| Title: | Development of a Sensor Node for Remote Monitoring of Plants |
| Author(s): | Catini A; Papale L; Capuano R; Pasqualetti V; Di Giuseppe D; Brizzolara S; Tonutti P; Di Natale C; |
| Address: | "Department of Electronic Engineering, Tor Vergata University of Rome, Via del Politecnico 1, 00133 Rome, Italy. Sant'Anna School of Advanced Studies, Piazza Martiri della Liberta 33, 56127 Pisa, Italy" |
| ISSN/ISBN: | 1424-8220 (Electronic) 1424-8220 (Linking) |
| Abstract: | "The appraisal of stress in plants is of great relevance in agriculture and any time the transport of living plants is involved. Wireless sensor networks (WSNs) are an optimal solution to simultaneously monitor a large number of plants in a mostly automatic way. A number of sensors are readily available to monitor indicators that are likely related to stress. The most common of them include the levels of total volatile compounds and CO(2) together with common physical parameters such as temperature, relative humidity, and illumination, which are known to affect plants' behavior. Recent progress in microsensors and communication technologies, such as the LoRa protocol, makes it possible to design sensor nodes of high sensitivity where power consumption, transmitting distances, and costs are optimized. In this paper, the design of a WSN dedicated to plant stress monitoring is described. The nodes have been tested on European privet (Ligustrum Jonandrum) kept in completely different conditions in order to induce opposite level of stress. The results confirmed the relationship between the release of total Volatile Organic Compounds (VOCs) and the environmental conditions. A machine learning model based on recursive neural networks demonstrates that total VOCs can be estimated from the measure of the environmental parameters" |
| Keywords: | Ligustrum Machine Learning Remote Sensing Technology/*methods Volatile Organic Compounds/analysis *Wireless Technology VOCs Wsn gas sensing plant health recursive neural network; |
| Notes: | "MedlineCatini, Alexandro Papale, Leonardo Capuano, Rosamaria Pasqualetti, Valentina Di Giuseppe, Davide Brizzolara, Stefano Tonutti, Pietro Di Natale, Corrado eng Switzerland 2019/11/14 Sensors (Basel). 2019 Nov 8; 19(22):4865. doi: 10.3390/s19224865" |
