Submit to Sensors Review for Sensors Propose a Special Issue

Journal Browser

► Journal Browser

Smart Sensing Technologies for Agriculture

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (1 November 2019) | Viewed by 66299

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special issue Smart Sensing Technologies for Agriculture book cover image

Share This Special Issue

Special Issue Editors

Prof. Dr. Viacheslav Adamchuk

E-Mail Website
Guest Editor

Department of Bioresource Engineering, 21,111 Lakeshore Road, Ste-Anne-de-Bellevue, QC H9X 3V9, Canada
Interests: development of soil and plant sensor systems; geospatial data processing; navigation of agricultural vehicles; implementation of precision agriculture
Special Issues, Collections and Topics in MDPI journals

Dr. Kenneth Sudduth

E-Mail Website
Guest Editor

USDA-ARS Cropping Systems and Water Quality Research Unit, 269 Agricultural Engineering Building, University of Missouri, Columbia, MO, USA
Interests: engineering and applications of soil and crop sensors; precision agriculture
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Asim Biswas

E-Mail Website
Guest Editor

School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada
Interests: soil physics; vadose zone hydrology; precision agriculture; proximal soil sensing; digital soil mapping; soil spatial variability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

You are invited to submit a manuscript for the upcoming Special Issue on Smart Sensing Technologies for Agriculture. In this issue, we intend to assemble descriptions of cutting-edge research on the development of new and exciting sensing technologies applied to agricultural crops, animals, soil, water, air, machinery, and other elements of modern food and biomaterials production systems. In particular, we expect to include quality sensor development research that will be presented at the Fifth Global Workshop on Proximal Soil Sensing and the 2019 Pedometrics meetings later this year. The Sensors journal maintains a relatively high impact factor and facilitates open access to published scholarly work. Please do not hesitate to ask any questions and pledge to make a submission of a manuscript pertaining to relevant research that is currently at its final stage.

Dr. Viacheslav Adamchuk
Dr. Kenneth Sudduth
Dr. Asim Biswas
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Precision agriculture
Proximal soil sensing
Crop phenotyping
Precision livestock management
Machine vision in agriculture

Published Papers (14 papers)

Download All Papers

Research

18 pages, 4089 KiB

Open AccessArticle

Development of Willow Tree Yield-Mapping Technology

by Maxime Leclerc, Viacheslav Adamchuk, Jaesung Park and Xavier Lachapelle-T.

Sensors 2020, 20(9), 2650; https://0-doi-org.brum.beds.ac.uk/10.3390/s20092650 - 06 May 2020

Viewed by 2432

Abstract

With today’s environmental challenges, developing sustainable energy sources is crucial. From this perspective, woody biomass has been, and continues to be, a significant research interest. The goal of this research was to develop new technology for mapping willow tree yield grown in a short-rotation forestry (SRF) system. The system gathered the physical characteristics of willow trees on-the-go, while the trees were being harvested. Features assessed include the number of trees harvested and their diameter. To complete this task, a machine-vision system featuring an RGB-D stereovision camera was built. The system tagged these data with the corresponding geographical coordinates using a Global Navigation Satellite System (GNSS) receiver. The proposed yield-mapping system showed promising detection results considering the complex background and variable light conditions encountered in the outdoors. Of the 40 randomly selected and manually observed trees in a row, 36 were successfully detected, yielding a 90% detection rate. The correctly detected tree rate of all trees within the scenes was actually 71.8% since the system tended to be sensitive to branches, thus, falsely detecting them as trees. Manual validation of the diameter estimation function showed a poor coefficient of determination and a root mean square error (RMSE) of 10.7 mm. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Agriculture)

► Show Figures

Figure 1

15 pages, 99154 KiB

Open AccessArticle

High Speed Crop and Weed Identification in Lettuce Fields for Precision Weeding

by Lydia Elstone, Kin Yau How, Samuel Brodie, Muhammad Zulfahmi Ghazali, William P. Heath and Bruce Grieve

Sensors 2020, 20(2), 455; https://0-doi-org.brum.beds.ac.uk/10.3390/s20020455 - 14 Jan 2020

