Sell Quality determination for Chemical Fertilizer Data using Expert System
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Abstract
There is an growing appetite for food due to the ever increasing global population, so new technologies need to be created to increase crop yield. This paper proposes an intelligent way of forecasting crop yield and recommends the best variables for optimising crop yield. With technical developments, the emphasis has now moved to the use of computers and control systems for process management and efficiency enhancement. we estimate the crop yield per acre, in this research work to proposed hybrid approach for Chemical Fertilizer Data classification using SVM and neural network approach with expert system improvement. Yield and data obtained from Madhya Pradesh of Agriculture are used in the proposed process. Humidity, yield, temperature and rainfall are the different parameters used in the dataset.
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