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Novel soil environment monitoring system based on RFID sensor and LoRa
Computers and Electronics in Agriculture 169 (2020) 105169
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Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag
Novel soil environment monitoring system based on RFID sensor and LoRa ⁎
T
Fangming Deng , Pengqi Zuo, Kaiyun Wen, Xiang Wu Department of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Soil Environment Monitoring RFID sensor LoRa
This work proposes a novel soil environment monitoring system based on RFID sensor and LoRa to realize long term and low cost monitoring. The monitoring system consists of patrol car, RFID sensor, farmland monitoring center and cloud platform. The proposed patrol car is responsible for collecting the information of embedded RFID sensors and communicate with the monitoring center through LoRa. The proposed RFID sensor is comprised of a power management section, a communication section, and a digital section. The power management section employs a whip antenna for energy harvesting, and a novel boost rectifier, consisting of a single stage rectifier and a DC-DC charge pump, is introduced to rectify the harvested RF power for high efficiency. A voltage regulator is used to generate a stable DC voltage supply. In the communication section, a novel monopole antenna is propose to minimize the size of antenna. It can be concluded from the experimental results that our RFID sensor can work under the maximum communication distance of 1.3 m. The errors of the measured temperature and moisture content are 1.5% and 1.0% respectively when the embedding depth of RFID sensor is 60 cm and the moisture content of soil is smaller than 30%. The optimum speed of patrol car is 33 km/h when the communication success rate is above 90% and the coverage area is above 10 km2.
1. Introduction Modern agriculture requires increased food production to satisfy the large global population. However, the existing productivity is still low due to the extensive agriculture technology. In this regard, the precision agriculture (PA) is introduced by many researchers to overcome this problem (Wachowiak et al., 2017). In the field of PA, the real-time monitoring of soil environment plays a significant role (Ko et al., 2014; Goh et al., 2014). Because of the dispersal of agricultural regions and thus the great various of terrains and environment conditions, how to collect the information of crop growth environment variables precisely and speedily is the main topic on the field of agricultural environment information technology (Khanna and Kaur, 2019). Wireless sensor network (WSN) was proposed to solve this issue (Ouyang et al., 2019). The WSN is composed of many sensor nodes distributed in the farm field, which can collaborate with each other to on-line measure the soil and environment conditions. This data can be processed by the senor nodes, Furthermore, these senor nodes can form the wireless network automatically to achieve the remote monitoring of the farmland environment (Zhang et al., 2019). WSN has been widely used in farmland environment, such as watering, cultivation, etc. Most WSNs are ground wireless sensor network systems designed for agricultural applications (Caicedo-Ortiz et al., 2018). In order to avoid the
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wireless signals transmitting in the soil, the sensors in the soil are connected with the wireless transceivers on the ground by wires. In recent years, the soil monitoring system based on LoRa arouse the great interest of researchers (Talavera et al., 2017). Compared with the conventional WSN technologies such as ZigBee, LoRa technology shows the advantages of large communication distance (up to 10 km), longe node life-time (up to 10 years) and high anti-interference performance. However these transceivers of the wireless sensors discussed above are exposed to the air and will block the agricultural activities. Furthermore, the transmission of wireless nodes will be influenced by environmental factors. Wireless underground sensor networks (WUSN) is a new technology for underground environmental monitoring and have become a hot topic in the agricultural environment monitoring field (Sun and Akyildiz, 2010). The wireless sensors buried in the ground transmit information through the soil, and the data is apperceived and collected when induction module perceives it. The WUSN exhibits many advantages, for example, great concealment, deployment and reliability, etc. However, the existing WUSN node always employs battery as power supply, once buried in the soil, it would be difficult to replace the battery and the obsolete battery would destroy soil environment. Radio frequency identification (RFID) technology is a non-contact automatic identification technology realized by radio frequency
Corresponding author. E-mail address: [email protected] (F. Deng).
https://doi.org/10.1016/j.compag.2019.105169 Received 26 September 2019; Received in revised form 4 December 2019; Accepted 19 December 2019 0168-1699/ © 2019 Published by Elsevier B.V.
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communication. In recent years the research on the RFID tag incorporated with sensors becomes hot topic. Due to the backscattering scheme, the RFID sensors show the advantages of simple architecture, low power and low cost (Deng et al., 2014). Furthermore, each RFID sensor has the unique identification (ID), which lead to fast locating when the fault occurs. The RFID tag always works under the power dissipation range of µW, hence many sensors, such as piezoceramicbased sensors (Serrano-Finetti et al., 2019; Furui and Gangbing, 2019), are not capable incorporated with RFID tag because of their high power conversion. Kuhn M F et al. (Kuhn et al., 2018) propose an antennabased RFID strain sensor for wireless structural health monitoring. Deng et al. (2018) present a chipless humidity sensor for low-cost environment monitoring. Tao et al., (2018), Wang et al. (2017) design a selfpowered RFID acceleration sensor for transformer condition monitoring. Donno et al. (2011) design a battery-assisted RFID tag integrated with temperature sensor, humidity sensor and light sensor for long-range communication distance. There are various design methods for RFID sensor. The RFID sensors based on inductive coupling technology operate on low or high frequency with the communication distance within 10 cm, but they exhibit high transferring efficiency and anti-metal interference performance (Pichler et al., 2017; Ruiz-Garcia and Lunadei, 2011). The RFID sensors operating in ultra-high frequency can obtain long communication distance (up to 10 m) and high data transmission rate. The chipless RFID sensors are introduced with the functionality of temperature and humidity sensing (Luvisi et al., 2016). This kind of wireless sensors are especially for low cost and low power application. RFID sensor based on integrated circuit (IC) technology has also garnered great interests. Different types of sensors can be integrated with the RFID circuit within one chip (Vaz et al., 2010), which can achieve the advantages in the fields of power consumption, circuit area and system stability. But limited by the fabrication process, only few kinds of sensors can be integrated. Recently, the RFID sensor based on discrete components has been widely employed. In this way, the RFID chip, radio frequency front-end circuit, micro- controller unit (MCU) and sensors are installed on the PCB board (Sample et al., 2008). This kind of RFID sensors has merits of low costs, short design time, easy to customize and extend. Hence, the RFID sensor made of discrete components is the best choice for underground soil environment monitoring. Some researchers have already carried out the research on RFID sensor for soil environment monitoring. Hamrita and Hoffacker (2005) develop a passive RFID sensor for soil temperature monitoring, however this RFID sensor works on 13.56 MHz and both of the communication speed and communication distance are very limited as discussed above. Hasan et al. (2013) design a RFID sensor on 915 MHz for pervasive soil moisture monitoring, however the designed RFID tag is still on the soil surface and connected with the humidity sensor underground through transmission line. In Pichorim et al. (2018) two RFID solutions for soil moisture monitoring are investigated, one is RFID sensors on surface, another one is the RFID tag on surface connected with underground humidity sensor. In Luvisi et al. (2016) an RFID temperature sensor is employed to monitor the underground soil solarization. However, the function of these RFID sensors discussed above is limited and only can measure the temperature or humidity of the soil environment. This work proposes a novel soil environment monitoring system based on RFID sensor and Lora. The patrol car collects the information of the RFID sensors embedded in soil and then communicates with the monitoring center through Lora. We also proves the feasibility of the passive RFID sensor for embedded soil monitoring. Compared with other soil monitoring techniques, the proposed method based on RFID sensor exhibits the advantages of passive operating mode and fast locating ability. Different from the current designs of RFID sensor for soil monitoring, the proposed RFID sensor is more versatile incorporating temperature, humidity and chloride ion sensing. The paper is organized as follows. The design of the proposed Soil
Fig. 1. Proposed monitoring system.
