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Dr. Heye Bogena

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Forschungszentrum Jülich, Institute of Bio- and Geosciences: Agrosphere (IBG-3), 52428 Jülich, Germany

Dr. Cosimo Brogi

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Forschungszentrum Jülich, Institute of Bio-and Geosciences: Agrosphere (IBG-3), 52428 Jülich, Germany

Prof. Dr. Christof Huebner

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Department of Electrical Engineering, University of Applied Sciences Mannheim, Paul-Wittsack-Str. 10, 68163 Mannheim, Germany

Dr. Andreas Panagopoulos

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Soil and Water Resources Institute, Hellenic Agricultural Organization, Gorgopotamou Str., 57400 Sindos, Thessaloniki, Greece

Metrology-Assisted Production in Agriculture and Forestry

Abstract submission deadline

closed (28 February 2023)

Manuscript submission deadline

closed (30 April 2023)

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13975

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	3.408	3.1	2011	18.6 Days	2000 CHF
Forests forests	3.282	4.0	2010	18.3 Days	2000 CHF
Sensors sensors	3.847	6.4	2001	15 Days	2400 CHF

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Published Papers (7 papers)

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Open AccessArticle

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

Iphigenia Keramitsoglou

Panagiotis Sismanidis

and

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

Viewed by 553

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 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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Open AccessArticle

Detection of Chrysanthemums Inflorescence Based on Improved CR-YOLOv5s Algorithm

Wentao Zhao

Dasheng Wu

and

Xinyu Zheng

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

Cited by 1 | Viewed by 558

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 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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Open AccessArticle

Monitoring Irrigation in Small Orchards with Cosmic-Ray Neutron Sensors

Harrie-Jan Hendricks Franssen

and

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

Viewed by 741

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 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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Open AccessArticle

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

and

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

Viewed by 880

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 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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Open AccessReview

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

Heye Reemt Bogena

Ansgar Weuthen

and

Johan Alexander Huisman

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

Viewed by 7786

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 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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Open AccessArticle

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

and

Leonard Kofitse Amekudzi

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

Cited by 1 | Viewed by 931

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 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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Open AccessArticle

Robust Soil Water Potential Sensor to Optimize Irrigation in Agriculture

and

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

Cited by 2 | Viewed by 1593

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 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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