Quantile correlative deep feedforward multilayer perceptron for crop yield prediction. (March 2022) - Sivanantham, V.; Sangeetha, V.; Alnuaim, Abeer Ali; Hatamleh, Wesam Atef; Anilkumar, Chunduru; Hatamleh, Ashraf Atef; Sweidan, Dirar
Highlights: Crop yield prediction is the great importance to improve large-scale food production. The quantile regression function is applied to evaluate testing and training data points. The proposed QRECF-DFFMPC techniques are compared to the existing methods. The results demonstrate that the crop yields are correctly predicted with minimum time. An input data from the dataset is sent to the input layer. Abstract: Crop yield prediction is an essential one in agriculture. Crop yield protection is the science and practice of handling plant diseases, weeds, and other pests. Accurate information regarding the crop yield history is essential for making decisions regarding agricultural risk management. Many research studies have been undertaken for identifying crop productivity using various data mining techniques. However, the prediction accuracy of crop yields was not improved with minimum time consumption. To overcome the issues, a novel Quantile Regressive Empirical correlative Functioned Deep FeedForward Multilayer Perceptron Classification (QRECF-DFFMPC) Method is proposed for crop yield prediction. QRECF-DFFMPC Method comprises three layers such as input and output layer with one or more hidden layers. The input layer of deep neural learning receives several features and data from the dataset and then sent it to the hidden layer 1. In that layer, Empirical Orthogonal Function is used to select the relevant features with the help of orthogonal basis functions. After that, Quantile regression is used in the hidden layer 2 to analyze the features and produce the regression value for every data point. Then, the regression value of data points is sent to the output layer for improving the prediction accuracy and reducing the time complexity. Experimental evaluation is carried out on factors such as prediction accuracy, precision, and prediction time for several data points and the number of features. The result shows that the proposed technique enhanced the prediction accuracy and precision by 6% and 9% and reduces the prediction time by 32%, as compared to existing works.