Topic Editors

Forschungszentrum Jülich, Institute of Bio- and Geosciences, Agrosphere (IBG-3), 52428 Jülich, Germany
Forschungszentrum Jülich, Institute of Bio-and Geosciences, Agrosphere (IBG-3), 52428 Jülich, Germany
Prof. Dr. Christof Huebner
Department of Electrical Engineering, University of Applied Sciences Mannheim, Paul-Wittsack-Str. 10, 68163 Mannheim, Germany
Soil and Water Resources Institute, Hellenic Agricultural Organization, Gorgopotamou Str., Sindos, 57400 Thessaloniki, Greece

Metrology-Assisted Production in Agriculture and Forestry

Abstract submission deadline
closed (28 February 2024)
Manuscript submission deadline
30 April 2024
Viewed by
28798

Topic Information

Dear Colleagues,

According to the Food and Agriculture Organization of the United Nations, climate change will negatively affect food security and create additional pressure on freshwater resources. Thus, a novel scientific endeavor must provide reliable, robust, and applicable management practices that can ensure the sustainability and increase the resilience of the agricultural and forestry sectors to the impacts of climate change whilst accounting for and protecting the sustainability of the environment and its resources. For instance, having spatiotemporal information on soil moisture is key in the decision-making processes of farmers and growers. These can define, for example, when a field can be driven on, when and how much irrigation should be applied, and when the use of fertilizers or pesticides is advisable or necessary. This information can also help farmers provide estimates of when the harvest period will be and how large the anticipated yield will be. The planning and execution of such operations would benefit from an increased availability of real-time data and/or forecasts on the development of soil moisture, soil temperature, meteorological quantities, crop water requirements, and the availability of water resources. To this end, modern agriculture and forestry are becoming more and more data-driven, and the adoption of sensor technology, data acquisition services, and advanced data processing and analysis capabilities is a key factor for the simultaneous increase in the sustainability and productivity of agricultural and forestry operations. This topic plans to give an overview of the most recent advances made in the field of metrology-assisted production in agriculture and forestry and their applications in diverse areas. Potential topics include, but are not limited to: 

  • Agriculture and forestry environmental monitoring;
  • Metrology-assisted production in agriculture and forestry;
  • New sensors and associated signal conditioning for agriculture and forestry;
  • Sensor calibration methods for environmental applications;
  • Tests of sensor performance and telemetry protocols;
  • Model-based forecasts for agriculture and forestry management.

Dr. Heye Bogena
Dr. Cosimo Brogi
Prof. Dr. Christof Huebner
Dr. Andreas Panagopoulos
Topic Editors

Keywords

  •  agriculture and forestry environment monitoring
  •  metrology-assisted production in agriculture and forestry
  •  sensors and associated signal conditioning for agriculture and forestry
  •  sensor calibration methods for environmental applications
  •  tests of sensor performance and telemetry protocols
  •  model-based forecasts for agriculture and forestry management

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Agriculture
agriculture
3.6 3.6 2011 17.7 Days CHF 2600 Submit
Forests
forests
2.9 4.5 2010 16.9 Days CHF 2600 Submit
Sensors
sensors
3.9 6.8 2001 17 Days CHF 2600 Submit

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Published Papers (14 papers)

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26 pages, 4086 KiB  
Article
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
Viewed by 334
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  
Article
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
Viewed by 454
Abstract
In this work, the performance of the TEROS 12 electromagnetic sensor, which measures volumetric soil water content (θ), bulk soil electrical conductivity (σb), 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 (σb), and temperature, is examined for a number of different soils, different θ and different levels of the electrical conductivity of the soil solution (ECW) 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 (ECw) 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 (εa) 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 (θm) and εa0.5 was strongly linear (0.869 < R2 < 0.989), but the linearity of the relation θma0.5 decreases with the increase in bulk EC (σb) 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 εa degrades substantially at values of σb less than 2.5 dS/m while εa returns to near 80 at higher values. Regarding the relation εab, it seems that it is strongly linear and that its slope depends on the pore water electrical conductivity (σp) 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  
Article
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 - 01 Feb 2024
Cited by 1 | Viewed by 648
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 (Kb) 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 (Kb) and soil water content (θ). In fine-textured soils, the conversion of Kb 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 Kb-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). Kb is influenced by T in both soils with contrasting T-Kb relationships. The measured data were fitted using a linear function θ = aKb + 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  
Article
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 1 | Viewed by 1247
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  
Article
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 - 09 Dec 2023
Cited by 2 | Viewed by 925
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  
Article
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 1 | Viewed by 980
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  
Article
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
Viewed by 1463
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  
Article
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
Viewed by 1653
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  
Article
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 4 | Viewed by 1284
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  
Article
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 3 | Viewed by 1546
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  
Article
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
Viewed by 1669
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  
Review
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 8 | Viewed by 9590
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  
Article
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 2 | Viewed by 1842
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  
Article
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 7 | Viewed by 2745
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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