Sensor-specific Calibration Research Articles

Enhancing greenhouse strawberry irrigation: integrating IoT technologies and low-cost moisture sensors in substrate

The integration of Internet of Things (IoT) technologies, particularly sensor-based systems, is revolutionizing agriculture, including strawberry cultivation in greenhouses. Furthermore, the use of wireless communication technologies, alongside efficient data storage and visualization platforms, can enhance monitoring and decision-making capabilities. This study aimed to calibrate and verify the real-time operation of the low-cost SKU SEN0193 V2.0 sensor and the SHT20 in strawberry cultivation substrate, evaluating IoT technologies for data transmission, including LoRa, InfluxDB for storage, and Grafana for visualization and monitoring. The adaptability and accuracy of the SHT20 and SKU SEN0193 V2.0 sensors were tested in laboratory and greenhouse strawberry crops. Water retention curves in the substrate were determined using the Haines Funnel, while recording sensor measurements simultaneously for calibration. For real-time monitoring, sensors were placed in two slabs with strawberry plants in a greenhouse. InfluxDB stored the data, and Grafana visualized it. While the SHT20 sensor showed limited suitability for substrates due to its lack of response to moisture variations, SKU SEN0193 V2.0 produced excellent results, detecting changes in moisture content in both laboratory and greenhouse tests. Sensor-specific calibration for greenhouse slabs is crucial. The utilization of data storage and visualization tools like InfluxDB and Grafana facilitated real-time data manipulation, demonstrating their potential for enhancing irrigation management in greenhouse strawberry production.

Design, fabrication, and calibration of the Building EnVironment and Occupancy (BEVO) Beacon: A rapidly-deployable and affordable indoor environmental quality monitor

Indoor Air Quality (IAQ) monitoring is essential to assess occupant exposure to the wide range of pollutants present in indoor environments. Accurate research-grade monitors are often used to monitor IAQ but the expense and logistics associated with these devices often limits the temporal and spatial scale of monitoring efforts. More affordable consumer-grade sensors – frequently referred to as low-cost sensors – can provide insight into IAQ conditions across greater scales but their accuracy and calibration requirements need further evaluation. In this paper, we present the Building EnVironment and Occupancy (BEVO) Beacon. The BEVO Beacon is entirely open-source, including the software, hardware, and design schematics which are all provided on GitHub. We created 20 of these standalone, stationary devices which measure up to 24 parameters at a one-minute resolution of which we focus on carbon dioxide, carbon monoxide, total volatile organic compounds, temperature, and size-resolved particulate matter. We investigated the efficacy of two different calibration approaches – device-specific and environment-averaged – for these sensors as well as also provide an extensive discussion considerations for each of the sensors. Calibrated sensors performed well when compared to reference monitors or calibrated gas standards. The CO sensors yielded the best agreement (r2=0.98-0.99), followed by temperature (r2=0.89-0.99), CO2 (r2=0.62-0.99), and PM2.5 (r2=-0.13-0.91). In all cases, the device-specific calibration approach yielded the most accurate results. We evaluated our devices through a successful 11-week field study where we monitored the IAQ in participants’ bedrooms. The work we present on consumer-grade sensors adds to the existing literature by considering sensor-specific calibration techniques and analysis. The BEVO Beacon adds to the successful line of similarly developed devices by providing an open-source framework that researchers can readily adapt and modify to their own applications.

Deep learning approach for hyperspectral image demosaicking, spectral correction and high-resolution RGB reconstruction

