Optimized Deep Learning Methods for Crop Yield Prediction

Crop yield has been predicted using environmental, land, water, and crop characteristics in a prospective research design. When it comes to predicting production, there are number of factors consider, including weather conditions, soil qualities, water levels the location farm. A broad variety algorithms based on deep learning used extract useful crops for forecasting. The combination data mining creates whole prediction system that is able connect raw yields. suggested study uses Discrete Deep belief network with Visual Geometry Group (VGG) Net classification method over tweak chick swarm optimization approach estimate agricultural production. Network’s successively stacked layers were fed parameters. Based input parameters, production environment constructed architecture. Using technique, best preprocessed, optimal output as process. classifier classify forecast model correctly predicts 97 percent accuracy, exceeding existing models by maintaining baseline distribution.