Abstract
In smart farming, precision agriculture irrigation is essential to reduce water consumption and produce higher crop yields. Closed-loop irrigation based on soil moisture measurements has demonstrated the capability to achieve a considerable amount of water savings while growing healthy crops. Automated irrigation systems are typically implemented over wireless sensor networks, where the sensing devices are battery-powered, and thus they have to manage energy constraints by implementing efficient communication schemas. Self-triggered control is an aperiodic sampling strategy capable of reducing the number of networked messages compared to traditional periodical sampling. In this paper, we propose an energy-efficient communication strategy for closed-loop control irrigation, implemented over a wireless sensor network, where event-driven soil moisture measurements are conducted by the sensing devices only when needed. Thereby, the self-triggered algorithm estimates the occurrence of the next sampling period based on the process dynamics. The proposed strategy was evaluated in a pecan crop field and compared with periodical sampling implementations. The experimental results show that the proposed adaptive sampling rate technique decreased the number of communication messages more than 85% and reduced power consumption up to 20%, while still accomplishing the system control objectives in terms of the irrigation efficiency and water consumption.
Highlights
Smart farming aims to reduce costs and increase productivity while optimizing natural resources through the processing and analyzing of data obtained from sensor devices [1]
The work presented in this paper proposes an adaptive sampling rate energy-efficient communication strategy to reduce power consumption from the remote nodes in a wireless sensor network using self-triggered algorithms to implement closed-loop control for precision agriculture irrigation
Both self-triggered approaches γ1 and γ2 significantly reduced the number of communication messages compared with the periodical strategy γ0, and still achieved practically the same control performance in terms of water consumption and irrigation error
Summary
Smart farming aims to reduce costs and increase productivity while optimizing natural resources through the processing and analyzing of data obtained from sensor devices [1]. Crop and environmental variables, such as soil moisture, leaf wetness, air temperature, atmospheric pressure, relative humidity, and solar radiation, are commonly monitored in different agriculture applications [4]. Soil moisture may be the most complex variable to measure due to its high spatial and temporal variability and nonlinear behavior. The relevance of measuring the soil moisture has recently increased as closed-loop irrigation, based on this variable, has become one of the most effective strategies to improve water efficiency by applying the right amount of water to crops at the right time [5]
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