Predictive Modeling of Crop Yield Using Deep Learning Based Transformer with Climate Change Effects

Abstract

Climate change is a significant global challenge concerning agriculture and food security. The understanding of climate change effects on crop production is necessary for developing an effective adaptation strategies and predicting a crop yield accurately. This paper suggests the combined Clustering Long Short Term Memory Transformer (CLSTMT) model for crop yield prediction. CLSTMT is a hybrid model that integrates clustering, deep learning based LSTM and Transformer techniques. The outliers from the historical crop and climate data are removed using k-means clustering. Followed by, the crop yield is predicted using Transformer-based neural network with LSTM layers and feed-forward neural network (FNN) components. The model design effectively captures climate-influenced patterns, enhances the precision and comprehensiveness of crop yield prediction. The experiment is conducted using the dataset with crop yield, climate, and pesticide details over 101 countries collected from 1990 to 2013. The comparative analysis reveals that the CLSTMT model outperforms other regression models such as SGDRegressor (SGDR), Lasso Regression (LR), Support Vector Regression (SVR), ElasticNet (EN) and Ridge Regression (RR). The proposed design effectively captures climate-influenced patterns, enhancing the precision and comprehensiveness of crop yield predictions. The findings indicate that the proposed model provides an accurate prediction of crop yield with high R2 of 0.951 and lesser Mean Absolute Percentage Error (MAPE) of 0.195. This value suggests a minimal average percentage deviation between the actual and predicted yields. The findings indicate that the CLSTMT model provides more accurate crop yield prediction compared to others.