Comprehensive Neural Network Techniques Application in Wheat Yield Prediction
نویسندگان
چکیده
Agricultural sector area plays major role in Indian economy. This paper shows research comparison in between MLP Feed Forward Neural Network, Generalized Regression Neural Network and Radial-Basis Function Neural Network in the field of Wheat yield prediction using Z-score Normalization method. The outcome represents that GRNN present better prediction results as compared to FFNN and RBNN. Eight different parameters are used in all these models which effect the wheat yield production like Area, Rainfall, Temperature, Seed Distribution, Fertilizer (P, N, and K) and Minimum selling price (MSP). GRNN presents better filtered result for next six years for wheat yield by applying varying input parameter vectors.
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