Enhanced wheat yield prediction through integrated climate and satellite data using advanced AI techniques

Wheat plays a vital role in Pakistan's economy and food security, making accurate yield forecasting essential for planning and resource management. Traditional approaches-such as manual field surveys and remote sensing-have been widely used, but their effectiveness in capturing yield variation across different growth stages remains uncertain. This study explores a multi-phase approach to wheat yield prediction by dividing the crop cycle into four key stages and analyzing data from 2017 to 2022. Using the Google Earth Engine platform, the analysis integrates satellite imagery, seasonal weather variables, and soil information. A range of machine learning and deep learning models were tested to assess their predictive performance. The results show that combining diverse data sources with advanced AI techniques significantly improves prediction accuracy, with model performance reaching R² values between 0.4 and 0.88. Among the tested models, those capable of capturing spatial and temporal patterns reduced prediction errors most effectively. These findings demonstrate the value of integrating environmental data and AI methods for enhancing crop yield forecasting across complex agricultural regions. This study presents a comparative analysis of AI-based approaches against conventional methods, demonstrating their superiority in three key areas: (1) improved computational efficiency through optimized learning architectures, (2) enhanced spatial generalizability by capturing complex, nonlinear patterns, and (3) greater prediction stability under varying conditions. Our findings underscore the transformative potential of AI models, offering a more reliable and scalable alternative for predictive tasks in our framework. This research offers a robust framework for forecasting wheat yields, aiding decision-makers in improving food security and agricultural planning.