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Technological advances are providing farmers with valuable data about their crops. However, to improve resource use efficiency in agriculture, it is necessary to transform this data into practical information, applicable by farmers and/or technicians in crop management. The objective of this work was to develop a fully automated IoT platform that integrates crop images from RGB cameras with open climate data sources and crop models, to optimize irrigation strategies and enhance crop productivity under varying environmental conditions. To achieve this, the AquaCrop-IoT platform was developed, which integrates the FAO’s AquaCrop model with a custom-build image capture and processing system, used to adjust the green canopy cover (CC) in real-time. Additionally, the platform incorporates weather data from in-situ weather stations, and forecasts and historical weather data from open datasets. Everything is presented in a web application that facilitates its use. The platform has been tested in a wheat crop in southern Spain throughout its growth cycle, demonstrating its potential as a decision support system for irrigation management. Dynamically updating CC values using images captured by the in-situ camera enabled the AquaCrop model to correct potential errors in crop growth estimation by including the effects of adverse factors like pests and diseases that the model cannot simulate. Furthermore, as the developed platform incorporates meteorological data daily, in real-time, it allowed the design of real-time irrigation schedules tailored to the crop in its particular environment and management. This approach improved the estimation of crop water requirements, reducing the amount of recommended irrigation water during the wheat growing season by approximately 32%.
Puig et al. (Fri,) studied this question.