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 CO2 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.

Highlights

  • The standard approach for the monitoring of crops and single plants is based on the measurement of environmental parameters such as humidity, illumination, and soil composition

  • We describe the development and application of a wireless sensor system aimed at detecting, in real-time, a number of physiological and environmental parameters

  • Results showed that the total VOCs (TVOCs) production is related to the health conditions

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Summary

Introduction

The standard approach for the monitoring of crops and single plants is based on the measurement of environmental parameters such as humidity, illumination, and soil composition. VOCs microsensors are becoming available in the market These devices are not specific, and they provide only a generic evaluation of a total amount of volatile compounds. As an example of application, the wireless sensor node (WSN) has been used to monitor the parameters of plants of the same species, size, and age, but kept in extremely different conditions. Both plants were planted in the same soil, but one plant was exposed to natural light and regularly watered, and the other was kept in the dark and without water. The relationship between environmental parameters and VOCs is evidenced by a neural network regression model that can estimate the amount of total VOCs from the environmental parameters

Sensors Selection
WSN Board Design
WSN on Plants
WSN Test on Plants
Functional
Plant Experiment
Principal
Conclusions
Full Text
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