Environment Monitoring System is introduced in detail in Section 2. In Section 3, the design of the RFID sensor is introduced in detail. The experiment results are discussed in Section 4 and then the conclusion is made in Section 5. 2. Proposed monitoring system design Fig. 1 shows the proposed soil environment monitoring system. The designed RFID sensors are embedded in soil for high precision monitoring. The patrol car, incorporating RFID reader and LoRa communication function, is responsible for collecting the information of embedded RFID sensors and communicate with the monitoring center. The monitoring center will store the received data and upload it to the cloud platform through wired or cellular mobile network. The cloud platform can analyses the data and make the corresponding decisions. Fig. 2 shows the proposed design of patrol car. The proposed RFID reader of the patrol car consist of a separate RFID transmitter in the front of the car and a separate RFID receiver in the end of the car. The transmitter generates a continuous wave (CW) signal to power the RFID sensor and the receiver amplifies and demodulates the backscatter signal from the embedded RFID sensors. The incorporated LoRa block is responsible for communicating with the monitoring center and the communication distance can reach 10 km. Ordinarily an RFID system is composed of a RFID tag, an interrogator and the backstage management. The communication manner of the proposed system depends on the protocol of the electronic product code (EPC) generation2 (G2), as shown in Fig. 3 (EPCglobal Inc., 2008). Firstly, select instruction is sent from the reader by Carrier Wave (CW) to the RFID tag. Then, query instruction is received by the tag, which responds with the RN16. The reader then sends out the ACK command in order to achieve the query ID information and the CRC code from the
Fig. 2. Proposed patrol car. 2
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Fig. 3. RFID communication flow.
The power received by the on-board antenna cannot supply other blocks in the RFID sensor directly. Thus, the harvested power should be rectified and boosted to the demanding DC voltage by the boosting rectifier in Fig. 5(b). Then, the voltage regulator is adopted to generate a DC voltage for other components. The proposed boost rectifier circuit consists of a single stage rectifier and a DC-DC charge pump. The single stage rectifier circuit is composed of rectifier diodes D1, D2 and capacitor C2, which are employed to transfer the input RF power into a DC voltage. Since the rectification efficiency is determined by the threshold voltage of the diodes (Deng et al., 2014), this paper adopts the zero-biased Schottky diode SMS7630 for high efficiency. Compared with traditional multiple-stage rectifier circuit (Wang et al., 2017), a high efficiency DC-DC charge pump chip S-882Z24 is adopted following the single stage rectifier. The proposed charge pump is fabricated in silicon on insulator technology, which minimum input voltage is only 0.3 V. When the output voltage of load capacitor VR increases to VRH = 2.4 V, the S-882Z24 chip starts to active the digital section, when VR decreases to VRL = 1.85 V, the S882Z24 chip disconnects the output circuit and starts a new charging process.
tag. After that the reader continues to send Req_RN to acquire the handle response from the tag. It can be obtained from the communication flow, the sensor information cannot be processed directly, so we have to improve the communication mechanism. Generally, the sensor information can be stored in the non-volatile memory of the tag. However, it needs a relative long response time and consumes higher power during the read and write process (Su et al., 2015). Therefore we propose a new method that the measurement sensor data is incorporated into the ID code of the RFID tag. By doing so, the time for reading and writing the information into the memory can be saved, which also results in less power consumption compared with the traditional method. Fig. 4 illustrates the comparisons between the proposed method and the traditional method. Usually anti-collision technology is an important part of the RFID system. Nevertheless, it is not a severe problem in the environment monitoring of underground mine. Because RFID sensor tags are fixed, and there are less RFID sensor tags and readers compare with other health monitoring applications in the underground environment. In this proposed design, we present a anti-collision Q algorithm in EPC Gen2 protocol (Liu et al., 2016) for RFID tag by implementing with readercoverage collision avoidance arrangement (RCCAA) method (Nguyen et al., 2016).
3.2. Antenna design Dipole antenna and microstrip antenna are the nature choices for the design of UHF RFID antenna, but both of them exhibit the drawbacks of large size (Zhou et al., 2017; Xiang et al., 2015). The monopole antenna is an omnidirectional antenna which length can be decreased to half of the dipole antenna. Moreover, it can be meandered to further reduce the length of antenna. The conventional matching network employs capacitors and inductors to realize maximum energy transmission efficiency. However, the energy storage effect of these passive components limit the speed of energy transmission. Therefore, in this paper microstrip barron coupling line is adopted to solve this problem. Fig. 6 shows the design topology of the antenna. The right connection of microstrip barron coupling line is characterized as inductive and low impedance, the left connection is characterized as capacitive and high impedance. The impedance of selected RFID chip is 18.689-j171.908 Ω, which characterized as capacitive, therefore, the monopole antenna should be connected to the right side of the microstrip barron to realize impedance matching. The operation frequency of the designed antenna is 915 MHz and the detailed design parameters are listed in Table 1.
3. Proposed RFID sensor design 3.1. Circuit design The structure of the wireless sensor is introduced in Fig. 5(a), which contains power management, communication and digital section. The communication section consists of an antenna and an RFID chip. The antenna used to transmit and receive information wirelessly and the RFID chip is responsible for signal modulation and demodulation. The power management section contains an energy harvesting antenna, an matching network, a boosting rectifier and a voltage regulator. The regulator is adopted to offer stable DC voltage for the sub-blocks. The matching network consists of an off-chip inductor (L1) and an adjustable trimmer capacitor (C1) for optimum power transfer efficiency.
4. Results and discussion 4.1. Communication performances test in lab In order to verify the communication performances of the designed RFID sensor, this paper adopts a special RFID tester of VISN-R1200 from JX Instrumentation, China. The test environment is shown in Fig. 7. The Bosch VCL4003 climate box is used to test the temperature and humidity performances of the proposed wireless sensor. The RFID sensor tag designed in this paper is fabricated with discrete
Fig. 4. Sensor information storage method: (a) Traditional method; (b) Proposed method. 3
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Fig. 5. Proposed RFID sensor design: (a) RFID sensor architecture; (b) boosting rectifier schematic. Table 1 Parameters in antenna design. Parameters
L1
L2
L3
L4
L5
W1
W2
W3
W4
W5
W6
Size(mm)
62.2
55.4
51.0
24.6
2.4
11.4
9.2
9.2
8.4
6.3
14.7
Fig. 7. Test environment.
components. The base material of the sensor is FR4 and it covers the area of 12 × 8 cm2. In order to test the communication performances of the proposed RFID sensor, the distance between the sensor and the RFID reader is chosen to be 2 m, and the transmitting power of the reader is chosen to be 4 W. Fig. 8(a) shows the measured communication flow. The reader
Fig. 6. Proposed patch antenna design topology.
