A Virtual Soil Moisture Sensor for Smart Farming Using Deep Learning

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, IoT technologies, and Wireless Sensor Networks 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 paper presents the design of a Wireless Sensor Network based on low-cost, low-power 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 paper proposes a deep learning 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. Performances estimation of the virtual sensors and an in-depth comparison with other learning-based approaches have been presented in this paper to validate the effectiveness of the proposed soft sensing approach.