Crop Yield Using Prediction Using Deep Neural Networks
Keywords:
crop yiled forecasting, discrete wavelet transform, deep neural networks, mean absolute percentage error, accuracyAbstract
Crop yield prediction is a critically important forecasting problem trying to address food security in the world. This is a crucial AI based application where the previous crop yield is used to train a machine learning model and then the future crop yield is forecasted. The results can be used to decide, which crop to be sown during a particular season in a particular geographical location. Since the data to be analyzed is large, random and complex for analysis, hence conventional statistical techniques do not render high accuracy of prediction. In this work, data filtering using the discrete wavelet transform (DWT) has been adopted prior to training using a back propagation based deep neural network. The performance of the system has been evaluated in terms of the mean square error, mean absolute percentage error, regression and accuracy. It has been shown that the proposed system achieves and accuracy of 98.39% and outperforms the previous system in terms of accuracy of prediction
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