4
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Fig. 8. The measured communication flow: (a) corresponding waveform; (b) corresponding data.
firstly sent the Select command to the RFID sensor. After that, the Query command was sent to the sensor which then responsed to the reader with the RN16. The reader then sent out ACK command in order to gain the sensor’s ID information including the measured data. Lastly the reader sent the Req_RN command to obtain the Handle response. From the Fig. 8(b), we also can tell the unique ID from the measured EPC message, which proving the fast locating ability of the proposed RFID sensor. This paper also compares the time delay and power consumption required to transmit the same data under two data transmission modes. The measured results are shown in Table 2. From the data in the table, it can be seen that the time and power required to transmit the data directly into the sensor ID are only one third of the traditional method, which significantly improves the data transmission performance of the RFID sensor. The return loss characteristic (S11) refers to the intensity of electromagnetic wave reflected when the electromagnetic wave is transmitted through the antenna. In our test, the S11 performance can be directly measured by VISN-R1200. According to the stipulation of ISO18000-6c protocol, the S11 value in the operating frequency band should not be larger than −10 dB in the practical engineering application. Fig. 9 shows the measured S11 performance of the proposed RFID sensor. The lowest frequency of the measured S11 is corresponding to the operating frequency of the RFID sensor. It can be seen from the Fig. 9 that the operating frequency of the proposed RFID sensor is 915 MHz where the S11 is −18 dB.
Fig. 9. Measured S11 performancs of the sensor.
The label sensitivity is defined as the minimum received signal power that can activate the label. In order to test the sensitivity of the sensor label, the RFID tester generates a continuous 915 MHz signal for the RFID sensor. From Fig. 10, it can be seen that the minimum sensitivity of the sensor tag is −16 dBm, which corresponding to 10 m for the maximum working distance of the proposed RFID sensor at 2 W reader power. Compared with the traditional power management section of RFID tag (Wang et al., 2017), the sensitivity of the proposed RFID sensor has been increased by 7.5 dB. This is mainly due to the following two reasons: firstly, the power management section only uses a single-stage AC-DC rectifier circuit, and the reduction of rectifier diodes reduces the power loss of the rectifier circuit. Secondly, the input voltage of the DC-DC chip can be as low as 0.3 V, and the consumption current is less than 100 μA.
Table 2 Performance comparison of different data storage modes. Method
Storage location
Time delay (ms)
Power (µw)
Traditional method Proposed method
NVM Sensor ID
21.41 7.35
400 135
5
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Fig. 12. Influence of the proposed sensor embedded depth on received signal.
strength is decreased with the increasing of embedding depth. Within the depth of 60 cm, the received signal strength is larger than −70 dBm and the BER is 0. When the embedding depth increases further, the BER increases with the depth. Therefore, the maximum depth can reach 60 cm when the proposed RFID sensor was embedded in soil with 5% humidity. The maximum communication distance is an important parameter in evaluating the performance of RFID sensor. In this paper, a RFID tester is adopted to determine the maximum radiation distance. Because RFID tester and underground RFID sensor are respectively located in air and soil, the electromagnetic wave will be refracted and reflected in the soil-air interface. When RFID tester is vertical to underground RFID sensor, there is no electromagnetic wave refraction and the energy loss is the smallest. As shown in Fig. 13, the tester was 1 m high above the surface of earth, and the embedding depth of RFID sensor is 60 cm, the moisture content of the soil is 30%. The distance between the tester and the RFID sensor is increased in a step of 5 cm, and at each measurement point 1500 attempts were conducted to interact with the RFID sensor, the measured results are shown in Fig. 14. According to (Zhang et al., 2018), the success ratio should be over 80% for effective communication distance. When the distance is increased to 1.3 m, the success ratio decreases to 80%, hence the maximum communication distance is 1.3 m in this paper.
Fig. 10. Measured sensitivity of the sensor.
4.2. Communication performances test on site The practical communication performances of the wireless RFID sensor depends on the depth of the soil, so the effective embedding depth plays a decisive role in the later wireless sensor layout. Fig. 11 shows the test environment on site. The proposed RFID sensors were embedded into the soil within the depth range of 10–100 cm at a step of 10 cm. A RFID reader is set on the surface of the earth to test the signal strength and bit error rate of the RFID sensor. The measured moisture content of the soil on site is 5%. According to (Toyoda, 2004), the electromagnetic wave will be attenuated when transmitted in the soil, the received signal strength can be calculated by the following equation:
Pr = Pt + Gt + Gr − Lp , Lp = L0 + Ls , L0 = 32.4 + 20 lg d + 20 lg f
(1)
where Gr represents the gain of receiving antenna, Pr represents received signal strength, Pt represents the transmitted signal strength, Gt is the gain of transmitter antenna, L0 is the electromagnetic (EM) wave transmission power loss in free space, Ls is the extra transmission loss of EM wave in soil, d represents the distance between transmitter and receiver, f represents the operating frequency. Bit error rate (BER), which means the ration of error bit amount and total bit amount, is an indicator to evaluate the data transmission accuracy. In this paper, we send a 50 byte data package every two seconds, in every experiment 1000 data package are sent, and then calculate the BER when the RFID sensor are embedded in different depth, the experiment results are illustrated in Fig. 12. The received signal
4.3. Sensor performances test The soil moisture also has important influence on the performances of RFID sensor. In the experiment, the most common loam with 45% sand, 33% silt and 24% clay was selected as experimental soil. Measurement of soil moisture content by gravimetric and Six soil samples were employed to evaluate the impact of soil moisture on the RFID sensor. Each piece of soil sample was first dried to a constant mass in a drying oven at high temperatures, and then mixed with the tap water and then stirred evenly. The water content of soil is obtained by
Fig. 11. Test environment on site.
Fig. 13. Schematic diagram of maximum communication distance test. 6
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Fig. 16. Influence of soil moisture on received signal after improved sensor placement.
Fig. 14. RFID reading success rate of the RFID sensor under different embedded depths.
rapid speed of the patrol car will lead to the failure of the RFID sensor tags to transmit data to the RFID reader in time. The success rate of successful communication decreases. If the patrol car of RFID reader adopts automatic patrol inspection, it can realize all-weather and uninterrupted patrol inspection. If manual patrol inspection is adopted, the patrol time of one day is limited. In the experimental conditions, the artificial patrol mode is chosen, and the patrol time is set to 8 h a day. The faster the speed of the RFID reader patrol car, the larger the patrol area. However, too fast the speed will lead to a rapid decline in the success rate of communication. Fig. 17 draws the data between the speed of the RFID patrol vehicle and the communication success rate and the patrol coverage in a graph, and obtains the optimal solution among them. The Fig. 17 shows that the optimum speed of patrol car is 33 km/h when the communication success rate is above 90% and the coverage area is above 10 km2. To test the measurement accuracy of the proposed method, five samples of soil with different moisture content are employed for experiment. These five samples were numbered 1 to 5, and the proposed RFID sensor were used to measure the temperature and moisture every two minutes, and 20 times measurement were performed to calculate the average value. A calibrated temperature detector is employed to measure the temperature as reference. Moreover, the moisture content was measured by oven drying method as reference. Table 3 is the temperature measurement results and Table 4 is the moisture measurement results. It can be seen that the measurement error of temperature is within 1.5% and the measurement error of soil moisture is within 1.0%. Considering the measurement error and the interference during wireless communication, such a result suits our expectation well.
