Insight generation for improving e-governance: towards crop yield prediction model

Abstract

ABSTRACT Agriculture is one crucial industry that expects to profit from these improvements in maintaining financial stability and availability of food. Accurate agricultural output prediction is critical for efficient policy formation, allocation of resources, and preparedness for catastrophes. This work intends to propose a novel insight generation for improving e-Governance towards crop yield prediction model. In the first stage, the data is preprocessed by conducting improved data normalisation and data augmentation. The outlier handling, custom weighted Min-Max scaling and using a custom scaling range are the steps to be followed in the improved data normalisation. Then the augmented data is subjected to a second stage called feature extraction. From the augmented data, the raw feature, Unbiased Estimator based Correlation (UEC) feature, correlation feature, and statistical features are extracted. In the UEC-based feature, the unbiased estimator is deployed to estimate outliers and sparse data. These extracted features are fed to a hybrid DL-based prediction model, wherein it contains Bidirectional Long Short Term Memory (Bi-LSTM) and Shifted Beamish Activation-Deep Convolutional Neural Network (SBA-DCNN) models. The SBA activation function is employed in SBA-DCNN at each convolutional layer. By taking the mean of both models, this hybrid prediction model accurately predicts the yield.