Climate-Resilient Crop Yield Forecasting Using Deep Learning

Abstract

Crop yield forecasting requires development of superior predictive systems because global temperature changes and unpredictable rainfall patterns create challenges for agriculture-based economies. The operational requirements of traditional biophysical crop models create excessive costs because they need specific calibration for each study area while classical statistical methods fail to work with the unpredictable climatic conditions that result from rapid atmospheric temperature increases. We present a deep learning system build through modular design which achieves ethical standards by using Temporal Convolutional Networks (TCN) to extract short-term weather data and attention-based Bidirectional Long Short-Term Memory (Bi-LSTM) networks to maintain long-term information and Residual Convolutional Neural Networks (ResCNN) to model the spatial characteristics of different agro-climatic patterns. The proposed system processes simulated climate conditions which correspond to standard Intergovernmental Panel on Climate Change (IPCC) atmospheric warming pathways and system demonstrates its effectiveness through Root Mean Square Error (RMSE) measurement of 4.19q/ha, MAE of 2.73 q/ha, and an R-squared of 0.9056. which surpasses both single Bi-LSTM baseline systems and common machine learning ensemble methods by 14 percent. This study creates a complete model safety system which handles algorithm bias across different agricultural regions and protects farm-level data through cryptographic methods and enables accurate uncertainty estimation for implementing agricultural policies in real-world situations.