Fig. 15. Influence of soil moisture on received signal.
measuring the weight difference of container after drying. During the experiment, the RFID sensor was embedded in depth of 60 cm and the moisture of the six soil samples were set within 5%-30% at a step of 5%. As shown in Fig. 15, the received signal strength is dramatically decreased with the increasing of soil moisture content, the range of received signal strength is from −52 dBm to −71 dBm. When the moisture content is lower than 10%, the BER is 0 and when the moisture content is within 10%-30%, the BER is increased to be 0.18. Therefore, when the RFID sensor is embedded in depth of 60 cm, the moisture content should be lower than 30%. The measured soil conductivity is from 0.2 to 1.5 ms/cm under the soil moisture of 5–30%. From Fig. 15, it can be seen that the moisture has obvious impact on the wireless transmission performance of RFID sensor. In order to expand the application scenario of the proposed RFID sensor, a multiple sensor layout scheme was introduced. When the soil moisture content was larger than 30%, we put two RFID sensors at the same measurement point, the two sensors were placed as “T” shape and the measured results are illustrated in Fig. 16. When the soil moisture is larger than 30%, the BER is dramatically decreased, the maximum BER is 0.13. Hence different deployment methods of RFID sensor can influence the communication performances. Furthermore, the performances of RFID sensor can be improved through the methods such as using high-gain and high-performance RFID antennas to enhance the received signal intensity of tags (Akbari et al., 2016; Chen et al., 2016), but these will undoubtedly increase the design and manufacturing costs of sensors. The speed of the patrol car will affect the parameters of all levels of the system, and the most important one is the success rate of radio frequency signal reception and the network coverage. Radio Frequency signal transmission in soil with a certain humidity will lead to greater path loss, which will greatly increase the time for RFID sensor tags to return to the reader from receiving the radio frequency signal. The
Fig. 17. Influence of soil moisture on received signal after improved sensor placement. 7
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communication performance. The chloride ion concentration sensor composed of two electrodes is adopted to evaluate the chloride ion concentration in soil. In the test, the proposed passive sensor tag can successfully establish a communication range of about 1.3 m, and the relative errors when measuring temperature and moisture content in soil are 1.5% and 1.0% respectively when the embedding depth is 60 cm and the moisture content of soil is smaller than 30%. The optimum speed of patrol car is 33 km/h when the communication success rate is above 90% and the coverage area is above 10 km2.
Table 3 Measurement results of soil temperature. Sample number
Measured temperature (oC)
Reference Temperature (oC)
Relative error (%)
1 2 3 4 5
20.1 19.9 20 20.2 19.8
20 20.2 19.9 20.1 20
0.50 1.49 0.51 0.49 1.0
Declaration of Competing Interest Table 4 Measurement results of soil humidity. Sample number
Measured humidity (%)
Reference humidity (%)
Relative error (%)
1 2 3 4 5
7.83 10.9 16.1 20 25.2
7.9 10.8 16 20.2 25
0.89 0.93 0.62 1.0 0.8
No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed. Acknowledgement This work was supported by Natural Science Foundation of China (51767006, 51665014), Key Research and Development Plan of Jiangxi Province (20181BBE50019), Science and Technology Project of Education Department of Jiangxi Province (GJJ170378).
Table 5 Performances comparison between various soil monitoring methods. Method
Real-time
Cost
Life
Green
(Hedayati-Dezfooli and Leong, 2019) (Zhang et al., 2015) (Caicedo-Ortiz et al., 2018) (Talavera et al., 2017) This work
low high high high high
high high medium medium low
short long long long long
high medium low low high
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Table 5 compares the performances of different soil monitoring methods. Traditional soil sampling method (Hedayati-Dezfooli and Leong, 2019) shows high accuracy performance, but it is time-consuming and laborious, and is not suitable for real-time monitoring. The soil environmental monitoring system based on wired communication (Zhang et al., 2015) exhibits the advantages of huge data transmission and fast transmission speed, but its deployment process is complex and maintenance cost is high. WSN monitoring system (Caicedo-Ortiz et al., 2018) has the advantages of low cost and easy deployment, but the exposure of transceivers to the air will hinder agricultural activities. The WUSN system shows the advantages of strong concealment and high reliability (Talavera et al., 2017), but the nodes are powered by batteries, which is easy to cause soil environmental pollution. The proposed soil monitoring method based on RFID sensor can not only achieve fast locating, but also work in passive state. It is especially suitable for long-term monitoring of soil environment. 5. Conclusion A novel soil environment monitoring system is presented in this paper to monitor the temperature, moisture content and chloride ion concentration of soil. The monitoring system consists of RFID sensor, patrol car, farmland monitoring center and cloud platform. The proposed patrol car is responsible for collecting the information of embedded RFID sensors and communicate with the monitoring center through LoRa. The proposed wireless sensor contains a power management section, a communication section, and a digital section. The power management section aims to provide stable DC voltage for the rest part of the RFID sensor. A boost rectifier employing DC-DC charge pump is designed to transfer the harvested RF power into DC power, which has higher efficiency compared with traditional passive multiple stage rectifier. A novel monopole antenna with microstrip barron coupling line is designed to communicate with readers, it has smaller size compared with dipole antenna and microstrip antenna with the same 8
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Pub, Tokyo, pp. 156–157. Vaz, A., Ubarretxena, A., Zalbide, I., et al., 2010. Full passive UHF tag with a temperature sensor suitable for human body temperature monitoring. IEEE Trans. Circuits Syst. II Express Briefs 57 (2), 95–99. Wachowiak, M.P., Walters, D.F., Kovacs, J.M., et al., 2017. Visual analytics and remote sensing imagery to support community-based research for precision agriculture in emerging areas. Comput. Electron. Agric. 143, 149–164. Wang, T., He, Y., Luo, Q., et al., 2017. Self-powered RFID sensor tag for fault diagnosis and prognosis of transformer winding. IEEE Sens. J. 17 (19), 6418–6430. Xiang, W., Deng, F., Yong, H., et al., 2015. Design of a humidity sensor tag for passive wireless applications. Sensors 15 (10), 25564–25576. Zhang, C., Fu, Y., Deng, F., et al., 2018. Methane gas density monitoring and predicting based on RFID sensor tag and CNN algorithm. Electronics 7 (5), 69. Zhang, Y., Sui, B., Shen, H., et al., 2019. Mapping stocks of soil total nitrogen using remote sensing data: a comparison of random forest models with different predictors. Comput. Electron. Agric. 160, 23–30. Zhang, C.C., Zhu, H.H., She, J.K., et al., 2015. Quantitative evaluation of optical fiber/soil interfacial behavior and its implications for sensing fiber selection. IEEE Sens. J. 15 (5), 3059–3067. Zhou, S., Sheng, W., Deng, F., et al., 2017. A novel passive wireless sensing method for concrete chloride ion concentration monitoring. Sensors 17 (12), 2871.
