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Crop-water assessment in Citrus (Citrus sinensis L.) based on continuous measurements of leaf-turgor pressure using machine learning and IoT

2022; Elsevier BV; Volume: 209; Linguagem: Inglês

10.1016/j.eswa.2022.118255

1873-6793

José Barriga, Fernando Blanco-Cipollone, Emiliano Trigo-Córdoba, Iván Francisco García Tejero, Pedro J. Clemente,

Smart Agriculture and AI

Water is the most limiting natural resource in many semi-arid areas. This, together with the current climate change scenario, is fostering a context of uncertainty and major challenges concerning the sustainability and viability of existing agroecosystems. Crop water status based on three pre-established values (severe, mild, and no stress) is the essential datum needed to implement optimised irrigation scheduling based on deficit irrigation. Currently however, its calculation is a repetitive, tedious, and technical process carried out by hand. This communication presents a novel system based on continuous measurements of leaf turgor pressure to assess the crop water status when deficit irrigation strategies are being applied and/or to optimise irrigation scheduling in water scarcity scenarios. To this end, a novel expert system based on machine learning, together with an IoT infrastructure based on continuous measurements of leaf turgor pressure, is able to predict the citrus crop ψstem with a 99% F1 score. Thus, crop irrigation strategies involving irrigation-restriction cycles can be applied based on stem water potential.