity to make advance preparations for storage and selling. Additionally, they lack sophisticated methods for predicting crop output at the time of seeding. This approach is suggested to forecast soil fertility using a decision tree. The proposed method primarily focuses on evaluating soil fertility and rainfall amounts to forecast the most suited crop and to suggest viable alternatives for cultivation that can boost farmers’ output.