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Abstract Stem water potential (Ψ_stem) is a key indicator for assessing plant water status, which is crucial in understanding plant health and productivity. However, existing measurement methods for Ψ_stem, characterized by destructiveness and intermittency, limit its applicability. Microtensiometers, an emerging plant-based sensor, offer continuous monitoring capabilities and have shown success in certain vine and tree species. In this study, we investigate the efficacy of microtensiometers ability to monitor the Ψ_stemof cotton (Gossypium hirsutumL.) under three distinct irrigation treatments in Maricopa, Arizona, an extremely hot, arid environment. We analyze the diurnal dynamics of Ψ_stemacross the irrigation regimes and compare these measurements with midday leaf water potentials (Ψ_leaf) obtained using a dewpoint potentiometer. Our results demonstrate that the microtensiometer-derived Ψ_stemclosely follows known diurnal patterns of Ψ_leaf, tracking with vapor pressure deficit (VPD) and responding to variations in irrigation levels and soil moisture content. Time cross-correlation analysis reveals an 80-minute lag in Ψ_stemresponse to changing VPD under non-water limiting conditions, which shortens under water-limiting conditions. Additionally, we establish a robust linear relationship (R²_adj = 0.82) between Ψ_stemand Ψ_leaf, with this relationship strengthening as water availability decreases. Notably, we observe mean gradients of 1.2 and 0.06 MPa between soil vs. stem and stem vs. leaf water potentials, respectively. Moreover, Ψ_stemdata proves to be more sensitive in distinguishing between irrigation treatments earlier in the growing season compared to Ψ_leaf, leaf temperature and leaf gas exchange parameters. These findings highlight the utility of microtensiometers as valuable tools for monitoring water status in smaller-stemmed row crops such as cotton. 

Charcoal rot of sorghum (CRS) is a significant disease affecting sorghum crops, with limited genetic resistance available. The causative agent,Macrophomina phaseolina(Tassi) Goid, is a highly destructive fungal pathogen that targets over 500 plant species globally, including essential staple crops. Utilizing field image data for precise detection and quantification of CRS could greatly assist in the prompt identification and management of affected fields and thereby reduce yield losses. The objective of this work was to implement various machine learning algorithms to evaluate their ability to accurately detect and quantify CRS in red‐green‐blue images of sorghum plants exhibiting symptoms of infection. EfficientNet‐B3 and a fully convolutional network emerged as the top‐performing models for image classification and segmentation tasks, respectively. Among the classification models evaluated, EfficientNet‐B3 demonstrated superior performance, achieving an accuracy of 86.97%, a recall rate of 0.71, and an F1 score of 0.73. Of the segmentation models tested, FCN proved to be the most effective, exhibiting a validation accuracy of 97.76%, a recall rate of 0.68, and an F1 score of 0.66. As the size of the image patches increased, both models’ validation scores increased linearly, and their inference time decreased exponentially. This trend could be attributed to larger patches containing more information, improving model performance, and fewer patches reducing the computational load, thus decreasing inference time. The models, in addition to being immediately useful for breeders and growers of sorghum, advance the domain of automated plant phenotyping and may serve as a foundation for drone‐based or other automated field phenotyping efforts. Additionally, the models presented herein can be accessed through a web‐based application where users can easily analyze their own images.