Metrology-Assisted Production in Agriculture and Forestry

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Agriculture agriculture	3.3	4.9	2011	20.2 Days	CHF 2600
Forests forests	2.4	4.4	2010	16.9 Days	CHF 2600
Sensors sensors	3.4	7.3	2001	16.8 Days	CHF 2600

4 pages, 162 KiB

Open AccessEditorial

Metrology-Assisted Production in Agriculture and Forestry

by H. R. Bogena, C. Brogi, C. Hübner and A. Panagopoulos

Sensors 2024, 24(23), 7542; https://doi.org/10.3390/s24237542 - 26 Nov 2024

Viewed by 417

According to the Food and Agriculture Organization of the United Nations, climate change will negatively affect food security and increase pressure on freshwater resources [...] Full article

(This article belongs to the Topic Metrology-Assisted Production in Agriculture and Forestry)

25 pages, 7510 KiB

Open AccessArticle

Effect of Biomass Water Dynamics in Cosmic-Ray Neutron Sensor Observations: A Long-Term Analysis of Maize–Soybean Rotation in Nebraska

by Tanessa C. Morris, Trenton E. Franz, Sophia M. Becker and Andrew E. Suyker

Sensors 2024, 24(13), 4094; https://doi.org/10.3390/s24134094 - 24 Jun 2024

Cited by 1 | Viewed by 860

Abstract

Precise soil water content (SWC) measurement is crucial for effective water resource management. This study utilizes the Cosmic-Ray Neutron Sensor (CRNS) for area-averaged SWC measurements, emphasizing the need to consider all hydrogen sources, including time-variable plant biomass and water content. Near Mead, Nebraska, [...] Read more.

Precise soil water content (SWC) measurement is crucial for effective water resource management. This study utilizes the Cosmic-Ray Neutron Sensor (CRNS) for area-averaged SWC measurements, emphasizing the need to consider all hydrogen sources, including time-variable plant biomass and water content. Near Mead, Nebraska, three field sites (CSP1, CSP2, and CSP3) growing a maize–soybean rotation were monitored for 5 (CSP1 and CSP2) and 13 (CSP3) years. Data collection included destructive biomass water equivalent (BWE) biweekly sampling, epithermal neutron counts, atmospheric meteorological variables, and point-scale SWC from a sparse time domain reflectometry (TDR) network (four locations and five depths). In 2023, dense gravimetric SWC surveys were collected eight (CSP1 and CSP2) and nine (CSP3) times over the growing season (April to October). The N₀ parameter exhibited a linear relationship with BWE, suggesting that a straightforward vegetation correction factor may be suitable (f_b). Results from the 2023 gravimetric surveys and long-term TDR data indicated a neutron count rate reduction of about 1% for every 1 kg m⁻² (or mm of water) increase in BWE. This reduction factor aligns with existing shorter-term row crop studies but nearly doubles the value previously reported for forests. This long-term study contributes insights into the vegetation correction factor for CRNS, helping resolve a long-standing issue within the CRNS community. Full article

(This article belongs to the Topic Metrology-Assisted Production in Agriculture and Forestry)

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24 pages, 4086 KiB

Open AccessArticle

Field Testing of Gamma-Spectroscopy Method for Soil Water Content Estimation in an Agricultural Field

by Sophia M. Becker, Trenton E. Franz, Tanessa C. Morris and Bailey Mullins

Sensors 2024, 24(7), 2223; https://doi.org/10.3390/s24072223 - 30 Mar 2024

Cited by 1 | Viewed by 1359

Abstract

Gamma-ray spectroscopy (GRS) enables continuous estimation of soil water content (SWC) at the subfield scale with a noninvasive sensor. Hydrological applications, including hyper-resolution land surface models and precision agricultural decision making, could benefit greatly from such SWC information, but a gap exists between [...] Read more.

