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

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.