ABSTRACT Hyperspectral imaging is one of the most promising techniques for intraoperative tissue characterisation. Snapshot mosaic cameras, which can capture hyperspectral data in a single exposure, have the potential to make a real-time hyperspectral imaging system for surgical decision-making possible. However, optimal exploitation of the captured data requires solving an ill-posed demosaicking problem and applying additional spectral corrections. In this work, we propose a supervised learning-based image demosaicking algorithm for snapshot hyperspectral images. Due to the lack of publicly available medical images acquired with snapshot mosaic cameras, a synthetic image generation approach is proposed to simulate snapshot images from existing medical image datasets captured by high-resolution, but slow, hyperspectral imaging devices. Image reconstruction is achieved using convolutional neural networks for hyperspectral image super-resolution, followed by spectral correction using a sensor-specific calibration matrix. The results are evaluated both quantitatively and qualitatively, showing clear improvements in image quality compared to a baseline demosaicking method using linear interpolation. Moreover, the fast processing time of 45 ms of our algorithm to obtain super-resolved RGB or oxygenation saturation maps per image for a state-of-the-art snapshot mosaic camera demonstrates the potential for its seamless integration into real-time surgical hyperspectral imaging applications.

Calibration of Low-Cost NO2 Sensors through Environmental Factor Correction.

Low-cost air quality sensors (LCSs) have become more widespread due to their low cost and increased capabilities; however, to supplement more traditional air quality networks, the performance of these LCSs needs to be validated. This study focused on NO2 measurements from eight Clarity Node-S sensors and used various environmental factors to calibrate the LCSs. To validate the calibration performance, we calculated the root-mean-square error (RMSE), mean absolute error (MAE), R2, and slope compared to reference measurements. Raw results from six of these sensors were comparable to those reported for other NO2 LCSs; however, two of the evaluated LCSs had RMSE values ~20 ppb higher than the other six LCSs. By applying a sensor-specific calibration that corrects for relative humidity, temperature, and ozone, this discrepancy was mitigated. In addition, this calibration improved the RMSE, MAE, R2, and slope of all eight LCS compared to the raw data. It should be noted that relatively stable environmental conditions over the course of the LCS deployment period benefited calibration performance over time. These results demonstrate the importance of developing LCS calibration models for individual sensors that consider pertinent environmental factors.

Evaluation of Soil Moisture Sensing Technologies in Silt Loam and Loamy Sand Soils: Assessment of Performance, Temperature Sensitivity, and Site- and Sensor-Specific Calibration Functions

<b>Highlights</b> <list list-type=bullet><list-item> Nine soil moisture sensors were evaluated in two soil types under different installation orientations. </list-item><list-item> Sensor-specific and soil-specific calibration functions were developed and validated. </list-item><list-item> Sensor performance improved substantially (31% to 89%) after calibration. </list-item><list-item> On average, sensor performance was 67% better in loamy sand than in silt loam soil. </list-item></list> <b>Abstract</b>. Reliable soil moisture information is vital for optimal irrigation management, farm-level agronomic decision-making, hydrologic studies, and cropping systems modeling. A wide range of soil moisture sensing technologies is commercially available, but their performance must be evaluated for diverse conditions of use. In this research, we investigated nine soil moisture sensors based on time-domain reflectometry, capacitance, and electrical resistance principles in production field conditions with two installation orientations, i.e., vertical (V) and horizontal (H), in two soils (silt loam and loamy sand) and two growing seasons (2017 and 2018). Performance parameters deduced from the 2017 datasets revealed that sensor type and soil type significantly affected the soil moisture sensor performance under factory calibration (F.C.); however, sensor installation orientation did not. Thus, the sensors were only evaluated based on their performance in horizontal orientation in both soils. Precision and accuracy were considered targets to assist in appropriate sensor selection. To improve sensor accuracy, site-specific calibration (S.S.C.) functions were developed and validated using independent datasets from 2018. Considering mean bias error (MBE), all sensors overestimated volumetric soil water content (θv) in both soils, with the exception of TEROS 21 (MPS-6) in silt loam and JD probe in loamy sand. On average, sensor performance was 67% better in loamy sand than in silt loam. Overall, the sensors showed higher precision in silt loam (R2 = 0.53 to 0.93) than in loamy sand (R2 = 0.25 to 0.82). Substantial post-S.S.C. improvement (32% to 89%) was observed in all sensors‘ performance relative to F.C. in silt loam. In loamy sand, while most sensors performed reasonably well with F.C., considerable improvements (28% to 85%) were observed with S.S.C. Significant differences (p < 0.05) were observed in sensors‘ sensitivity to soil temperature (Tsoil), which ranged from 14°C to 23°C in silt loam and from 14°C to 25°C loamy sand during the experiments. The CS655, 10HS, 5TE, and TEROS 21 (MPS-6) sensors showed significant (p < 0.05) sensitivity to Tsoil fluctuations, with Tsoil explaining a maximum of 17% of the variance observed in sensor performance. No statistically significant (p > 0.05) sensitivity was detected for any of the sensors in loamy sand. TEROS 21 (MPS-6) had the highest sensitivity to Tsoil with a slope of -4.25. In contrast, while statistically significant (p < 0.05), 5TE was the least sensitive to Tsoil variability with a slope of 1.81. The information, data, and analyses presented here can be instrumental for informed sensor selection and use in decision-making in production fields with similar soil textures and soil water regimes.