Gamma-ray spectroscopy (GRS) enables continuous estimation of soil water content (SWC) at the subfield scale with a noninvasive sensor. Hydrological applications, including hyper-resolution land surface models and precision agricultural decision making, could benefit greatly from such SWC information, but a gap exists between established theory and accurate estimation of SWC from GRS in the field. In response, we conducted a robust three-year field validation study at a well-instrumented agricultural site in Nebraska, United States. The study involved 27 gravimetric water content sampling campaigns in maize and soybean and ⁴⁰K specific activity (Bq kg⁻¹) measurements from a stationary GRS sensor. Our analysis showed that the current method for biomass water content correction is appropriate for our maize and soybean field but that the ratio of soil mass attenuation to water mass attenuation used in the theoretical equation must be adjusted to satisfactorily describe the field data. We propose a calibration equation with two free parameters: the theoretical ⁴⁰K intensity in dry soil and a, which creates an “effective” mass attenuation ratio. Based on statistical analyses of our data set, we recommend calibrating the GRS sensor for SWC estimation using 10 profiles within the footprint and 5 calibration sampling campaigns to achieve a cross-validation root mean square error below 0.035 g g⁻¹. Full article

(This article belongs to the Topic Metrology-Assisted Production in Agriculture and Forestry)

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17 pages, 3199 KiB

Open AccessArticle

Response of the TEROS 12 Soil Moisture Sensor under Different Soils and Variable Electrical Conductivity

by Athanasios Fragkos, Dimitrios Loukatos, Georgios Kargas and Konstantinos G. Arvanitis

Sensors 2024, 24(7), 2206; https://doi.org/10.3390/s24072206 - 29 Mar 2024

Cited by 6 | Viewed by 2676

Abstract

In this work, the performance of the TEROS 12 electromagnetic sensor, which measures volumetric soil water content (θ), bulk soil electrical conductivity (σ_b), and temperature, is examined for a number of different soils, different θ and different levels of the electrical [...] Read more.

In this work, the performance of the TEROS 12 electromagnetic sensor, which measures volumetric soil water content (θ), bulk soil electrical conductivity (σ_b), and temperature, is examined for a number of different soils, different θ and different levels of the electrical conductivity of the soil solution (EC_W) under laboratory conditions. For the above reason, a prototype device was developed including a low-cost microcontroller and suitable adaptation circuits for the aforementioned sensor. Six characteristic porous media were examined in a θ range from air drying to saturation, while four different solutions of increasing Electrical Conductivity (EC_w) from 0.28 dS/m to approximately 10 dS/m were used in four of these porous media. It was found that TEROS 12 apparent dielectric permittivity (ε_a) readings were lower than that of Topp’s permittivity–water content relationship, especially at higher soil water content values in the coarse porous bodies. The differences are observed in sand (S), sandy loam (SL) and loam (L), at this order. The results suggested that the relationship between experimentally measured soil water content (θ_m) and ε_a^0.5 was strongly linear (0.869 < R² < 0.989), but the linearity of the relation θ_m-ε_a^0.5 decreases with the increase in bulk EC (σ_b) of the soil. The most accurate results were provided by the multipoint calibration method (CAL), as evaluated with the root mean square error (RMSE). Also, it was found that ε_a degrades substantially at values of σ_b less than 2.5 dS/m while ε_a returns to near 80 at higher values. Regarding the relation ε_a-σ_b, it seems that it is strongly linear and that its slope depends on the pore water electrical conductivity (σ_p) and the soil type. Full article

(This article belongs to the Topic Metrology-Assisted Production in Agriculture and Forestry)

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15 pages, 5616 KiB

Open AccessArticle

Temperature-Corrected Calibration of GS3 and TEROS-12 Soil Water Content Sensors

by Paolo Nasta, Francesca Coccia, Ugo Lazzaro, Heye R. Bogena, Johan A. Huisman, Benedetto Sica, Caterina Mazzitelli, Harry Vereecken and Nunzio Romano

Sensors 2024, 24(3), 952; https://doi.org/10.3390/s24030952 - 1 Feb 2024

Cited by 5 | Viewed by 1583

Abstract

The continuous monitoring of soil water content is commonly carried out using low-frequency capacitance sensors that require a site-specific calibration to relate sensor readings to apparent dielectric bulk permittivity (K_b) and soil water content (θ). In fine-textured soils, [...] Read more.

