Spatial Mapping of Biomass and Yield of Rice-Wheat Cropping Systems across Different Irrigation Methods Using UAV Images and Machine Learning Algorithms

Irrigation water management is a critical factor that influences crop biomass, yield, and water usage, since irrigation makes the crop development independent of rainfall. Poor irrigation management can result in many problems on the farm and off the farm, such as waterlogging, erosion, and non-point source pollution. Therefore, improving irrigation water-use-efficiency is essential to reduce the amount of water needed without penalizing the yields. Considering the growing competition for water resources, there is a need to explore novel methods for quantifying and enhancing water use efficiency in irrigated fields, such as Unmanned Aerial Vehicle (UAV)-based remote sensing. This study integrates UAV-derived vegetation indices with machine-learning (ML) algorithms to quantify biomass and yield response of rice under alternate wetting and drying (AWD) and wheat under different irrigation methods (drip, sprinkler, and flood) with variable rates of crop evapotranspiration (100%, 75%, 50% and 0% rainfed treatment) across two seasons of the rice-wheat cropping system in Roorkee, India. The biomass and yield results obtained from the different ML algorithms were compared. During the training process of the ensemble random forest model, it performed better with a higher KGE (0.91) and a lower value of NRMSE (0.033), and a minimal PBIAS of 0.13%. The ensemble random forest model performed better during the testing process of the rice yield estimation (R² = 0.60, KGE = 0.71, PBIAS = −2.26%, NRMSE = 0.136). For wheat yield estimation, training results were similar with strong model performance (R² = 0.8137, KGE = 0.83, PBIAS = 1.36%, NRMSE = 0.470). The UAV-ML workflow captured both the fine-scale spatial variability needed for site-specific field decisions and the process understanding needed for generalization across the seasons. This integrated workflow supports the UN Sustainable Development Goals (SDGs), specifically SDG 2 (Zero Hunger) and SDG 6 (Clean Water and Sanitation).