214–226. Pichorim, S., Gomes, N., Batchelor, J., 2018. Two solutions of soil moisture sensing with RFID for landslide monitoring. Sensors 18 (2), 452. Ruiz-Garcia, L., Lunadei, L., 2011. The role of RFID in agriculture: applications, limitations and challenges. Comput. Electron. Agric. 79 (1), 42–50. Sample, A.P., Yeager, D.J., Powledge, P.S., et al., 2008. Design of an RFID-based batteryfree programmable sensing platform. IEEE Trans. Instrum. Meas. 57 (11), 2608–2615. Serrano-Finetti, E., Aliau-Bonet, C., López-Lapeña, O., et al., 2019. Cost-effective autonomous sensor for the long-term monitoring of water electrical conductivity of crop fields. Comput. Electron. Agric. 165, 104940. Su, Y., Wickramasinghe, A., Ranasinghe, D.C., 2015. Investigating sensor data retrieval schemes for multi-sensor passive RFID tags. In: Proc. IEEE Int. Conf. RFID, San Diego, CA, USA, Apr. pp. 158–165. Sun, Z., Akyildiz, I.F., 2010. Magnetic induction communications for wireless underground sensor networks. IEEE Trans. Antennas Propag. 58 (7), 2426–2435. Talavera, J.M., Tobón, L.E., Gómez, J.A., et al., 2017. Review of IoT applications in agroindustrial and environmental fields. Comput. Electron. Agric. 142, 283–297. Tao, W., Yigang, H., Tiancheng, S., et al., 2018. Transformer health management based on self-powered RFID sensor and multiple kernel RVM. IEEE Trans. Instrum. Meas. 68 (3), 1–11. Toyoda, K., 2004. New Agricultural Informatic Engineering: Spectroscopy. Youkendo
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Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag
Novel soil environment monitoring system based on RFID sensor and LoRa ⁎
T
Fangming Deng , Pengqi Zuo, Kaiyun Wen, Xiang Wu Department of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Soil Environment Monitoring RFID sensor LoRa
This work proposes a novel soil environment monitoring system based on RFID sensor and LoRa to realize long term and low cost monitoring. The monitoring system consists of patrol car, RFID sensor, farmland monitoring center and cloud platform. The proposed patrol car is responsible for collecting the information of embedded RFID sensors and communicate with the monitoring center through LoRa. The proposed RFID sensor is comprised of a power management section, a communication section, and a digital section. The power management section employs a whip antenna for energy harvesting, and a novel boost rectifier, consisting of a single stage rectifier and a DC-DC charge pump, is introduced to rectify the harvested RF power for high efficiency. A voltage regulator is used to generate a stable DC voltage supply. In the communication section, a novel monopole antenna is propose to minimize the size of antenna. It can be concluded from the experimental results that our RFID sensor can work under the maximum communication distance of 1.3 m. The errors of the measured temperature and moisture content are 1.5% and 1.0% respectively when the embedding depth of RFID sensor is 60 cm and the moisture content of soil is smaller than 30%. The optimum speed of patrol car is 33 km/h when the communication success rate is above 90% and the coverage area is above 10 km2.
1. Introduction Modern agriculture requires increased food production to satisfy the large global population. However, the existing productivity is still low due to the extensive agriculture technology. In this regard, the precision agriculture (PA) is introduced by many researchers to overcome this problem (Wachowiak et al., 2017). In the field of PA, the real-time monitoring of soil environment plays a significant role (Ko et al., 2014; Goh et al., 2014). Because of the dispersal of agricultural regions and thus the great various of terrains and environment conditions, how to collect the information of crop growth environment variables precisely and speedily is the main topic on the field of agricultural environment information technology (Khanna and Kaur, 2019). Wireless sensor network (WSN) was proposed to solve this issue (Ouyang et al., 2019). The WSN is composed of many sensor nodes distributed in the farm field, which can collaborate with each other to on-line measure the soil and environment conditions. This data can be processed by the senor nodes, Furthermore, these senor nodes can form the wireless network automatically to achieve the remote monitoring of the farmland environment (Zhang et al., 2019). WSN has been widely used in farmland environment, such as watering, cultivation, etc. Most WSNs are ground wireless sensor network systems designed for agricultural applications (Caicedo-Ortiz et al., 2018). In order to avoid the
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wireless signals transmitting in the soil, the sensors in the soil are connected with the wireless transceivers on the ground by wires. In recent years, the soil monitoring system based on LoRa arouse the great interest of researchers (Talavera et al., 2017). Compared with the conventional WSN technologies such as ZigBee, LoRa technology shows the advantages of large communication distance (up to 10 km), longe node life-time (up to 10 years) and high anti-interference performance. However these transceivers of the wireless sensors discussed above are exposed to the air and will block the agricultural activities. Furthermore, the transmission of wireless nodes will be influenced by environmental factors. Wireless underground sensor networks (WUSN) is a new technology for underground environmental monitoring and have become a hot topic in the agricultural environment monitoring field (Sun and Akyildiz, 2010). The wireless sensors buried in the ground transmit information through the soil, and the data is apperceived and collected when induction module perceives it. The WUSN exhibits many advantages, for example, great concealment, deployment and reliability, etc. However, the existing WUSN node always employs battery as power supply, once buried in the soil, it would be difficult to replace the battery and the obsolete battery would destroy soil environment. Radio frequency identification (RFID) technology is a non-contact automatic identification technology realized by radio frequency
Corresponding author. E-mail address: [email protected] (F. Deng).
https://doi.org/10.1016/j.compag.2019.105169 Received 26 September 2019; Received in revised form 4 December 2019; Accepted 19 December 2019 0168-1699/ © 2019 Published by Elsevier B.V.