The continuous monitoring of soil water content is commonly carried out using low-frequency capacitance sensors that require a site-specific calibration to relate sensor readings to apparent dielectric bulk permittivity (K_b) and soil water content (θ). In fine-textured soils, the conversion of K_b to θ is still challenging due to temperature effects on the bound water fraction associated with clay mineral surfaces, which is disregarded in factory calibrations. Here, a multi-point calibration approach accounts for temperature effects on two soils with medium to high clay content. A calibration strategy was developed using repacked soil samples in which the K_b-θ relationship was determined for temperature (T) steps from 10 to 40 °C. This approach was tested using the GS3 and TEROS-12 sensors (METER Group, Inc. Pullman, WA, USA; formerly Decagon Devices). K_b is influenced by T in both soils with contrasting T-K_b relationships. The measured data were fitted using a linear function θ = a

\sqrt{K_{b}}

+ b with temperature-dependent coefficients a and b. The slope, a(T), and intercept, b(T), of the loam soil were different from the ones of the clay soil. The consideration of a temperature correction resulted in low RMSE values, ranging from 0.007 to 0.033 cm³ cm⁻³, which were lower than the RMSE values obtained from factory calibration (0.046 to 0.11 cm³ cm⁻³). However, each experiment was replicated only twice using two different sensors. Sensor-to-sensor variability effects were thus ignored in this study and will be systematically investigated in a future study. Finally, the applicability of the proposed calibration method was tested at two experimental sites. The spatial-average θ from a network of GS3 sensors based on the new calibration fairly agreed with the independent area-wide θ from the Cosmic Ray Neutron Sensor (CRNS). This study provided a temperature-corrected calibration to increase the accuracy of commercial sensors, especially under dry conditions, at two experimental sites. Full article

(This article belongs to the Topic Metrology-Assisted Production in Agriculture and Forestry)

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16 pages, 7314 KiB

Open AccessArticle

MSGV-YOLOv7: A Lightweight Pineapple Detection Method

by Rihong Zhang, Zejun Huang, Yuling Zhang, Zhong Xue and Xiaomin Li

Agriculture 2024, 14(1), 29; https://doi.org/10.3390/agriculture14010029 - 23 Dec 2023

Cited by 3 | Viewed by 2211

Abstract

In order to optimize the efficiency of pineapple harvesting robots in recognition and target detection, this paper introduces a lightweight pineapple detection model, namely MSGV-YOLOv7. This model adopts MobileOne as the innovative backbone network and uses thin neck as the neck network. The [...] Read more.

In order to optimize the efficiency of pineapple harvesting robots in recognition and target detection, this paper introduces a lightweight pineapple detection model, namely MSGV-YOLOv7. This model adopts MobileOne as the innovative backbone network and uses thin neck as the neck network. The enhancements in these architectures have significantly improved the ability of feature extraction and fusion, thereby speeding up the detection rate. Empirical results indicated that MSGV-YOLOv7 surpassed the original YOLOv7 with a 1.98% increase in precision, 1.35% increase in recall rate, and 3.03% increase in mAP, while the real-time detection speed reached 17.52 frames per second. Compared with Faster R-CNN and YOLOv5n, the mAP of this model increased by 14.89% and 5.22%, respectively, while the real-time detection speed increased by approximately 2.18 times and 1.58 times, respectively. The application of image visualization testing has verified the results, confirming that the MSGV-YOLOv7 model successfully and precisely identified the unique features of pineapples. The proposed pineapple detection method presents significant potential for broad-scale implementation. It is expected to notably reduce both the time and economic costs associated with pineapple harvesting operations. Full article