Body-Worn IMU Human Skeletal Pose Estimation Using a Factor Graph-Based Optimization Framework.

Traditionally, inertial measurement units- (IMU) based human joint angle estimation requires a priori knowledge about sensor alignment or specific calibration motions. Furthermore, magnetometer measurements can become unreliable indoors. Without magnetometers, however, IMUs lack a heading reference, which leads to unobservability issues. This paper proposes a magnetometer-free estimation method, which provides desirable observability qualities under joint kinematics that sufficiently excite the lower body degrees of freedom. The proposed lower body model expands on the current self-calibrating human-IMU estimation literature and demonstrates a novel knee hinge model, the inclusion of segment length anthropometry, segment cross-leg length discrepancy, and the relationship between the knee axis and femur/tibia segment. The maximum a posteriori problem is formulated as a factor graph and inference is performed via post-hoc, on-manifold global optimization. The method is evaluated (N = 12) for a prescribed human motion profile task. Accuracy of derived knee flexion/extension angle (4.34 root mean square error (RMSE)) without magnetometers is similar to current state-of-the-art with magnetometer use. The developed framework can be expanded for modeling additional joints and constraints.

Low-cost electronic sensors for environmental research: Pitfalls and opportunities

Repeat observations underpin our understanding of environmental processes, but financial constraints often limit scientists’ ability to deploy dense networks of conventional commercial instrumentation. Rapid growth in the Internet-Of-Things (IoT) and the maker movement is paving the way for low-cost electronic sensors to transform global environmental monitoring. Accessible and inexpensive sensor construction is also fostering exciting opportunities for citizen science and participatory research. Drawing on 6 years of developmental work with Arduino-based open-source hardware and software, extensive laboratory and field testing, and incorporation of such technology into active research programmes, we outline a series of successes, failures and lessons learned in designing and deploying environmental sensors. Six case studies are presented: a water table depth probe, air and water quality sensors, multi-parameter weather stations, a time-sequencing lake sediment trap, and a sonic anemometer for monitoring sand transport. Schematics, code and purchasing guidance to reproduce our sensors are described in the paper, with detailed build instructions hosted on our King’s College London Geography Environmental Sensors Github repository and the FreeStation project website. We show in each case study that manual design and construction can produce research-grade scientific instrumentation (mean bias error for calibrated sensors –0.04 to 23%) for a fraction of the conventional cost, provided rigorous, sensor-specific calibration and field testing is conducted. In sharing our collective experiences with build-it-yourself environmental monitoring, we intend for this paper to act as a catalyst for physical geographers and the wider environmental science community to begin incorporating low-cost sensor development into their research activities. The capacity to deploy denser sensor networks should ultimately lead to superior environmental monitoring at the local to global scales.