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communication. In recent years the research on the RFID tag incorporated with sensors becomes hot topic. Due to the backscattering scheme, the RFID sensors show the advantages of simple architecture, low power and low cost (Deng et al., 2014). Furthermore, each RFID sensor has the unique identification (ID), which lead to fast locating when the fault occurs. The RFID tag always works under the power dissipation range of µW, hence many sensors, such as piezoceramicbased sensors (Serrano-Finetti et al., 2019; Furui and Gangbing, 2019), are not capable incorporated with RFID tag because of their high power conversion. Kuhn M F et al. (Kuhn et al., 2018) propose an antennabased RFID strain sensor for wireless structural health monitoring. Deng et al. (2018) present a chipless humidity sensor for low-cost environment monitoring. Tao et al., (2018), Wang et al. (2017) design a selfpowered RFID acceleration sensor for transformer condition monitoring. Donno et al. (2011) design a battery-assisted RFID tag integrated with temperature sensor, humidity sensor and light sensor for long-range communication distance. There are various design methods for RFID sensor. The RFID sensors based on inductive coupling technology operate on low or high frequency with the communication distance within 10 cm, but they exhibit high transferring efficiency and anti-metal interference performance (Pichler et al., 2017; Ruiz-Garcia and Lunadei, 2011). The RFID sensors operating in ultra-high frequency can obtain long communication distance (up to 10 m) and high data transmission rate. The chipless RFID sensors are introduced with the functionality of temperature and humidity sensing (Luvisi et al., 2016). This kind of wireless sensors are especially for low cost and low power application. RFID sensor based on integrated circuit (IC) technology has also garnered great interests. Different types of sensors can be integrated with the RFID circuit within one chip (Vaz et al., 2010), which can achieve the advantages in the fields of power consumption, circuit area and system stability. But limited by the fabrication process, only few kinds of sensors can be integrated. Recently, the RFID sensor based on discrete components has been widely employed. In this way, the RFID chip, radio frequency front-end circuit, micro- controller unit (MCU) and sensors are installed on the PCB board (Sample et al., 2008). This kind of RFID sensors has merits of low costs, short design time, easy to customize and extend. Hence, the RFID sensor made of discrete components is the best choice for underground soil environment monitoring. Some researchers have already carried out the research on RFID sensor for soil environment monitoring. Hamrita and Hoffacker (2005) develop a passive RFID sensor for soil temperature monitoring, however this RFID sensor works on 13.56 MHz and both of the communication speed and communication distance are very limited as discussed above. Hasan et al. (2013) design a RFID sensor on 915 MHz for pervasive soil moisture monitoring, however the designed RFID tag is still on the soil surface and connected with the humidity sensor underground through transmission line. In Pichorim et al. (2018) two RFID solutions for soil moisture monitoring are investigated, one is RFID sensors on surface, another one is the RFID tag on surface connected with underground humidity sensor. In Luvisi et al. (2016) an RFID temperature sensor is employed to monitor the underground soil solarization. However, the function of these RFID sensors discussed above is limited and only can measure the temperature or humidity of the soil environment. This work proposes a novel soil environment monitoring system based on RFID sensor and Lora. The patrol car collects the information of the RFID sensors embedded in soil and then communicates with the monitoring center through Lora. We also proves the feasibility of the passive RFID sensor for embedded soil monitoring. Compared with other soil monitoring techniques, the proposed method based on RFID sensor exhibits the advantages of passive operating mode and fast locating ability. Different from the current designs of RFID sensor for soil monitoring, the proposed RFID sensor is more versatile incorporating temperature, humidity and chloride ion sensing. The paper is organized as follows. The design of the proposed Soil
Fig. 1. Proposed monitoring system.
Environment Monitoring System is introduced in detail in Section 2. In Section 3, the design of the RFID sensor is introduced in detail. The experiment results are discussed in Section 4 and then the conclusion is made in Section 5. 2. Proposed monitoring system design Fig. 1 shows the proposed soil environment monitoring system. The designed RFID sensors are embedded in soil for high precision monitoring. The patrol car, incorporating RFID reader and LoRa communication function, is responsible for collecting the information of embedded RFID sensors and communicate with the monitoring center. The monitoring center will store the received data and upload it to the cloud platform through wired or cellular mobile network. The cloud platform can analyses the data and make the corresponding decisions. Fig. 2 shows the proposed design of patrol car. The proposed RFID reader of the patrol car consist of a separate RFID transmitter in the front of the car and a separate RFID receiver in the end of the car. The transmitter generates a continuous wave (CW) signal to power the RFID sensor and the receiver amplifies and demodulates the backscatter signal from the embedded RFID sensors. The incorporated LoRa block is responsible for communicating with the monitoring center and the communication distance can reach 10 km. Ordinarily an RFID system is composed of a RFID tag, an interrogator and the backstage management. The communication manner of the proposed system depends on the protocol of the electronic product code (EPC) generation2 (G2), as shown in Fig. 3 (EPCglobal Inc., 2008). Firstly, select instruction is sent from the reader by Carrier Wave (CW) to the RFID tag. Then, query instruction is received by the tag, which responds with the RN16. The reader then sends out the ACK command in order to achieve the query ID information and the CRC code from the
Fig. 2. Proposed patrol car. 2
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Fig. 3. RFID communication flow.
The power received by the on-board antenna cannot supply other blocks in the RFID sensor directly. Thus, the harvested power should be rectified and boosted to the demanding DC voltage by the boosting rectifier in Fig. 5(b). Then, the voltage regulator is adopted to generate a DC voltage for other components. The proposed boost rectifier circuit consists of a single stage rectifier and a DC-DC charge pump. The single stage rectifier circuit is composed of rectifier diodes D1, D2 and capacitor C2, which are employed to transfer the input RF power into a DC voltage. Since the rectification efficiency is determined by the threshold voltage of the diodes (Deng et al., 2014), this paper adopts the zero-biased Schottky diode SMS7630 for high efficiency. Compared with traditional multiple-stage rectifier circuit (Wang et al., 2017), a high efficiency DC-DC charge pump chip S-882Z24 is adopted following the single stage rectifier. The proposed charge pump is fabricated in silicon on insulator technology, which minimum input voltage is only 0.3 V. When the output voltage of load capacitor VR increases to VRH = 2.4 V, the S-882Z24 chip starts to active the digital section, when VR decreases to VRL = 1.85 V, the S882Z24 chip disconnects the output circuit and starts a new charging process.
tag. After that the reader continues to send Req_RN to acquire the handle response from the tag. It can be obtained from the communication flow, the sensor information cannot be processed directly, so we have to improve the communication mechanism. Generally, the sensor information can be stored in the non-volatile memory of the tag. However, it needs a relative long response time and consumes higher power during the read and write process (Su et al., 2015). Therefore we propose a new method that the measurement sensor data is incorporated into the ID code of the RFID tag. By doing so, the time for reading and writing the information into the memory can be saved, which also results in less power consumption compared with the traditional method. Fig. 4 illustrates the comparisons between the proposed method and the traditional method. Usually anti-collision technology is an important part of the RFID system. Nevertheless, it is not a severe problem in the environment monitoring of underground mine. Because RFID sensor tags are fixed, and there are less RFID sensor tags and readers compare with other health monitoring applications in the underground environment. In this proposed design, we present a anti-collision Q algorithm in EPC Gen2 protocol (Liu et al., 2016) for RFID tag by implementing with readercoverage collision avoidance arrangement (RCCAA) method (Nguyen et al., 2016).
3.2. Antenna design Dipole antenna and microstrip antenna are the nature choices for the design of UHF RFID antenna, but both of them exhibit the drawbacks of large size (Zhou et al., 2017; Xiang et al., 2015). The monopole antenna is an omnidirectional antenna which length can be decreased to half of the dipole antenna. Moreover, it can be meandered to further reduce the length of antenna. The conventional matching network employs capacitors and inductors to realize maximum energy transmission efficiency. However, the energy storage effect of these passive components limit the speed of energy transmission. Therefore, in this paper microstrip barron coupling line is adopted to solve this problem. Fig. 6 shows the design topology of the antenna. The right connection of microstrip barron coupling line is characterized as inductive and low impedance, the left connection is characterized as capacitive and high impedance. The impedance of selected RFID chip is 18.689-j171.908 Ω, which characterized as capacitive, therefore, the monopole antenna should be connected to the right side of the microstrip barron to realize impedance matching. The operation frequency of the designed antenna is 915 MHz and the detailed design parameters are listed in Table 1.
