CROP YIELD PREDICTION USING MACHINE LEARNING ALGORITHMS
Raghavendra Rao
Abstract
India is an absolute agriculture country whose economy is mainly depends on agriculture yield growth which have been essential to feed a growing population while reducing the environmental impact of food production at the same time. The impact of climate change for most of the agriculture crops affects the yield. It attempts to solve the issue by building a prototype of an interactive predictive system which predicts the yield of certain suitable crops. The primitive aim of this study is to come up with an efficient crop yield prediction system using Kalman filter algorithm. A farmer can put any verity of crop in his field, but there can be no clarity of how much yield he might get and what’s the maximum profit he can gain. Technology can help them to predict the yield of the crop considering various factors whether to go for that crop or not. Crop yield is the proportion of harvest delivered per area of land. It's a significant measurement to comprehend in light of the fact that it assists us with figuring out food security and furthermore makes sense of why your potatoes can cost more one year and afterward less the next year. In this project, we will study different methods that can be used to predict the crop yield. This study shows that they can also be done using Machine Learning algorithms known as Kalman Filter Algorithm and Random Forest Algorithm. In this Project, we compare various Machine Learning algorithms version and different Data processing techniques to obtain best possible results in crop yield prediction. To get the highly accurate percentage to predict yield is by using Kalman filter and Random Forest Algorithm.