(This article belongs to the Topic Metrology-Assisted Production in Agriculture and Forestry)

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16 pages, 6629 KiB

Open AccessArticle

Detection of Cherry Tree Crown Based on Improved LA-dpv3+ Algorithm

by Zhenzhen Cheng, Yifan Cheng, Meng Li, Xiangxiang Dong, Shoufu Gong and Xiaoxiao Min

Forests 2023, 14(12), 2404; https://doi.org/10.3390/f14122404 - 9 Dec 2023

Cited by 3 | Viewed by 1447

Abstract

Accurate recognition of the canopy is a prerequisite for precision orchard yield estimation. This paper proposed an enhanced LA-dpv3+ approach for the recognition of cherry canopies based on UAV image data, with a focus on enhancing feature representation through the implementation of an [...] Read more.

Accurate recognition of the canopy is a prerequisite for precision orchard yield estimation. This paper proposed an enhanced LA-dpv3+ approach for the recognition of cherry canopies based on UAV image data, with a focus on enhancing feature representation through the implementation of an attention mechanism. The attention mechanism module was introduced to the encoder stage of the DeepLabV3+ architecture, which improved the network’s detection accuracy and robustness. Specifically, we developed a diagonal discrete cosine transform feature strategy within the attention convolution module to extract finer details of canopy information from multiple frequency components. The proposed model was constructed based on a lightweight DeepLabv3+ network architecture that incorporates a MobileNetv2 backbone, effectively reducing computational costs. The results demonstrate that our proposed method achieved a balance between computational cost and the quality of results when compared to competing approaches. Our model’s accuracy exceeded 89% while maintaining a modest model size of only 46.8 MB. The overall performance indicated that with the help of a neural network, segmentation failures were notably reduced, particularly in high-density weed conditions, resulting in significant increases in accuracy (ACC), F1-score, and intersection over union (IOU), which were increased by 5.44, 3.39, and 8.62%, respectively. The method proposed in this paper may be applied to future image-based applications and contribute to automated orchard management. Full article

(This article belongs to the Topic Metrology-Assisted Production in Agriculture and Forestry)

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15 pages, 760 KiB

Open AccessArticle

Risk Cognition, Social Learning, and Farmers’ Adoption of Conservation Agriculture Technology

by Yaqin Ren, Hui Feng and Tianzhi Gao

Agriculture 2023, 13(8), 1644; https://doi.org/10.3390/agriculture13081644 - 21 Aug 2023

Cited by 5 | Viewed by 1753

Abstract

Soil degradation and declining soil fertility are prominent issues for sustainable agricultural development in China. Therefore, it is of great significance to promote the adoption rate of conservation agriculture technology. Risk cognition and technology adoption are closely related, but this perspective is rarely [...] Read more.

Soil degradation and declining soil fertility are prominent issues for sustainable agricultural development in China. Therefore, it is of great significance to promote the adoption rate of conservation agriculture technology. Risk cognition and technology adoption are closely related, but this perspective is rarely focused on, and it is essential to discuss the influence of social learning on the impact. The Loess Plateau is a representative area for promoting and implementing conservation agriculture techniques. By collecting face-to-face survey data from 1268 farmers in Shaanxi, Shanxi, and Ningxia provinces in China, this study used the binary probit model to examine the impact of risk cognition on the adoption of conservation agriculture technology and the influence of social learning on the impact. The results showed that risk cognition has a significant positive impact on the adoption of conservation agriculture technology; social learning significantly enhances the effect of risk cognition on farmers’ adoption of conservation agriculture technology. Both offline practical learning through “learning by doing” and online learning with ICT play an important moderating role in the impact; a high level of social learning enhances risk cognition to a greater extent and promotes enthusiasm for adopting conservation agriculture technology. Therefore, the value of farmers’ risk cognition should be considered in promoting and implementing conservation agriculture technology. Moreover, expanding offline and online social learning channels is crucial to improve farmers’ risk cognition and promote the adoption of conservation agriculture technology. Full article

