Deep Learning for Climate-Smart Agriculture: Predicting and Mitigating Climate Impacts on Crop Production

Abstract

Climate change has become a major threat to global agricultural productivity owing to increasing temperature variability, irregular rainfall patterns, and frequent extreme weather events. Climate- Smart Agriculture (CSA) aims to enhance agricultural resilience, improve productivity, and ensure environmental sustainability. Recent advances in deep learning (DL) have provided powerful tools for analyzing complex, multi-dimensional agricultural and climate data. This study proposes a hybrid deep learning framework that integrates convolutional neural networks (CNN), long short-term memory (LSTM) networks, and transformer-based attention mechanisms to predict crop yield under climate stress and support mitigation strategies. Multi-source datasets, including meteorological data, satellite imagery, soil parameters, and historical crop yield records, were utilized. The experimental results demonstrate that the proposed model significantly outperforms traditional machine learning and standalone deep learning approaches, achieving improved accuracy and robustness. These findings highlight the potential of deep learning-based CSA systems to support sustainable and climate-resilient agricultural practices.