Design of crop yield prediction model using machine learning and smart irrigation system
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Agriculture is one of the strong pillars of Uganda’s economy. Considering that very few technologies exist to aid the farmers in selecting the right crops depending on the environmental factors. Moreover, most irrigation systems all around the world require at least some form of human intervention. Machine learning can bring a boom in the agriculture field by changing the income scenario through growing the optimum crop as well as automating the irrigation system. This project focuses on predicting the yield of the crop by applying various machine learning techniques. The outcome of these techniques is compared on the basis of mean absolute error, mean squared error and R2. The prediction made by machine learning algorithms will help the farmers to decide which crop to grow to get the maximum yield by considering factors like temperature, rainfall, area, etc. The project also focuses on designing a control circuit to switch the D.C pump.