Accuracy calibration and evaluation of capacitance-based soil moisture sensors for a variety of soil properties

Li, Bingze; Wang, Chunmei; Ma, Ming; Li, Lei; Feng, Zhuangzhuang; Ding, Tianyu; Li, Xiaofeng; Jiang, Tao; Li, Xiaojie; Zheng, Xingming

doi:10.1016/j.agwat.2022.107913

Cited by 15 publications

(13 citation statements)

References 30 publications

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“…Gaussian noise and uniform noise are two common noise patterns that are widely chosen in the studies and could be used to check the performance of proposed algorithms. Non‐random error with Gaussian noise is used to simulate the systematic bias in observations brought by soil moisture sensors that realistically occur when measuring the soil water content (B. Li et al., 2022; Singh et al., 2018).…”

Section: Resultsmentioning

confidence: 99%

“…Nevertheless, an important but unresolved problem is that considerable observational data is required in current algorithms and is quite sensitive to measurement errors (Ghorbani et al., 2021; Song et al., 2022). However, measuring soil moisture data is still expensive and generally collected sparsely with measurement errors in field conditions (e.g., B. Li et al., 2022). These practical situations and the shortcomings of the algorithms bring considerable challenges to the application of this method.…”

Section: Introductionmentioning

confidence: 99%

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Reconstructing the Unsaturated Flow Equation From Sparse and Noisy Data: Leveraging the Synergy of Group Sparsity and Physics‐Informed Deep Learning

Song

Shi

et al. 2023

Water Resources Research

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Precise characterization of soil moisture dynamics in the vadose zone is fundamental for numerous science and engineering applications, such as soil mechanics, agricultural water management, and hydrological cycle simulation (Vereecken et al., 2015(Vereecken et al., , 2022. Accurate model establishment for soil moisture flow is the key to characterizing soil moisture dynamics.With contact and non-contact soil moisture sensor technology improved (e.g., W. Dorigo et al., 2021;W. A. Dorigo et al., 2011), it is increasingly convenient to access soil moisture observational data. Meanwhile, developments in data-driven algorithms provide opportunities to leverage these rich data, gaining us new insights into modeling, simulating, and understanding soil moisture dynamics (Ghanbarian & Pachepsky, 2022). Most current practices in modeling soil moisture flow have leveraged data-driven approaches. Their core idea is basically to use algorithms to establish mapping to approximate the latent soil moisture dynamics involving traditional machine learning (e.g.,

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reconstructing the Unsaturated Flow Equation From Sparse and Noisy Data: Leveraging the Synergy of Group Sparsity and Physics‐Informed Deep Learning

Song

Shi

et al. 2023

Water Resources Research

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“…In general, the role of the IoT is to connect agricultural animals or plants, agricultural equipment, and agricultural facilities to a network through wireless or wired communications, so that every animal and plant can be managed accurately to achieve the best possible yield and minimize costs [ 6 ]. In recent years, IoT technology has been applied to many areas of agriculture, including agricultural environmental monitoring, greenhouse gas emission control, water-saving irrigation, weather monitoring, product safety and traceability, and intelligent equipment diagnosis and management [ 7 , 8 , 9 ]. Its main goal is to enable the combination of historical analyzed data and real-time inspection data to provide a more accurate model and optimization solution, with the ultimate aim of achieving sustainability in future intelligent agriculture.…”

Section: Introductionmentioning

confidence: 99%

Internet-of-Things-Based Multiple-Sensor Monitoring System for Soil Information Diagnosis Using a Smartphone

2023

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The rise of Internet of Things (IoT) technology has moved the digital world in a new direction and is considered the third wave of the information industry. To meet the current growing demand for food, the agricultural industry should adopt updated technologies and smart agriculture based on the IoT which will strongly enable farmers to reduce waste and increase productivity. This research presents a novel system for the application of IoT technology in agricultural soil measurements, which consists of multiple sensors (temperature and moisture), a micro-processor, a microcomputer, a cloud platform, and a mobile phone application. The wireless sensors can collect and transmit soil information in real time with a high speed, while the mobile phone app uses the cloud platform as a monitoring center. A low power consumption is specified in the hardware and software, and a modular power supply and time-saving algorithm are adopted to improve the energy effectiveness of the nodes. Meanwhile, a novel soil information prediction strategy was explored based on the deep Q network (DQN) reinforcement learning algorithm. Following the weighted combination of a bidirectional long short-term memory, online sequential extreme learning machine, and parallel extreme machine learning, the DQN Bi-OS-P prediction model was obtained. The proposed data acquisition system achieved a long-term stable and reliable collection of time-series soil data with equal intervals and provided an accurate dataset for the precise diagnosis of soil information. The RMSE, MAE, and MAPE of the DQN Bi-OS-P were all reduced, and the R2 was improved by 0.1% when compared to other methods. This research successfully implemented the smart soil system and experimentally showed that the time error between the value displayed on the mobile phone app and its exact acquisition moment was no more than 3 s, proving that mobile applications can be effectively used for the real-time monitoring of soil quality and conditions in wireless multi-sensing based on the Internet of Things.

