• Sandeep V. Rode

After considering climatologically phenomena, that leads to affecting local weather conditions in several regions of the world. Those weather conditions are directly correlated to crop yield. Hence, various researchers demonstrate there are connections among crop yield and large-scale climatologically phenomena. In this paper, we have made the use of artificial neural networks (ANN) to demonstrate the crop yield prediction on the basis of algorithmically tested soil nutrients. We have made the use of powerful tools like MATLAB for modelling and prediction, to optimize their effectiveness. Basically, soil nutrients such as nitrogen (N), phosphate (P), potassium (K) are obtained from an algorithm. Other parameters such as organic carbon (OC), calcium (Ca), magnesium (Mg), sulphur (S), manganese (Mz), copper (Cu), iron (Fe), depth, temperature, rainfall, humidity are obtained from lab analysis. Finally, the parameters from the algorithm and from lab analysis are supposed to apply for prediction of the suitable crop in the given geographic area.

ANN, Lab Analysis, Crop Yield Prediction.

"AGRICULTURAL CROP YIELD PREDICTION MODEL BASED ON MULTISPECTRAL DATA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.731-734, March-2019, Available :http://www.jetir.org/papers/JETIR1903400.pdf

"AGRICULTURAL CROP YIELD PREDICTION MODEL BASED ON MULTISPECTRAL DATA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp731-734, March-2019, Available at : http://www.jetir.org/papers/JETIR1903400.pdf