Field performance assessment and calibration of multi-depth AquaCheck capacitance-based soil moisture probes under permanent pasture for hill country soils

The use of accurate and near real time volumetric soil moisture (θv, m3 m−3) measurements informs not only precise irrigation scheduling but also decisions regarding fertiliser applications, feed supply and stock management in non-irrigated farming systems. To assist θv monitoring, capacitance-based AquaCheck (AquaCheck, South Africa) sensors are being increasingly used in precision agriculture applications. However, the performance of these sensors have not been investigated extensively under field conditions with dynamic soil moisture regimes.For evaluation, 20 probes (4 sensors/unit) were installed in predominantly silt loam soils on a pastoral hill country farm in the southern east coast of the North Island of New Zealand. Raw readings, and θv derived from a manufacturer-recommended calibration, were compared with 400 reference θv values over a one-year timespan (01/11/2016 – 01/12/2017). In addition, three custom calibration methods were developed and assessed, each tailored to suit the availability of soil information.Over all depths (100 to 400 mm), the manufacturer calibration resulted in θv readings with a mean RMSE of 0.106 m3 m−3, a mean MBE of -0.099 m3 m−3 (indicating underestimation), and a coefficient of determination (R2) of 0.58 when correlated to reference θv values. A single custom formula, relevant to the local soils resulted in an improved RMSE of 0.039 m3 m−3 and R2 = 0.58, while probe-specific calibrations achieved an RMSE of 0.029 m3 m−3 and R2 of 0.77. The application of sensor-specific calibration resulted in an RMSE of 0.019 m3 m−3 with R2 = 0.9. Temporal sensor responses to the magnitude of rainfall events closely agreed. Sensor performance and accuracy errors were observed to vary as a function of soil wetness, bulk density (ρb gcm-3) clay and total organic carbon (TOC) content. Increasing clay content and ρb resulted in increased raw output for a given θv whereas TOC demonstrated the opposite trend. These effects were significant (P value < 0.001) but eliminated by the sensor-specific custom calibration.

Effective Calibration of Low-Cost Soil Water Content Sensors.

Soil water content is a key variable for understanding and modelling ecohydrological processes. Low-cost electromagnetic sensors are increasingly being used to characterize the spatio-temporal dynamics of soil water content, despite the reduced accuracy of such sensors as compared to reference electromagnetic soil water content sensing methods such as time domain reflectometry. Here, we present an effective calibration method to improve the measurement accuracy of low-cost soil water content sensors taking the recently developed SMT100 sensor (Truebner GmbH, Neustadt, Germany) as an example. We calibrated the sensor output of more than 700 SMT100 sensors to permittivity using a standard procedure based on five reference media with a known apparent dielectric permittivity (1 < Ka < 34.8). Our results showed that a sensor-specific calibration improved the accuracy of the calibration compared to single “universal” calibration. The associated additional effort in calibrating each sensor individually is relaxed by a dedicated calibration setup that enables the calibration of large numbers of sensors in limited time while minimizing errors in the calibration process.

A Consistent AVHRR Visible Calibration Record Based on Multiple Methods Applicable for the NOAA Degrading Orbits. Part I: Methodology

AbstractThe 35-yr NOAA Advanced Very High Resolution Radiometer (AVHRR) observation record offers an excellent opportunity to study decadal climate variability, provided that all participating AVHRR instruments are calibrated on a consistent radiometric scale. Because of the lack of onboard calibration systems, the solar imaging channels of the AVHRR must be vicariously calibrated using invariant Earth targets as a calibrated reference source. The greatest challenge in calibrating the AVHRR dataset is the orbit degradation of the NOAA satellites, which eventually drift into a terminator orbit several years after launch. Therefore, the invariant targets must be characterized over the full range of solar zenith angles (SZAs) sampled by the satellite instrument.This study outlines a multiple invariant Earth target calibration approach specifically designed to account for the degrading NOAA orbits. The desert, polar ice, and deep convective cloud (DCC) invariant targets are characterized over all observed SZAs using NOAA-16 AVHRR measurements, which are referenced to the Aqua MODIS Collection 6 calibration via direct transfer of the MODIS calibration to the NOAA-16 AVHRR instrument using simultaneous nadir overpass (SNO) observations over the North Pole. The multiple invariant target calibrations are combined using the inverse of their temporal variance to optimize the resulting calibration stability. The NOAA-18 AVHRR gains derived using the desert, polar ice, and DCC targets, as well as from SNO, were found consistent within 1%, thereby validating that the Aqua MODIS calibration is effectively transferred to the reference calibration targets. The companion paper, Part II, applies the methodology across the AVHRR record to derive the sensor-specific calibration coefficients.