3. Proposed RFID sensor design 3.1. Circuit design The structure of the wireless sensor is introduced in Fig. 5(a), which contains power management, communication and digital section. The communication section consists of an antenna and an RFID chip. The antenna used to transmit and receive information wirelessly and the RFID chip is responsible for signal modulation and demodulation. The power management section contains an energy harvesting antenna, an matching network, a boosting rectifier and a voltage regulator. The regulator is adopted to offer stable DC voltage for the sub-blocks. The matching network consists of an off-chip inductor (L1) and an adjustable trimmer capacitor (C1) for optimum power transfer efficiency.
4. Results and discussion 4.1. Communication performances test in lab In order to verify the communication performances of the designed RFID sensor, this paper adopts a special RFID tester of VISN-R1200 from JX Instrumentation, China. The test environment is shown in Fig. 7. The Bosch VCL4003 climate box is used to test the temperature and humidity performances of the proposed wireless sensor. The RFID sensor tag designed in this paper is fabricated with discrete
Fig. 4. Sensor information storage method: (a) Traditional method; (b) Proposed method. 3
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Fig. 5. Proposed RFID sensor design: (a) RFID sensor architecture; (b) boosting rectifier schematic. Table 1 Parameters in antenna design. Parameters
L1
L2
L3
L4
L5
W1
W2
W3
W4
W5
W6
Size(mm)
62.2
55.4
51.0
24.6
2.4
11.4
9.2
9.2
8.4
6.3
14.7
Fig. 7. Test environment.
components. The base material of the sensor is FR4 and it covers the area of 12 × 8 cm2. In order to test the communication performances of the proposed RFID sensor, the distance between the sensor and the RFID reader is chosen to be 2 m, and the transmitting power of the reader is chosen to be 4 W. Fig. 8(a) shows the measured communication flow. The reader
Fig. 6. Proposed patch antenna design topology.
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Fig. 8. The measured communication flow: (a) corresponding waveform; (b) corresponding data.
firstly sent the Select command to the RFID sensor. After that, the Query command was sent to the sensor which then responsed to the reader with the RN16. The reader then sent out ACK command in order to gain the sensor’s ID information including the measured data. Lastly the reader sent the Req_RN command to obtain the Handle response. From the Fig. 8(b), we also can tell the unique ID from the measured EPC message, which proving the fast locating ability of the proposed RFID sensor. This paper also compares the time delay and power consumption required to transmit the same data under two data transmission modes. The measured results are shown in Table 2. From the data in the table, it can be seen that the time and power required to transmit the data directly into the sensor ID are only one third of the traditional method, which significantly improves the data transmission performance of the RFID sensor. The return loss characteristic (S11) refers to the intensity of electromagnetic wave reflected when the electromagnetic wave is transmitted through the antenna. In our test, the S11 performance can be directly measured by VISN-R1200. According to the stipulation of ISO18000-6c protocol, the S11 value in the operating frequency band should not be larger than −10 dB in the practical engineering application. Fig. 9 shows the measured S11 performance of the proposed RFID sensor. The lowest frequency of the measured S11 is corresponding to the operating frequency of the RFID sensor. It can be seen from the Fig. 9 that the operating frequency of the proposed RFID sensor is 915 MHz where the S11 is −18 dB.
Fig. 9. Measured S11 performancs of the sensor.
The label sensitivity is defined as the minimum received signal power that can activate the label. In order to test the sensitivity of the sensor label, the RFID tester generates a continuous 915 MHz signal for the RFID sensor. From Fig. 10, it can be seen that the minimum sensitivity of the sensor tag is −16 dBm, which corresponding to 10 m for the maximum working distance of the proposed RFID sensor at 2 W reader power. Compared with the traditional power management section of RFID tag (Wang et al., 2017), the sensitivity of the proposed RFID sensor has been increased by 7.5 dB. This is mainly due to the following two reasons: firstly, the power management section only uses a single-stage AC-DC rectifier circuit, and the reduction of rectifier diodes reduces the power loss of the rectifier circuit. Secondly, the input voltage of the DC-DC chip can be as low as 0.3 V, and the consumption current is less than 100 μA.
Table 2 Performance comparison of different data storage modes. Method
Storage location
Time delay (ms)
Power (µw)
Traditional method Proposed method
NVM Sensor ID
21.41 7.35
400 135
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Fig. 12. Influence of the proposed sensor embedded depth on received signal.
strength is decreased with the increasing of embedding depth. Within the depth of 60 cm, the received signal strength is larger than −70 dBm and the BER is 0. When the embedding depth increases further, the BER increases with the depth. Therefore, the maximum depth can reach 60 cm when the proposed RFID sensor was embedded in soil with 5% humidity. The maximum communication distance is an important parameter in evaluating the performance of RFID sensor. In this paper, a RFID tester is adopted to determine the maximum radiation distance. Because RFID tester and underground RFID sensor are respectively located in air and soil, the electromagnetic wave will be refracted and reflected in the soil-air interface. When RFID tester is vertical to underground RFID sensor, there is no electromagnetic wave refraction and the energy loss is the smallest. As shown in Fig. 13, the tester was 1 m high above the surface of earth, and the embedding depth of RFID sensor is 60 cm, the moisture content of the soil is 30%. The distance between the tester and the RFID sensor is increased in a step of 5 cm, and at each measurement point 1500 attempts were conducted to interact with the RFID sensor, the measured results are shown in Fig. 14. According to (Zhang et al., 2018), the success ratio should be over 80% for effective communication distance. When the distance is increased to 1.3 m, the success ratio decreases to 80%, hence the maximum communication distance is 1.3 m in this paper.
Fig. 10. Measured sensitivity of the sensor.
4.2. Communication performances test on site The practical communication performances of the wireless RFID sensor depends on the depth of the soil, so the effective embedding depth plays a decisive role in the later wireless sensor layout. Fig. 11 shows the test environment on site. The proposed RFID sensors were embedded into the soil within the depth range of 10–100 cm at a step of 10 cm. A RFID reader is set on the surface of the earth to test the signal strength and bit error rate of the RFID sensor. The measured moisture content of the soil on site is 5%. According to (Toyoda, 2004), the electromagnetic wave will be attenuated when transmitted in the soil, the received signal strength can be calculated by the following equation:
Pr = Pt + Gt + Gr − Lp , Lp = L0 + Ls , L0 = 32.4 + 20 lg d + 20 lg f
(1)
where Gr represents the gain of receiving antenna, Pr represents received signal strength, Pt represents the transmitted signal strength, Gt is the gain of transmitter antenna, L0 is the electromagnetic (EM) wave transmission power loss in free space, Ls is the extra transmission loss of EM wave in soil, d represents the distance between transmitter and receiver, f represents the operating frequency. Bit error rate (BER), which means the ration of error bit amount and total bit amount, is an indicator to evaluate the data transmission accuracy. In this paper, we send a 50 byte data package every two seconds, in every experiment 1000 data package are sent, and then calculate the BER when the RFID sensor are embedded in different depth, the experiment results are illustrated in Fig. 12. The received signal
4.3. Sensor performances test The soil moisture also has important influence on the performances of RFID sensor. In the experiment, the most common loam with 45% sand, 33% silt and 24% clay was selected as experimental soil. Measurement of soil moisture content by gravimetric and Six soil samples were employed to evaluate the impact of soil moisture on the RFID sensor. Each piece of soil sample was first dried to a constant mass in a drying oven at high temperatures, and then mixed with the tap water and then stirred evenly. The water content of soil is obtained by
Fig. 11. Test environment on site.
