Abstract
Precision irrigation represents those strategies aiming to feed the plant needs following the soil’s spatial and temporal characteristics. Such a differential irrigation requires a different approach and equipment with regard to conventional irrigation to reduce the environmental impact and the resources use while maximizing the production and thus profitability. This study described the development of an open source soil moisture LoRa (long-range) device and analysis of the data collected and updated directly in the field (i.e., weather station and ground sensor). The work produced adaptive supervised predictive models to optimize the management of agricultural precision irrigation practices and for an effective calibration of other agronomic interventions. These approaches are defined as adaptive because they self-learn with the acquisition of new data, updating the on-the-go model over time. The location chosen for the experimental setup is a cultivated area in the municipality of Tenna (Trentino, Alto Adige region, Italy), and the experiment was conducted on two different apple varieties during summer 2019. The adaptative partial least squares time-lag time-series modeling, in operative field conditions, was a posteriori applied in the consortium for 78 days during the dry season, producing total savings of 255 mm of irrigated water and 44,000 kW of electricity, equal to 10.82%.
Highlights
Traditional irrigation consumes great amounts of water and electrical energy because it applies water uniformly over every part of the field without considering the variability of soil and crop different water needs
As reported by Alt et al [4], this means that agricultural production processes should become more efficient, and there is an inevitability of digitalization of all agricultural systems
The results show that soil moisture increase after one day at 30 cm depth from the water input on the surface, and after two days at 60 cm
Summary
Traditional irrigation consumes great amounts of water and electrical energy because it applies water uniformly over every part of the field without considering the variability of soil and crop different water needs. As reported by Alt et al [4], this means that agricultural production processes should become more efficient, and there is an inevitability of digitalization of all agricultural systems. This is made possible using intelligent technologies (e.g., artificial intelligence, robotics, Internet of things (IoT), unmanned aerial vehicles, etc.), which could increase productivity, reduce production costs, and reduce labor requirements [5]
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