The Ocean Colour Climate Change Initiative: II. Spatial and temporal homogeneity of satellite data retrieval due to systematic effects in atmospheric correction processors

The established procedure to access the quality of atmospheric correction processors and their underlying algorithms is the comparison of satellite data products with related in-situ measurements. Although this approach addresses the accuracy of derived geophysical properties in a straight forward fashion, it is also limited in its ability to catch systematic sensor and processor dependent behaviour of satellite products along the scan-line, which might impair the usefulness of the data in spatial analyses.The Ocean Colour Climate Change Initiative (OC-CCI) aims to create an ocean colour dataset on a global scale to meet the demands of the ecosystem modelling community. The need for products with increasing spatial and temporal resolution that also show as little systematic and random errors as possible, increases. Due to cloud cover, even temporal means can be influenced by along-scanline artefacts if the observations are not balanced and effects cannot be cancelled out mutually.These effects can arise from a multitude of results which are not easily separated, if at all. Among the sources of artefacts, there are some sensor-specific calibration issues which should lead to similar responses in all processors, as well as processor-specific features which correspond with the individual choices in the algorithms.A set of methods is proposed and applied to MERIS data over two regions of interest in the North Atlantic and the South Pacific Gyre. The normalised water leaving reflectance products of four atmospheric correction processors, which have also been evaluated in match-up analysis, is analysed in order to find and interpret systematic effects across track. These results are summed up with a semi-objective ranking and are used as a complement to the match-up analysis in the decision for the best Atmospheric Correction (AC) processor.Although the need for discussion remains concerning the absolutes by which to judge an AC processor, this example demonstrates clearly, that relying on the match-up analysis alone can lead to misjudgement.

ELIMINATING THE HEAT INPUT AS PARAMETER IN THE SAPFLOW+ METHOD

The recently developed Sapflow+ method to determine sap flux density and sapwood water content necessitates a correct estimation of the heat generated by the heater needle. Even though this can theoretically be calculated based on the resistance of the heater and the applied voltage and current, in practice, however, these theoretical calculations overestimate the actual heat output due to probe limitations. Originally, the heater was therefore calibrated in a medium with known thermal characteristics, leading to an accurate correction factor. Elimination of this heat input from the model is, nevertheless, much more convenient as sensor specific calibration is labour-intensive and has to be conducted meticulously as the needle distances during calibration have to be equal to those applied during field measurements. Dividing the original equations is theoretically possible to eliminate the heat input, but has practical limitations. Adapting the equations and grouping heat input and volumetric heat capacity into a single parameter, however, enables in-situ estimations of heat input if a single measurement of dry wood density and water content is conducted. Our results show that, based on a single wood core measurement, the Sapflow+ sensor can be calibrated in-situ, pre-empting sensor specific calibration in a separate medium.

ESTIMATION OF RADIOMETRIC CALIBRATION COEFFICIENTS OF EGYPTSAT-1 SENSOR

Abstract. Sensors usually must be calibrated as part of a measurement system. Calibration may include the procedure of correcting the transfer of the sensor, using the reference measurements, in such a way that a specific input-output relation can be guaranteed with a certain accuracy and under certain conditions. It is necessary to perform a calibration to relate the output signal precisely to the physical input signal (e.g., the output Digital Numbers (DNs) to the absolute units of at-sensor spectral radiance). Generic calibration data associated with Egyptsat-1 sensor are not provided by the manufacturer. Therefore, this study was conducted to estimate Egyptsat-1 sensor specific calibration data and tabulates the necessary constants for its different multispectral bands. We focused our attention on the relative calibration between Egyptsat-1 and Spot-4 sensors for their great spectral similarity. The key idea is to use concurrent correlation of signals received at both sensors in the same day (i.e., sensors are observing the same phenomenon). Calibration formula constructed from Spot-4 sensor is used to derive the calibration coefficients for Egyptsat-1. A brief overview of the radiometric calibration coefficients retrieval procedures is presented. A reasonable estimate of the overall calibration coefficient is obtained. They have been used to calibrate reflectances of Egyptsat-1 sensor. Further updates to evaluate and improve the retrieved calibration data are being investigated.

