Yield2ActionAI for calibrated multimodal crop yield forecasting and uncertainty aware resource optimization in precision farming

The research problem is the increasing demand for smart precision agriculture systems that can enhance crop yields and resource efficiency in fluctuating environments. Current approaches to machine learning and deep learning have shown high predictive qualities in using remote sensing and weather imagery, but typically concentrate on accuracy, but not on prediction reliability, uncertainty estimation and translating intentions on what to do with predictions to actual actions. This restricts their usage in real-life farm situations where decision-making that results in risk management is necessary. In order to address these constraints, this article suggests Yield2ActionAI, a two-step multimodal deep learning system, which combines the forecast of yields with the optimisation of resources under uncertainty, based on the core philosophy of Yield2ActionAI. The former makes use of spatio-temporal multi-modal learning to combine both MODIS NDVI/EVI and ERA5-Land weather data to predict the yield correctly, and this is then followed by a calibration module that makes sure that confidence is well-estimated. The second step involves using a multi-objective measurement algorithm (NSGA-II) to produce the best irrigation and fertiliser plans through the equilibrium of the yield maximisation, minimal cost and reduction of risk. Experimental findings show that the proposed model provides better performance (with a decreased RMSE of 0.49), a lower MAE (0.38), and a higher R2 (0.91) than baseline models. The optimisation phase, too, results in a matter of 10–12% in resource savings and 9–11% yield. The suggested framework offers a practical, scalable approach to precision farming that enables effective, evidence-based agricultural decision-making.