(This article belongs to the Topic Metrology-Assisted Production in Agriculture and Forestry)

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16 pages, 4317 KiB

Open AccessArticle

Evaluation of Three Soil Moisture Profile Sensors Using Laboratory and Field Experiments

by Felix Nieberding, Johan Alexander Huisman, Christof Huebner, Bernd Schilling, Ansgar Weuthen and Heye Reemt Bogena

Sensors 2023, 23(14), 6581; https://doi.org/10.3390/s23146581 - 21 Jul 2023

Cited by 5 | Viewed by 2652

Abstract

Soil moisture profile sensors (SMPSs) have a high potential for climate-smart agriculture due to their easy handling and ability to perform simultaneous measurements at different depths. To date, an accurate and easy-to-use method for the evaluation of long SMPSs is not available. In [...] Read more.

Soil moisture profile sensors (SMPSs) have a high potential for climate-smart agriculture due to their easy handling and ability to perform simultaneous measurements at different depths. To date, an accurate and easy-to-use method for the evaluation of long SMPSs is not available. In this study, we developed laboratory and field experiments to evaluate three different SMPSs (SoilVUE10, Drill&Drop, and SMT500) in terms of measurement accuracy, sensor-to-sensor variability, and temperature stability. The laboratory experiment features a temperature-controlled lysimeter to evaluate intra-sensor variability and temperature stability of SMPSs. The field experiment features a water level-controlled sandbox and reference TDR measurements to evaluate the soil water measurement accuracy of the SMPS. In both experiments, a well-characterized fine sand was used as measurement medium to ensure homogeneous dielectric properties in the measurement domain of the sensors. The laboratory experiments with the lysimeter showed that the Drill&Drop sensor has the highest temperature sensitivity with a decrease of 0.014 m³ m⁻³ per 10 °C, but at the same time showed the lowest intra- and inter-sensor variability. The field experiment with the sandbox showed that all three SMPSs have a similar performance (average RMSE ≈ 0.023 m³ m⁻³) with higher uncertainties at intermediate soil moisture contents. The presented combination of laboratory and field tests were found to be well suited to evaluate the performance of SMPSs and will be used to test additional SMPSs in the future. Full article

(This article belongs to the Topic Metrology-Assisted Production in Agriculture and Forestry)

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13 pages, 2705 KiB

Open AccessArticle

SENSE-GDD: A Satellite-Derived Temperature Monitoring Service to Provide Growing Degree Days

by Iphigenia Keramitsoglou, Panagiotis Sismanidis, Olga Sykioti, Vassilios Pisinaras, Ioannis Tsakmakis, Andreas Panagopoulos, Argyrios Argyriou and Chris T. Kiranoudis

Agriculture 2023, 13(5), 1108; https://doi.org/10.3390/agriculture13051108 - 22 May 2023

Cited by 2 | Viewed by 2445

Abstract

A new satellite-enabled interoperable service has been developed to provide high spatiotemporal and continuous time series of Growing Degree Days (GDDs) at the field. The GDDs are calculated from MSG-SEVIRI data acquired by the EUMETCast station operated by IAASARS/NOA and downscaled on-the-fly to [...] Read more.

