M2E2Net model: Multimodal fusion for real-time estimation of daily maize evapotranspiration

Efficient irrigation is critical for improving agricultural water-use efficiency and crop productivity. Accurate estimation of crop evapotranspiration (ETc), a key parameter for scientific irrigation scheduling, remains challenging due to the complex coupling of external meteorological conditions and dynamic crop growth status. Single-modality data sources are insufficient to fully capture these interactions, thereby limiting estimation accuracy. To address this challenge, this study proposes M2E2Net, a multimodal deep learning model designed for high-precision, real-time daily maize ETc estimation. The model integrates smartphone-captured canopy RGB images as the visual modality, employing EfficientNet-B3 combined with Transformer to extract deep spatial and global features that accurately represent temporal crop growth dynamics. A tabular modality is constructed from RGB indices, canopy coverage, and publicly available meteorological parameters, encoded via grouped multilayer perceptrons (MLPs) to capture crop–environment interaction patterns. Cross-modal attention mechanisms are introduced to enhance semantic interaction between the two modalities, enabling dynamic matching of maize water demand variations. Validated against large-scale weighing lysimeter measurements, M2E2Net achieved an R 2 of 0.8893, representing a 47.8% improvement over the conventional FAO 56 crop coefficient method. With an inference time of only 35–56 ms and lightweight design, the model is fully compatible with mobile deployment. Experiments further identified 09:00 as the optimal single-shot acquisition time. The proposed approach provides smallholder farmers with a low-cost, user-friendly, and mobile ETc monitoring solution without reliance on expensive equipment, offering substantial support for agricultural water conservation and precision irrigation.