Machine learning based crop, yield and price prediction system for smart agriculture using random forest and weather parameters

This research paper focuses on reducing agricultural losses caused by changing climatic and environmental conditions, which often result in unsuitable crop selection for cultivation on a particular land area. Machine Learning ML is a significant methodology for accomplishing the reasonable and compelling answers for this disadvantage. In India farmers still follow the traditional techno logy which they adopted from their ancestors. But the problem is that in the earliest time climate was very healthy everything happened on time. But now most of the things have been changed due to global warming and many other factors. The leading annoyance with agribusiness in India is the shortage of rainfall in seasonal periods. Humidity is also required for production, though it has been unreasonable, it also transforms as a weakness. Accurateness of harvest price forecasting strategies plays an important function in encouraging market characteristics such as direction and collection. Harvest Yield Prediction technique includes foreseeing yield of the harvest from reachable historical and possible data like climate parameter, soil parameter and yield prediction. Machine learning algorithms are applied and we get best predicted results through a web application. This Project is an attempt of predicting the outcome of harvest supported the current data by using of RFA Random Forest Algorithm and Back Propagation. The expectation can make the farmer to foresee the yield of harvest before developing onto the agribusiness zone.