A new satellite-enabled interoperable service has been developed to provide high spatiotemporal and continuous time series of Growing Degree Days (GDDs) at the field. The GDDs are calculated from MSG-SEVIRI data acquired by the EUMETCast station operated by IAASARS/NOA and downscaled on-the-fly to increase the initial coarse spatial resolution from the original 4–5 km to 1 km. The performance of the new service SENSE-GDD, in deriving reliable GDD timeseries at dates very close to key phenological stages, is assessed using in situ air temperature measurements from weather stations installed in Gerovassiliou Estate vineyard at Epanomi (Northern Greece) and an apple orchard at Agia (Central Greece). Budburst, pollination, and the start of veraison are selected as key phenological stages for the vineyards, whilst budburst and pollination for the apple orchard. The assessment shows that SENSE-GDD provided uninterrupted accurate measurements in both crop types. A distinct feature is that the proposed service can support decisions in non-instrumented crop fields in a cost-effective way, paving the way for its extended operational use in agriculture. Full article

(This article belongs to the Topic Metrology-Assisted Production in Agriculture and Forestry)

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15 pages, 5496 KiB

Open AccessArticle

Detection of Chrysanthemums Inflorescence Based on Improved CR-YOLOv5s Algorithm

by Wentao Zhao, Dasheng Wu and Xinyu Zheng

Sensors 2023, 23(9), 4234; https://doi.org/10.3390/s23094234 - 24 Apr 2023

Cited by 6 | Viewed by 1844

Abstract

Accurate recognition of the flowering stage is a prerequisite for flower yield estimation. In order to improve the recognition accuracy based on the complex image background, such as flowers partially covered by leaves and flowers with insignificant differences in various fluorescence, this paper [...] Read more.

Accurate recognition of the flowering stage is a prerequisite for flower yield estimation. In order to improve the recognition accuracy based on the complex image background, such as flowers partially covered by leaves and flowers with insignificant differences in various fluorescence, this paper proposed an improved CR-YOLOv5s to recognize flower buds and blooms for chrysanthemums by emphasizing feature representation through an attention mechanism. The coordinate attention mechanism module has been introduced to the backbone of the YOLOv5s so that the network can pay more attention to chrysanthemum flowers, thereby improving detection accuracy and robustness. Specifically, we replaced the convolution blocks in the backbone network of YOLOv5s with the convolution blocks from the RepVGG block structure to improve the feature representation ability of YOLOv5s through a multi-branch structure, further improving the accuracy and robustness of detection. The results showed that the average accuracy of the improved CR-YOLOv5s was as high as 93.9%, which is 4.5% better than that of normal YOLOv5s. This research provides the basis for the automatic picking and grading of flowers, as well as a decision-making basis for estimating flower yield. Full article

(This article belongs to the Topic Metrology-Assisted Production in Agriculture and Forestry)

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27 pages, 11211 KiB

Open AccessArticle

Monitoring Irrigation in Small Orchards with Cosmic-Ray Neutron Sensors

by Cosimo Brogi, Vassilios Pisinaras, Markus Köhli, Olga Dombrowski, Harrie-Jan Hendricks Franssen, Konstantinos Babakos, Anna Chatzi, Andreas Panagopoulos and Heye Reemt Bogena

Sensors 2023, 23(5), 2378; https://doi.org/10.3390/s23052378 - 21 Feb 2023

Cited by 5 | Viewed by 2305

Abstract

Due to their unique characteristics, cosmic-ray neutron sensors (CRNSs) have potential in monitoring and informing irrigation management, and thus optimising the use of water resources in agriculture. However, practical methods to monitor small, irrigated fields with CRNSs are currently not available and the [...] Read more.

Due to their unique characteristics, cosmic-ray neutron sensors (CRNSs) have potential in monitoring and informing irrigation management, and thus optimising the use of water resources in agriculture. However, practical methods to monitor small, irrigated fields with CRNSs are currently not available and the challenges of targeting areas smaller than the CRNS sensing volume are mostly unaddressed. In this study, CRNSs are used to continuously monitor soil moisture (SM) dynamics in two irrigated apple orchards (Agia, Greece) of ~1.2 ha. The CRNS-derived SM was compared to a reference SM obtained by weighting a dense sensor network. In the 2021 irrigation period, CRNSs could only capture the timing of irrigation events, and an ad hoc calibration resulted in improvements only in the hours before irrigation (RMSE between 0.020 and 0.035). In 2022, a correction based on neutron transport simulations, and on SM measurements from a non-irrigated location, was tested. In the nearby irrigated field, the proposed correction improved the CRNS-derived SM (from 0.052 to 0.031 RMSE) and, most importantly, allowed for monitoring the magnitude of SM dynamics that are due to irrigation. The results are a step forward in using CRNSs as a decision support system in irrigation management. Full article

