CROP GROWTH PREDICTION USING DEEP LEARNING

Abstract: The agricultural production mainly aims to generate high yield for the crops.Prediction of the crop on a global scale and regional scale is highly important for the agriculture management sector, crop farmers, food trade policies and carbon cycle research. To maintain the high demand and secured level of food chain supply to the people, the prediction of crop yield is a national priority for the government. To get most crop yield at minimum value is one of the primary goals in agriculture. Detecting and dealing with troubles related with crop yield indicators in early stages of the rural field can give benefits in expanded yield and elevated earnings too. By reading weather styles of a specific location, massive-scale meteorological phenomena will have a completely green impact on agricultural production. The crop yield predictions can be utilized by farmers to reduce losses when negative conditions may occur. Also, predictions may be used to maximize crop prediction while there is favourable situation for farming. We are currently developing an automated yield estimation system that optically estimates crop yield in orchards during various stages of growth. Instead of using traditional machine vision, we build on recent advances in support vector machine (SVM) to provide results about crop details with improved accuracy rate.