Development of a Sensor Node for Remote Monitoring of Plants

Catini, Alexandro; Papale, Leonardo; Capuano, Rosamaria; Pasqualetti, Valentina; Giuseppe, Davide Di; Brizzolara, Stefano; Tonutti, Pietro; Natale, Corrado Di

doi:10.3390/s19224865

Cited by 24 publications

(14 citation statements)

References 14 publications

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“…However, nowadays, there are still few studies in the bibliography that include them. Some of these sensors have previously been used to monitoring plants activity [19], to study their individual response to BTEX (Benzene, Toluene, Ethylbenzene and Xylenes) compounds [20], or to investigate their architecture and operation (specifically Sensirion's SGP30 model) [21].…”

Section: Introductionmentioning

confidence: 99%

Electronic Nose with Digital Gas Sensors Connected via Bluetooth to a Smartphone for Air Quality Measurements

Arroyo

Meléndez

Suárez

et al. 2020

Sensors

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This paper introduces a miniaturized personal electronic nose (39 mm × 33 mm), which is managed through an app developed on a smartphone. The electronic nose (e-nose) incorporates four new generation digital gas sensors. These MOx-type sensors incorporate a microcontroller in the same package, being also smaller than the previous generation. This makes it easier to integrate them into the electronics and improves their performance. In this research, the application of the device is focused on the detection of atmospheric pollutants in order to complement the information provided by the reference stations. To validate the system, it has been tested with different concentrations of NOx including some tests specifically developed to study the behavior of the device in different humidity conditions. Finally, a mobile application has been developed to provide classification services. In this regard, a neural network has been developed, trained, and integrated into a smartphone to process the information retrieved from e-nose devices.

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Section: Introductionmentioning

confidence: 99%

Electronic Nose with Digital Gas Sensors Connected via Bluetooth to a Smartphone for Air Quality Measurements

Arroyo

Meléndez

Suárez

et al. 2020

Sensors

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“…where V R is the offset voltage due to the series R LOAD , V REF is the new reference voltage of the circuit given by V REF = vs. − V R , τ is the characteristic time constant of the equivalent circuit given by the τ = R T C LOAD and R T is the total resistance path contributing to the potential difference on the capacitance terminals given by R T = R AIN || R pu + R LOAD + R out . Thus, C LOAD can be found at any time t by rearranging Equation ( 9) to: (10) where t is the time that takes charges to accumulate in the capacitance. Figure 5a illustrates the transient response to a voltage step expressed by Equation (10), where the highlighted region consists of the offset voltage (V R ) due to the bridge of ports P A0 and P A1 with the serial RC network.…”

Section: Measurements Of a Serial Rc Network (Rc-meter Mode)mentioning

confidence: 99%

“…Thus, C LOAD can be found at any time t by rearranging Equation ( 9) to: (10) where t is the time that takes charges to accumulate in the capacitance. Figure 5a illustrates the transient response to a voltage step expressed by Equation (10), where the highlighted region consists of the offset voltage (V R ) due to the bridge of ports P A0 and P A1 with the serial RC network. As for the reading capacitance voltage level (V C ) it is determined when t = τ = R T C LOAD , and is given by…”

Section: Measurements Of a Serial Rc Network (Rc-meter Mode)mentioning

confidence: 99%

“…The simplest approach to find V F using the transient response to a voltage step is to define the maximum characteristic time constant (τ max ) to measure. In these circumstances all parameters are known, and both C LOAD and R LOAD values can be measured using Equations ( 2) and (10). Figure 6b depicts the pseudo-code of a routine to implement the measurement of a parallel RC network.…”

Section: Measurements Of a Parallel Rc Network (Rc-meter Mode)mentioning

confidence: 99%

“…Impedimetric sensor arrays are an emerging field of study that is concerned with sense, processing and casting the measured data to the Internet [ 1 , 2 , 3 ]. Typical applications are wearable and in-body electronics [ 4 , 5 , 6 , 7 , 8 ] and plants and smart agriculture [ 9 , 10 , 11 , 12 , 13 ]. A common aspect shared between different devices is that the electronic interfaces for detection, processing and connection to the Internet are mainly carried out by separate external systems specifically optimized for each of these functions.…”

