Approach towards Weather Prediction Model for Aggrotech

Agriculture is indispensably responsible for the growth of India, as it contributes not only extensively to employment but also aids GDP. This primary sector mainly suffers the adversities due to types and level of nutrition in the soil, weather conditions, and various other factors. The datasets available for crop yield according to the state, daily weather predictions, and annual rainfall could help better the agricultural output. Declassification of datasets has led to the absence of knowledge, and perhaps, could not solve the problem of improving crop yield. Thus, as a solution, choosing suitable crops in alignment with the weather will result in harvesting at the right time. The objective is to collaborate technology with agriculture, to solve the problem or issue of lesser crop yield due to lesser availability of knowledge regarding rainfall and agriculture. Predicting the rainfall conditions through a regression model for crops according to their states and the respective rainfall data will solve the irrigation-related issues. The proposed regression model examines extensively available data to predict the quarterly rainfall which the farmers can then use to get better agricultural output.