(This article belongs to the Topic Metrology-Assisted Production in Agriculture and Forestry)

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18 pages, 7050 KiB

Open AccessArticle

DEM Study of the Motion Characteristics of Rice Particles in the Indented Cylinder Separator

by Xinzhi Yu, Xuesong Jiang, Haiyang Gu and Fei Shen

Sensors 2023, 23(1), 285; https://doi.org/10.3390/s23010285 - 27 Dec 2022

Cited by 1 | Viewed by 2382

Abstract

The precise separation of rice particles is an important step in rice processing. In this paper, discrete element simulations of the motion of rice particles of different integrity in an indented cylinder separator were carried out using numerical simulation methods. The effects of [...] Read more.

The precise separation of rice particles is an important step in rice processing. In this paper, discrete element simulations of the motion of rice particles of different integrity in an indented cylinder separator were carried out using numerical simulation methods. The effects of single factors (cylinder rotation rate, cylinder axial inclination angle, and collection trough inclination angle) on the motion trajectories of particles are investigated and the probability distribution functions of particles are obtained. The statistical method of Kullback-Leibler divergence is used to quantitatively evaluate the differences in the probability distribution functions of the escape angles of particles of different degrees of integrity. The purpose of this paper is to determine the optimum parameters for an indent cylinder separator by understanding the material cylinder separating process from particle scale and to provide a basis for the numerical design of a grain particle cylinder separators. Full article

(This article belongs to the Topic Metrology-Assisted Production in Agriculture and Forestry)

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19 pages, 3177 KiB

Open AccessReview

Recent Developments in Wireless Soil Moisture Sensing to Support Scientific Research and Agricultural Management

by Heye Reemt Bogena, Ansgar Weuthen and Johan Alexander Huisman

Sensors 2022, 22(24), 9792; https://doi.org/10.3390/s22249792 - 13 Dec 2022

Cited by 19 | Viewed by 12737

Abstract

In recent years, wireless sensor network (WSN) technology has emerged as an important technique for wireless sensing of soil moisture from the field to the catchment scale. This review paper presents the current status of wireless sensor network (WSN) technology for distributed, near [...] Read more.

In recent years, wireless sensor network (WSN) technology has emerged as an important technique for wireless sensing of soil moisture from the field to the catchment scale. This review paper presents the current status of wireless sensor network (WSN) technology for distributed, near real-time sensing of soil moisture to investigate seasonal and event dynamics of soil moisture patterns. It is also discussed how WSN measurements of soil measurements contribute to the validation and downscaling of satellite data and non-invasive geophysical instruments as well as the validation of distributed hydrological models. Finally, future perspectives for WSN measurements of soil moisture are highlighted, which includes the improved integration of real-time WSN measurements with other information sources using the latest wireless communication techniques and cyberinfrastructures. Full article

(This article belongs to the Topic Metrology-Assisted Production in Agriculture and Forestry)

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19 pages, 3563 KiB

Open AccessArticle

Optimal Temporal Filtering of the Cosmic-Ray Neutron Signal to Reduce Soil Moisture Uncertainty

by Patrick Davies, Roland Baatz, Heye Reemt Bogena, Emmanuel Quansah and Leonard Kofitse Amekudzi

Sensors 2022, 22(23), 9143; https://doi.org/10.3390/s22239143 - 25 Nov 2022

Cited by 7 | Viewed by 2573

Abstract

Cosmic ray neutron sensors (CRNS) are increasingly used to determine field-scale soil moisture (SM). Uncertainty of the CRNS-derived soil moisture strongly depends on the CRNS count rate subject to Poisson distribution. State-of-the-art CRNS signal processing averages neutron counts over many hours, thereby accounting [...] Read more.

