Water stress limits wheat growth and the yield on rain-fed sodic soils. Appropriate selection of traits and novel methods are required to forecast yield and to identify water stress tolerant wheat genotypes on sodic soils. In this study, we proposed a thermal remote sensing and machine learning-based approach to help predict the biomass and grain yields of wheat genotypes grown with variable water stress in sodic soil environments. We employed unmanned aerial vehicle-based thermal imaging to quantify water stress of 18 contrasting wheat genotypes grown on moderately sodic (MS) and highly sodic (HS) soils in north-eastern grains growing regions of Australia and related these to ground-measured plant biomass and grain yields. We evaluated crop water stress indices; standardized canopy temperature index, crop water stress index, stomatal conductance index, vapour pressure deficit, and crop stress index, which were computed from thermal imagery and on-site agro-meteorological parameters close to flowering. We then employed a classification and regression tree (CRT) as a supervised machine learning algorithm to classify crop water stress and predict biomass and grain yields as a function of crop water stress indices. The CRT accurately predicted biomass yield (coefficient of determination (R²) = 0.86; root mean square error (RMSE) = 41.3 g/m² and R² = 0.75; RMSE = 47.7 g/m² for the MS and HS site) and grain yield (R² = 0.78; RMSE = 16.7 g/m² and R² = 0.69; RMSE = 23.2 g/m² for the MS and HS site, respectively). High sodic soil constraints increased crop water stress more than moderately sodic constraints soil that limits wheat yield ~40%. Wheat genotypes; Bremer, Gregory, Lancer, Mace, and Mitch were more productive than Gladius, Flanker, Scout, Emu Rock, and Janz in sodic soil environments. The study improves our ability to develop decision-making tools to assist farmers and breeders in securing agricultural productivity on sodic soils.

水分胁迫限制了小麦的生长和雨养钠质土壤的产量。Appropriate selection of traits and novel methods are required to forecast yield and to identify water stress tolerant wheat genotypes on sodic soils. 在这项研究中，我们提出了一种基于热遥感和机器学习的方法，以帮助预测在钠土壤环境中在可变水分胁迫下生长的小麦基因型的生物量和谷物产量。我们使用基于无人机的热成像来量化在澳大利亚东北部谷物种植区中度钠 (MS) 和高钠 (HS) 土壤上生长的 18 种对比小麦基因型的水分胁迫，并将这些与地面测量的植物生物量相关联和粮食产量。我们评估了作物水分胁迫指数；标准化冠层温度指数、作物水分胁迫指数、气孔导度指数、蒸汽压差和作物压力指数，这些指数是根据接近开花的热成像和现场农业气象参数计算得出的。然后，我们采用分类和回归树（CRT）作为有监督的机器学习算法，对作物水分胁迫进行分类，并根据作物水分胁迫指数预测生物量和谷物产量。CRT 准确预测了生物质产量（决定系数 (R² ) = 0.86; 均方根误差 (RMSE) = 41.3 g/m ²且 R ²  = 0.75；MS 和 HS 站点的RMSE = 47.7 g/m ²）和谷物产量（R ²  = 0.78；RMSE = 16.7 g/m ²和 R ²  = 0.69；MSE和 HS 站点的RMSE = 23.2 g/m ²，分别）。高钠盐土壤限制比限制小麦产量约40％的中度钠盐限制土壤增加的作物水分胁迫。小麦基因型；布雷默，格雷戈里，蓝瑟，梅斯和米奇比格拉迪乌斯，弗兰克，侦察兵，埃姆·洛克和扬兹更具生产力在含钠土壤环境中。该研究提高了我们开发决策工具的能力，以帮助农民和育种者确保钠质土壤的农业生产力。