Cited by 36 | Viewed by 5401

Abstract

Precision weeding can significantly reduce or even eliminate the use of herbicides in farming. To achieve high-precision, individual targeting of weeds, high-speed, low-cost plant identification is essential. Our system using the red, green, and near-infrared reflectance, combined with a size differentiation method, is used to identify crops and weeds in lettuce fields. Illumination is provided by LED arrays at 525, 650, and 850 nm, and images are captured in a single-shot using a modified RGB camera. A kinematic stereo method is utilised to compensate for parallax error in images and provide accurate location data of plants. The system was verified in field trials across three lettuce fields at varying growth stages from 0.5 to 10 km/h. In-field results showed weed and crop identification rates of 56% and 69%, respectively. Post-trial processing resulted in average weed and crop identifications of 81% and 88%, respectively. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Agriculture)

► Show Figures

Figure 1

16 pages, 7447 KiB

Open AccessArticle

Real-Time Electrical Resistivity Measurement and Mapping Platform of the Soils with an Autonomous Robot for Precision Farming Applications

by İlker Ünal, Önder Kabaş and Salih Sözer

Sensors 2020, 20(1), 251; https://0-doi-org.brum.beds.ac.uk/10.3390/s20010251 - 01 Jan 2020

Cited by 10 | Viewed by 7399

Abstract

Soil electrical resistivity (ER) is an important indicator to indirectly determine soil physical and chemical properties such as moisture, salinity, porosity, organic matter level, bulk density, and soil texture. In this study, real-time ER measurement system has been developed with the help of an autonomous robot. The aim of this study is to provide rapid measurement of the ER in large areas using the Wenner four-probe measurement method for precision farming applications. The ER measurement platform consists of the Wenner probes, a y-axis shifter driven by a DC motor through a gear reducer, all installed on a steel-frame that mount to an autonomous robot. An embedded industrial computer and differential global positioning system (DGPS) were used to assist in real-time measuring, recording, mapping, and displaying the ER and the robot position during the field operation. The data acquisition software was codded in Microsoft Visual Basic.NET. Field experiments were carried out in a 1.2 ha farmland soil. ER and DGPS values were stored in Microsoft SQL Server 2005 database, an ordinary Kriging interpolation technique by ArcGIS was used and the average ER values were mapped for the soil depth between 0 and 50 cm. As a result, ER values were observed to be between 30.757 and 70.732 ohm-m. In conclusion, the experimental results showed that the designed system works quite well in the field and the ER measurement platform is a practical tool for providing real-time soil ER measurements. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Agriculture)

► Show Figures

Figure 1

15 pages, 8361 KiB

Open AccessArticle

An Autonomous Fruit and Vegetable Harvester with a Low-Cost Gripper Using a 3D Sensor

by Tan Zhang, Zhenhai Huang, Weijie You, Jiatao Lin, Xiaolong Tang and Hui Huang

Sensors 2020, 20(1), 93; https://0-doi-org.brum.beds.ac.uk/10.3390/s20010093 - 22 Dec 2019

Cited by 39 | Viewed by 8354

Abstract

Reliable and robust systems to detect and harvest fruits and vegetables in unstructured environments are crucial for harvesting robots. In this paper, we propose an autonomous system that harvests most types of crops with peduncles. A geometric approach is first applied to obtain the cutting points of the peduncle based on the fruit bounding box, for which we have adapted the model of the state-of-the-art object detector named Mask Region-based Convolutional Neural Network (Mask R-CNN). We designed a novel gripper that simultaneously clamps and cuts the peduncles of crops without contacting the flesh. We have conducted experiments with a robotic manipulator to evaluate the effectiveness of the proposed harvesting system in being able to efficiently harvest most crops in real laboratory environments. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Agriculture)

► Show Figures

Figure 1

14 pages, 3658 KiB

Open AccessArticle

An Automatic Head Surface Temperature Extraction Method for Top-View Thermal Image with Individual Broiler

by Xingguo Xiong, Mingzhou Lu, Weizhong Yang, Guanghui Duan, Qingyan Yuan, Mingxia Shen, Tomas Norton and Daniel Berckmans

Sensors 2019, 19(23), 5286; https://0-doi-org.brum.beds.ac.uk/10.3390/s19235286 - 30 Nov 2019