Cosmic ray neutron sensors (CRNS) are increasingly used to determine field-scale soil moisture (SM). Uncertainty of the CRNS-derived soil moisture strongly depends on the CRNS count rate subject to Poisson distribution. State-of-the-art CRNS signal processing averages neutron counts over many hours, thereby accounting for soil moisture temporal dynamics at the daily but not sub-daily time scale. This study demonstrates CRNS signal processing methods to improve the temporal accuracy of the signal in order to observe sub-daily changes in soil moisture and improve the signal-to-noise ratio overall. In particular, this study investigates the effectiveness of the Moving Average (MA), Median filter (MF), Savitzky–Golay (SG) filter, and Kalman filter (KF) to reduce neutron count error while ensuring that the temporal SM dynamics are as good as possible. The study uses synthetic data from four stations for measuring forest ecosystem–atmosphere relations in Africa (Gorigo) and Europe (SMEAR II (Station for Measuring Forest Ecosystem–Atmosphere Relations), Rollesbroich, and Conde) with different soil properties, land cover and climate. The results showed that smaller window sizes (12 h) for MA, MF and SG captured sharp changes closely. Longer window sizes were more beneficial in the case of moderate soil moisture variations during long time periods. For MA, MF and SG, optimal window sizes were identified and varied by count rate and climate, i.e., estimated temporal soil moisture dynamics by providing a compromise between monitoring sharp changes and reducing the effects of outliers. The optimal window for these filters and the Kalman filter always outperformed the standard procedure of simple 24-h averaging. The Kalman filter showed its highest robustness in uncertainty reduction at three different locations, and it maintained relevant sharp changes in the neutron counts without the need to identify the optimal window size. Importantly, standard corrections of CRNS before filtering improved soil moisture accuracy for all filters. We anticipate the improved signal-to-noise ratio to benefit CRNS applications such as detection of rain events at sub-daily resolution, provision of SM at the exact time of a satellite overpass, and irrigation applications. Full article

(This article belongs to the Topic Metrology-Assisted Production in Agriculture and Forestry)

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16 pages, 2820 KiB

Open AccessArticle

Robust Soil Water Potential Sensor to Optimize Irrigation in Agriculture

by David Menne, Christof Hübner, Dennis Trebbels and Norbert Willenbacher

Sensors 2022, 22(12), 4465; https://doi.org/10.3390/s22124465 - 13 Jun 2022

Cited by 9 | Viewed by 3981

Abstract

Extreme weather phenomena are on the rise due to ongoing climate change. Therefore, the need for irrigation in agriculture will increase, although it is already the largest consumer of water, a valuable resource. Soil moisture sensors can help to use water efficiently and [...] Read more.

Extreme weather phenomena are on the rise due to ongoing climate change. Therefore, the need for irrigation in agriculture will increase, although it is already the largest consumer of water, a valuable resource. Soil moisture sensors can help to use water efficiently and economically. For this reason, we have recently presented a novel soil moisture sensor with a high sensitivity and broad measuring range. This device does not measure the moisture in the soil but the water available to plants, i.e., the soil water potential (SWP). The sensor consists of two highly porous (>69%) ceramic discs with a broad pore size distribution (0.5 to 200 μm) and a new circuit board system using a transmission line within a time-domain transmission (TDT) circuit. This detects the change in the dielectric response of the ceramic discs with changing water uptake. To prove the concept, a large number of field tests were carried out and comparisons were made with commercial soil water potential sensors. The experiments confirm that the sensor signal is correlated to the soil water potential irrespective of soil composition and is thus suitable for the optimization of irrigation systems. Full article

(This article belongs to the Topic Metrology-Assisted Production in Agriculture and Forestry)

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