Precision farming technologies refer to a set of cutting-edge tools and strategies implemented to optimize the management of the plantation. Smart meter devices, Internet of Things (IoT) technologies, and wireless sensor networks (WSNs) are only a few examples of the innovative systems increasingly employed from an Agriculture 4.0 point of view. Recent literature has paid close attention to the role of artificial intelligence (AI) and deep learning (DL) algorithms in helping farmers and improving soil productivity. In this regard, this article presents the design of a WSN based on low-cost, low-power photovoltaic (PV)-supplied sensor nodes able to acquire data regarding environmental conditions and soil parameters. Among all the implemented sensors, the most critical is the soil moisture sensors because of many issues related to cost, installation, reliability, and calibration. Thus, this article proposes a DL approach based on long short-term memory (LSTM) networks to provide a virtual soil moisture sensor using only the data acquired by the other transducer installed on the node. Performance estimation of the virtual sensors and an in-depth comparison with other learning-based approaches have been presented in this article to validate the effectiveness of the proposed soft sensing approach.

A Virtual Soil Moisture Sensor for Smart Farming Using Deep Learning

Gallo, V;Sommella, P;Carratu', M
2022

Abstract

Precision farming technologies refer to a set of cutting-edge tools and strategies implemented to optimize the management of the plantation. Smart meter devices, Internet of Things (IoT) technologies, and wireless sensor networks (WSNs) are only a few examples of the innovative systems increasingly employed from an Agriculture 4.0 point of view. Recent literature has paid close attention to the role of artificial intelligence (AI) and deep learning (DL) algorithms in helping farmers and improving soil productivity. In this regard, this article presents the design of a WSN based on low-cost, low-power photovoltaic (PV)-supplied sensor nodes able to acquire data regarding environmental conditions and soil parameters. Among all the implemented sensors, the most critical is the soil moisture sensors because of many issues related to cost, installation, reliability, and calibration. Thus, this article proposes a DL approach based on long short-term memory (LSTM) networks to provide a virtual soil moisture sensor using only the data acquired by the other transducer installed on the node. Performance estimation of the virtual sensors and an in-depth comparison with other learning-based approaches have been presented in this article to validate the effectiveness of the proposed soft sensing approach.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4807747
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