Fig. 13. Schematic diagram of maximum communication distance test. 6
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Fig. 16. Influence of soil moisture on received signal after improved sensor placement.
Fig. 14. RFID reading success rate of the RFID sensor under different embedded depths.
rapid speed of the patrol car will lead to the failure of the RFID sensor tags to transmit data to the RFID reader in time. The success rate of successful communication decreases. If the patrol car of RFID reader adopts automatic patrol inspection, it can realize all-weather and uninterrupted patrol inspection. If manual patrol inspection is adopted, the patrol time of one day is limited. In the experimental conditions, the artificial patrol mode is chosen, and the patrol time is set to 8 h a day. The faster the speed of the RFID reader patrol car, the larger the patrol area. However, too fast the speed will lead to a rapid decline in the success rate of communication. Fig. 17 draws the data between the speed of the RFID patrol vehicle and the communication success rate and the patrol coverage in a graph, and obtains the optimal solution among them. The Fig. 17 shows that the optimum speed of patrol car is 33 km/h when the communication success rate is above 90% and the coverage area is above 10 km2. To test the measurement accuracy of the proposed method, five samples of soil with different moisture content are employed for experiment. These five samples were numbered 1 to 5, and the proposed RFID sensor were used to measure the temperature and moisture every two minutes, and 20 times measurement were performed to calculate the average value. A calibrated temperature detector is employed to measure the temperature as reference. Moreover, the moisture content was measured by oven drying method as reference. Table 3 is the temperature measurement results and Table 4 is the moisture measurement results. It can be seen that the measurement error of temperature is within 1.5% and the measurement error of soil moisture is within 1.0%. Considering the measurement error and the interference during wireless communication, such a result suits our expectation well.
Fig. 15. Influence of soil moisture on received signal.
measuring the weight difference of container after drying. During the experiment, the RFID sensor was embedded in depth of 60 cm and the moisture of the six soil samples were set within 5%-30% at a step of 5%. As shown in Fig. 15, the received signal strength is dramatically decreased with the increasing of soil moisture content, the range of received signal strength is from −52 dBm to −71 dBm. When the moisture content is lower than 10%, the BER is 0 and when the moisture content is within 10%-30%, the BER is increased to be 0.18. Therefore, when the RFID sensor is embedded in depth of 60 cm, the moisture content should be lower than 30%. The measured soil conductivity is from 0.2 to 1.5 ms/cm under the soil moisture of 5–30%. From Fig. 15, it can be seen that the moisture has obvious impact on the wireless transmission performance of RFID sensor. In order to expand the application scenario of the proposed RFID sensor, a multiple sensor layout scheme was introduced. When the soil moisture content was larger than 30%, we put two RFID sensors at the same measurement point, the two sensors were placed as “T” shape and the measured results are illustrated in Fig. 16. When the soil moisture is larger than 30%, the BER is dramatically decreased, the maximum BER is 0.13. Hence different deployment methods of RFID sensor can influence the communication performances. Furthermore, the performances of RFID sensor can be improved through the methods such as using high-gain and high-performance RFID antennas to enhance the received signal intensity of tags (Akbari et al., 2016; Chen et al., 2016), but these will undoubtedly increase the design and manufacturing costs of sensors. The speed of the patrol car will affect the parameters of all levels of the system, and the most important one is the success rate of radio frequency signal reception and the network coverage. Radio Frequency signal transmission in soil with a certain humidity will lead to greater path loss, which will greatly increase the time for RFID sensor tags to return to the reader from receiving the radio frequency signal. The
Fig. 17. Influence of soil moisture on received signal after improved sensor placement. 7
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communication performance. The chloride ion concentration sensor composed of two electrodes is adopted to evaluate the chloride ion concentration in soil. In the test, the proposed passive sensor tag can successfully establish a communication range of about 1.3 m, and the relative errors when measuring temperature and moisture content in soil are 1.5% and 1.0% respectively when the embedding depth is 60 cm and the moisture content of soil is smaller than 30%. The optimum speed of patrol car is 33 km/h when the communication success rate is above 90% and the coverage area is above 10 km2.
Table 3 Measurement results of soil temperature. Sample number
Measured temperature (oC)
Reference Temperature (oC)
Relative error (%)
1 2 3 4 5
20.1 19.9 20 20.2 19.8
20 20.2 19.9 20.1 20
0.50 1.49 0.51 0.49 1.0
Declaration of Competing Interest Table 4 Measurement results of soil humidity. Sample number
Measured humidity (%)
Reference humidity (%)
Relative error (%)
1 2 3 4 5
7.83 10.9 16.1 20 25.2
7.9 10.8 16 20.2 25
0.89 0.93 0.62 1.0 0.8
No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed. Acknowledgement This work was supported by Natural Science Foundation of China (51767006, 51665014), Key Research and Development Plan of Jiangxi Province (20181BBE50019), Science and Technology Project of Education Department of Jiangxi Province (GJJ170378).
Table 5 Performances comparison between various soil monitoring methods. Method
Real-time
Cost
Life
Green
(Hedayati-Dezfooli and Leong, 2019) (Zhang et al., 2015) (Caicedo-Ortiz et al., 2018) (Talavera et al., 2017) This work
low high high high high
high high medium medium low
short long long long long
high medium low low high
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Table 5 compares the performances of different soil monitoring methods. Traditional soil sampling method (Hedayati-Dezfooli and Leong, 2019) shows high accuracy performance, but it is time-consuming and laborious, and is not suitable for real-time monitoring. The soil environmental monitoring system based on wired communication (Zhang et al., 2015) exhibits the advantages of huge data transmission and fast transmission speed, but its deployment process is complex and maintenance cost is high. WSN monitoring system (Caicedo-Ortiz et al., 2018) has the advantages of low cost and easy deployment, but the exposure of transceivers to the air will hinder agricultural activities. The WUSN system shows the advantages of strong concealment and high reliability (Talavera et al., 2017), but the nodes are powered by batteries, which is easy to cause soil environmental pollution. The proposed soil monitoring method based on RFID sensor can not only achieve fast locating, but also work in passive state. It is especially suitable for long-term monitoring of soil environment. 5. Conclusion A novel soil environment monitoring system is presented in this paper to monitor the temperature, moisture content and chloride ion concentration of soil. The monitoring system consists of RFID sensor, patrol car, farmland monitoring center and cloud platform. The proposed patrol car is responsible for collecting the information of embedded RFID sensors and communicate with the monitoring center through LoRa. The proposed wireless sensor contains a power management section, a communication section, and a digital section. The power management section aims to provide stable DC voltage for the rest part of the RFID sensor. A boost rectifier employing DC-DC charge pump is designed to transfer the harvested RF power into DC power, which has higher efficiency compared with traditional passive multiple stage rectifier. A novel monopole antenna with microstrip barron coupling line is designed to communicate with readers, it has smaller size compared with dipole antenna and microstrip antenna with the same 8
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