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“…ECH2O-5TE sensors (METER Environment, formerly Decagon Devices, Inc., Pullman, WA, USA) which are increasingly used around the world, simplify the process because they are economical, smaller in size, they ensure fast and easy installation, and provide three measurements in one, namely, the bulk EC, VWC, and the soil temperature; thus, these sensors were selected for this study. Some studies [1][2][3][4][5][6] have reported that 5TE sensors have high suitability in agricultural fields when using the proposed calibration methods. The main perturbation of these sensors is that their measurements are only accurate in a limited range of soil environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

“…The main perturbation of these sensors is that their measurements are only accurate in a limited range of soil environmental conditions. Numerous studies [1][2][3][4][5][6] have concluded that none of the low-cost sensors possess a level of performance consistent with the manufacturer's specifications. Further studies [6][7][8][9] attributed this to the fact that these sensors (including ECH2O-5TE) are affected by the conditions of the soil environment.…”

Section: Introductionmentioning

confidence: 99%

Calibration of Soil Moisture Sensors (ECH2O-5TE) in Hot and Saline Soils with New Empirical Equation

Louki

Al-Omran

2022

Agronomy

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The use of soil moisture sensors is a practice applied to improve irrigation water management. ECH2O-5TE sensors are increasingly being used to estimate the volumetric water content (VWC). In view of the importance of the efficient use of these devices, six main factors affecting the accuracy of sensor measurements were studied: soil moisture levels, soil salinity, temperature, organic matter, soil texture, and bulk density. The study showed that the electrical conductivity of the soil and the temperature independently affect the measurements, while the influence of other factors interferes with that of salinity. This study found that the sensor measurements of the VWC were closest to the actual VWC at the soil ECe and temperatures of 2.42 dS m−1 and 25 °C, with root-mean-square errors (RMSE) of 0.003 and 0.004 m3 m−3. Otherwise, the measured VWC values of these sensor readouts significantly overestimated the actual VWC, with an increasing soil ECe and/or producing temperatures higher than the stated values, and vice versa. Given the importance of these sensors for obtaining accurate measurements for water management, a simplified empirical equation was derived using the data collected from a wide range of measurements to correct the influences of electrical conductivity and temperature on the measurement accuracy of the sensors, while considering the influence of the soil’s texture. Thus, the following equation was proposed: ϴva = θvsaECe2+bECe+c+dT2+eT+f−1. The results concerning the measurement of different VWC levels via these sensors and the proposed L&O correction equation were compared with the corresponding actual VWC values determined by gravimetric methods. It was found that this empirical equation reduced the differences in the RMSE between the sensor readings for the VWC and the actual VWC from 0.072 and 0.252 to 0.030 and 0.030 m3 m−3 for 1 and 5 dS m−1, respectively, with respect to the EC’s influence at 25 °C and reduced the RMSE from 0.053 and 0.098 to 0.007 and 0.011 at 3 and 50 °C, respectively, regarding the effect of the temperature at EC 2.42 dS m−1 at different levels of the actual VWC values.

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Accuracy calibration and evaluation of capacitance-based soil moisture sensors for a variety of soil properties

Cited by 15 publications

References 30 publications

Reconstructing the Unsaturated Flow Equation From Sparse and Noisy Data: Leveraging the Synergy of Group Sparsity and Physics‐Informed Deep Learning

Reconstructing the Unsaturated Flow Equation From Sparse and Noisy Data: Leveraging the Synergy of Group Sparsity and Physics‐Informed Deep Learning

Internet-of-Things-Based Multiple-Sensor Monitoring System for Soil Information Diagnosis Using a Smartphone

Calibration of Soil Moisture Sensors (ECH2O-5TE) in Hot and Saline Soils with New Empirical Equation

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