A novel approach for efficient crop yield prediction

Abstract

Crop yield prediction is one of the challenging task in agricultural domain. Extensive research in agricultural domain has been carried out to predict better crop yield using the machine learning algorithm Artificial Neural Network (ANN) and statistical model Multiple Linear Regression (MLR). This article examines the intrinsic relationship between MLR and ANN. A hybrid MLR-ANN model has been proposed in this research work for efficient crop yield prediction. The proposed hybrid model is modeled to analyze the prediction accuracy when MLR intercept and coefficients were applied to initialize the ANN’s input layer weights and bias. Feed Forward Artificial Neural Network with Back Propagation training algorithm was used for predicting accurate paddy crop yield. In conventional ANN model, the weights and bias of input and hidden layer are initilized randomly. This hybrid MLR-ANN model, instead of random weights and bias initialization, the input layer weights and bias are initialized by using MLR’s coefficients and bias. The hybrid model prediction accuracy is compared with ANN, MLR, Support Vector Regression (SVR), k-Nearest Neighbour (KNN) and Random Forest (RF) models by using performance metrics. The computational time for both hybrid MLR-ANN and conventional ANN was calculated. The results show that the proposed hybrid MLR-ANN model gives better accuracy than the conventional models.