Evaluation of MPS-1 soil water potential sensor

Summary Soil water matric potential sensors are essential for understanding agricultural and natural ecosystems. However, accurate sensors that can be used for a wide range of soil moisture conditions have yet to be developed. The MPS-1 sensor by Decagon Devices, Inc. was tested and calibrated in a laboratory setting using pressure plate and suction apparatuses. This study evaluated the general performance of the MPS-1 sensor by: (1) testing for a wide range of varying soil conditions, including temperature and hysteresis; (2) determining measurement range, sensitivity, consistency, accuracy, and variation among sensors; and (3) developing a laboratory calibration procedure prior to field installation. Sensitivity of the MPS-1 sensor was found to be a highly nonlinear function of soil matric potential. Sensor-to-sensor variations were large, but were partly removed using the presented one-point calibration. Variation among sensors is largely eliminated by using a sensor-specific calibration. The MPS-1 sensitivity to temperature was relatively small and inconsistent, and hysteresis effects were relatively minor. Individual sensor output readings were consistent in time, remaining almost constant over a measurement period of 1.5 years. By comparing calibrated MPS-1 sensors with tensiometers in the field, we conclude that measurement uncertainty is less than 10 kPa (or 0.1 bar) in the range from 10 to 60 kPa.

Sensor-to-Sensor Variability of the ECHO EC-5, TE, and 5TE Sensors in Dielectric Liquids

Low-budget sensors used in wireless soil water content sensor networks typically show considerable variation. Because of the large number of sensors in sensor network applications, it is not feasible to account for this variability using a calibration between sensor response and soil water content. An alternative approach is to split the calibration into two parts: (i) determination of sensor response–permittivity relationships using standard liquids with a defined reference permittivity, and (ii) site-specific calibration between permittivity and soil water content using a subset of sensors. In this study, we determined sensor response–permittivity relationships for several ECH2O, EC-5, TE, and 5TE sensors by Decagon Devices (Pullman, WA). The objectives of this study were to determine (i) the sensor-to-sensor variability and precision of these sensor types, and (ii) the increase in accuracy when a sensor-specific calibration is used instead of a single calibration. The results showed that the sensor-to-sensor variability was significantly larger than the measurement noise for each sensor type. When a sensor-specific calibration was used, the RMSE expressed in (equivalent) soil water content ranged from 0.008 cm3 cm−3 for the TE sensor to 0.014 cm3 cm−3 for the EC-5 sensor in a permittivity range between ∼2 and 35. When a single calibration was used, the RMSE was higher and ranged from 0.01 cm3 cm−3 for the 5TE sensor to 0.02 cm3 cm−3 for the TE sensor. An improvement in accuracy of nearly 0.01 cm3 cm−3 can be reached in the high-permittivity range for each sensor type by calibrating each sensor individually.

International co-operation in satellite sensor calibration; The role of the CEOS Working Group on Calibration and Validation

Since 1984 the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) has acted as a forum for international co-operation for calibration and validation activities. WGCV has two objectives. These are “sensor-specific calibration and validation”, and “geophysical parameter and derived product validation”. Calibration is defined by WGCV as the process of quantitatively defining the system response to known, controlled signal inputs, and validation is the process of assessing by independent means the quality of the data products derived from the system outputs. International co-operation underpins this work and is at the heart of a new CEOS initiative, the Integrated Global Observing Strategy (IGOS). The IGOS objectives are to derive greater benefit from earth observing systems, to identify gaps in current system planning, to increase the range of Earth observation applications and improve the integration of global observation activities. With the advent of the IGOS, WGCV has embarked on a new three-year work plan. The main elements of improving co-ordination and communication and implementation of validation activities are described in this paper.