Expert System for Yield Prediction

Abstract

The ability of expert system technology to be used for the approximation and prediction of crop yields at rural district and federal state scales in different climate zones based on reported daily weather data. This research is to develop a farmer prediction system to identify crop suitable for particular soil. Neural Network should be trained to perform correct prediction for farmers.Previous research has established that large-scale climatological phenomena influence local weather conditions in various parts of the world. These weather conditions have a direct effect on crop yield. Consequently, much research has been done exploring the connections between large-scale climatological phenomena and crop yield. Artificial neural networks have been demonstrated to be powerful tools for modeling and prediction, to increase their effectiveness. In this research work Crop prediction methodology is used to predict the suitable crop by sensing various parameter of soil and also parameter related to atmosphere. Parameters like type of soil, PH, nitrogen, phosphate, potassium, organic carbon, calcium, magnesium, Sulphur, manganese, copper, iron, depth, temperature, rainfall, humidity. For this purpose, we are going to proposed this system based on back propagation feed forward neural networks was best suited for effective crop prediction.