MResGat: Multi-head Residual Dilated Convolution Assisted Gated Unit Framework for Crop Yield Prediction

Abstract

The importance of predicting crop yields has increased due to growing concerns of surrounding food security. Early forecasting of crop yields holds a pivotal role in avoiding starvations by estimating the food supply available for the expanding global population. Several Deep Learning (DL) and Machine Learning (ML) techniques are involved to develop effective and accurate crop yield prediction model. Nevertheless, existing models faces some limitations such as less accuracy, high error rate because of noisy data, high training time and extracted less effective features for prediction. To overcome these issues, the novel DL methodology is introduced for attaining high accurate crop yield prediction. Initially, the soil, weather and other resources big data are collected from the various agriculture field. In data collection phase, the input data of larger size are stored in the Hadoop platform for the purpose of storing as well as processing the entire data in a distributed manner. The data are pre-processed through the utilization of Missing value imputation and z-score based data normalization. From the pre-processed data, the optimal features are considered using Integrated Correlation Random recursive elimination (InCorRe) approach. Based on the previous soil and weather information, the suitable yield of crops can be predicted using Multi head Residual dilated convolution assisted gated unit (MResGat) model. Finally, the losses of the network model can be optimized using African vulture optimization algorithm (AVO). The proposed method is evaluated using the several performance metrics, which achieved 0.023% of MSE value and 0.036% of MAE values.