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Water status estimation of cherry trees using infrared thermal imagery coupled with supervised machine learning modeling
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NSTL
Elsevier
? 2022 Elsevier B.V.The implementation of artificial intelligence (AI) in parallel with remote sensing could be a powerful tool to manage irrigation scheduling on crops with narrow thresholds between water stress levels, such as cherry trees. This research assessed the water status of 'Regina' cherry trees using machine learning (ML) modeling from data extracted automatically using infrared thermal imagery (IRTI). These models were used to predict stomatal conductance (gs) and stem water potential (Ψs) (Model 1) and a complete assessment using a matrix differential analysis procedure per IRTI of cherry tree canopies' temperature and relative humidity (Model 2). Results showed that the supervised ML regression models presented high and significant correlation coefficients (R = 0.83 and R = 0.81, respectively) without signs of overfitting assessed through their performance. The complex interactions among climatic factors, the soil moisture, and canopy architecture observed in cherry trees or any other fruit tree oblige exploring the performance of ML-based models to offer simple alternatives for decision-making processes in the field.
Artificial intelligenceArtificial neural networksIrrigation schedulingRemote sensingStem water potential
Carrasco-Benavides M.、Baffico-Hernandez A.、Gonzalez Viejo C.、Tongson E.、Fuentes S.、Avila-Sanchez C.、Mora M.
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Departamento de Ciencias Agrarias Facultad de Ciencias Agrarias y Forestales Universidad Católica del Maule
Digital Agriculture Food and Wine Group School of Agriculture and Food Faculty of Veterinary and Agricultural Sciences University of Melbourne
Research and Extension Center for Irrigation and Agroclimatology (CITRA) Faculty of Agricultural Sciences Universidad de Talca
Laboratory of Technological Research in Pattern Recognition (LITRP) Faculty of Engineering Science Universidad Católica del Maule
NSTL
Elsevier