Section: Introductionmentioning

confidence: 99%

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An Ultra-Low-Cost RCL-Meter

Inácio

Guerra

Stallinga

2022

Sensors

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An ultra-low-cost RCL meter, aimed at IoT applications, was developed, and was used to measure electrical components based on standard techniques without the need of additional electronics beyond the AVR® micro-controller hardware itself and high-level routines. The models and pseudo-routines required to measure admittance parameters are described, and a benchmark between the ATmega328P and ATmega32U4 AVR® micro-controllers was performed to validate the resistance and capacitance measurements. Both ATmega328P and ATmega32U4 micro-controllers could measure isolated resistances from 0.5 Ω to 80 MΩ and capacitances from 100 fF to 4.7 mF. Inductance measurements are estimated at between 0.2 mH to 1.5 H. The accuracy and range of the measurements of series and parallel RC networks are demonstrated. The relative accuracy (ar) and relative precision (pr) of the measurements were quantified. For the resistance measurements, typically ar, pr < 10% in the interval 100 Ω–100 MΩ. For the capacitance, measured in one of the modes (fast mode), ar < 20% and pr < 5% in the range 100 fF–10 nF, while for the other mode (transient mode), typically ar < 20% in the range 10 nF–10 mF and pr < 5% for 100 pF–10 mF. ar falls below 5% in some sub-ranges. The combination of the two capacitance modes allows for measurements in the range 100 fF–10 mF (11 orders of magnitude) with ar < 20%. Possible applications include the sensing of impedimetric sensor arrays targeted for wearable and in-body bioelectronics, smart agriculture, and smart cities, while complying with small form factor and low cost.

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Plant‐Wearable Sensors for Intelligent Forestry Monitoring

Han

et al. 2022

Advanced Sustainable Systems

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Traditional monitoring technologies, such as infrared thermography, [7] hyperspectral techniques, [8] wireless sensor networks, [9] satellite imaging and remote sensing techniques, [10] etc. have limited their use in monitoring the growth of forest seedlings due to their high cost, difficult installation, complicated signal conversion, and difficulty in achieving high temporal resolution for real-time monitoring. [11] Moreover, the operational complexity, nonportability, and poor biocompatibility of traditional rigid sensors used for forestry monitoring hinder the healthy growth of seedlings dealing with developing and fragile tree seedlings. [12,13] On the other hand, the growth and development of seedlings directly affect the quality of forestry trees and changes in the forest ecological environment. [14] Plant physiological metabolism, plant growth in seedling cultivation and growing environment monitoring of young trees need to be monitored to evaluate plant growth characteristics, and then integrated with molecular theories such as genetic modification in forest genetics and breeding. [15,16] In this process, plant-wearable sensors play a very important role, and the lack of intelligent forestry monitoring technology brings problems such as high cost, long time, and inaccurate data. Hence, it is essential to develop intelligent strategies that monitor the growth of young forests and the harmful stresses during the growth of young forests, which induces unique requirement of flexible plant-wearable sensors for forestry monitoring.Meanwhile, plant-wearable sensors can be closely fitted to the surface of tree seedlings for real-time continuous monitoring without causing damage to the seedlings themselves by using their own characteristics such as flexibility and stretchability, [17,18] thus appealing to this wearables requirement for intelligent forestry monitoring. Therefore, flexible, lightweight, and wearable properties of plant-wearable sensors are urgently needed for this application of intelligent forestry monitoring.Herein, this paper introduces the recent development status of plant-wearable sensors in detail from the perspective of early intelligent monitoring in forestry and enumerates their structural compositions and working mechanisms. Application scenarios of plant wearables, including for the detection of plant physiological metabolism and plant growth, signal molecule detection, forest fire prevention, growth environment monitoring, and development of self-powered wireless monitoring system, are systematically summarized (Figure 1). Flexible plant-wearable sensors show great potential for precise and intelligent monitoring of real-time physical and chemical signals in forestry seedlings to optimize their growth environment, increase carbon sequestration, and promote underforest ecosystems. Furthermore, plant-wearable sensors can help prevent threats such as forest ecological deterioration, forest diseases, and insect pests. Herein, recent advances in emerging plantwearable sensors used in forest...

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Development of a Sensor Node for Remote Monitoring of Plants

Cited by 24 publications

References 14 publications

Electronic Nose with Digital Gas Sensors Connected via Bluetooth to a Smartphone for Air Quality Measurements

Electronic Nose with Digital Gas Sensors Connected via Bluetooth to a Smartphone for Air Quality Measurements

An Ultra-Low-Cost RCL-Meter

Plant‐Wearable Sensors for Intelligent Forestry Monitoring

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