TAM-Net: A deep network combining tabular diffusion algorithm, attention mechanism, and multi-task learning for monitoring crop water status from UAV multi-source images

Abstract

Rapid and accurate monitoring of crop water status is essential for ensuring sustainable agricultural development and food security. Crops exhibit a complex set of growth and physiological responses under water deficit. Existing studies primarily focused on the monitoring of phenotypic parameters, while the physiological indicators highly relevant to crop water status were ignored. In this context, we aimed to develop a novel model to comprehensively and accurately monitor maize water status using multi-source UAV data and multiple growth and physiological indicators. We first composed the original dataset, including feature variables based on multi-source UAV data (spectral indices, texture indices, thermal indices, and structural indices) and prediction variables based on field measurements (equivalent water thickness, stomatal conductance, transpiration rate, and actual photochemical efficiency) in 2023 and 2024. Next, the tabular denoising diffusion probabilistic model (TabDDPM) was employed for synthesizing new samples to adequately train the models. Then, a deep learning network named TAM-Net, with the hybrid attention mechanism and multi-task learning, was trained on the synthetic dataset. Finally, the fuzzy comprehensive water index (FCWI) considering uncertainty and variability was obtained in 2023–2024. The results indicated that multi-source data significantly improved the model performance, with the R² of 0.52–0.63, and NRMSE of 27.89 %–29.86 %. TabDDPM was able to synthesize new datasets with high similarity and effectiveness. TAM-Net achieved the highest monitoring accuracy for the four indicators of crop water status (R² of 0.76–0.90, NRMSE of 12.92 %–22.95 %). FCWI effectively assessed the water status across different treatments. Overall, TAM-Net was demonstrated with powerful performance for monitoring maize water status, which has potential in supporting precision irrigation practices.

Plant phenotyping relevance

UAVマルチソース画像からトウモロコシの水分状態・生理形質を推定するTAM-Netを開発し、精度評価まで行っており、表現型取得・推定手法が研究の中心である。

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