Cited by 15 | Viewed by 3156

Abstract

Surface temperature variation in a broiler’s head can be used as an indicator of its health status. Surface temperatures in the existing thermograph based animal health assessment studies were mostly obtained manually. 2185 thermal images, each of which had an individual broiler, were captured from 20 broilers. Where 15 broilers served as the experimental group, they were injected with 0.1mL of pasteurella inoculum. The rest, 5 broilers, served as the control group. An algorithm was developed to extract head surface temperature automatically from the top-view broiler thermal image. Adaptive K-means clustering and ellipse fitting were applied to locate the broiler’s head region. The maximum temperature inside the head region was extracted as the head surface temperature. The developed algorithm was tested in Matlab^® (R2016a) and the testing results indicated that the head region in 92.77% of the broiler thermal images could be located correctly. The maximum error of the extracted head surface temperatures was not greater than 0.1 °C. Different trend features were observed in the smoothed head surface temperature time series of the broilers in experimental and control groups. Head surface temperature extracted by the presented algorithm lays a foundation for the development of an automatic system for febrile broiler identification. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Agriculture)

► Show Figures

Figure 1

16 pages, 4144 KiB

Open AccessArticle

Comparison of Calibration Approaches in Laser-Induced Breakdown Spectroscopy for Proximal Soil Sensing in Precision Agriculture

by Daniel Riebe, Alexander Erler, Pia Brinkmann, Toralf Beitz, Hans-Gerd Löhmannsröben and Robin Gebbers

Sensors 2019, 19(23), 5244; https://0-doi-org.brum.beds.ac.uk/10.3390/s19235244 - 28 Nov 2019

Cited by 18 | Viewed by 3263

Abstract

The lack of soil data, which are relevant, reliable, affordable, immediately available, and sufficiently detailed, is still a significant challenge in precision agriculture. A promising technology for the spatial assessment of the distribution of chemical elements within fields, without sample preparation is laser-induced breakdown spectroscopy (LIBS). Its advantages are contrasted by a strong matrix dependence of the LIBS signal which necessitates careful data evaluation. In this work, different calibration approaches for soil LIBS data are presented. The data were obtained from 139 soil samples collected on two neighboring agricultural fields in a quaternary landscape of northeast Germany with very variable soils. Reference analysis was carried out by inductively coupled plasma optical emission spectroscopy after wet digestion. The major nutrients Ca and Mg and the minor nutrient Fe were investigated. Three calibration strategies were compared. The first method was based on univariate calibration by standard addition using just one soil sample and applying the derived calibration model to the LIBS data of both fields. The second univariate model derived the calibration from the reference analytics of all samples from one field. The prediction is validated by LIBS data of the second field. The third method is a multivariate calibration approach based on partial least squares regression (PLSR). The LIBS spectra of the first field are used for training. Validation was carried out by 20-fold cross-validation using the LIBS data of the first field and independently on the second field data. The second univariate method yielded better calibration and prediction results compared to the first method, since matrix effects were better accounted for. PLSR did not strongly improve the prediction in comparison to the second univariate method. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Agriculture)

► Show Figures

Graphical abstract

14 pages, 5748 KiB

Open AccessArticle

Simplifying Sample Preparation for Soil Fertility Analysis by X-ray Fluorescence Spectrometry

by Tiago Rodrigues Tavares, Lidiane Cristina Nunes, Elton Eduardo Novais Alves, Eduardo de Almeida, Leonardo Felipe Maldaner, Francisco José Krug, Hudson Wallace Pereira de Carvalho and José Paulo Molin

Sensors 2019, 19(23), 5066; https://0-doi-org.brum.beds.ac.uk/10.3390/s19235066 - 20 Nov 2019

Cited by 23 | Viewed by 4504

Abstract

Portable X-ray fluorescence (pXRF) sensors allow one to collect digital data in a practical and environmentally friendly way, as a complementary method to traditional laboratory analyses. This work aimed to assess the performance of a pXRF sensor to predict exchangeable nutrients in soil samples by using two contrasting strategies of sample preparation: pressed pellets and loose powder (<2 mm). Pellets were prepared using soil and a cellulose binder at 10% w w⁻¹ followed by grinding for 20 min. Sample homogeneity was probed by X-ray fluorescence microanalysis. Exchangeable nutrients were assessed by pXRF furnished with a Rh X-ray tube and silicon drift detector. The calibration models were obtained using 58 soil samples and leave-one-out cross-validation. The predictive capabilities of the models were appropriate for both exchangeable K (ex-K) and Ca (ex-Ca) determinations with R² ≥ 0.76 and RPIQ > 2.5. Although XRF analysis of pressed pellets allowed a slight gain in performance over loose powder samples for the prediction of ex-K and ex-Ca, satisfactory performances were also obtained with loose powders, which require minimal sample preparation. The prediction models with local samples showed promising results and encourage more detailed investigations for the application of pXRF in tropical soils. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Agriculture)

► Show Figures

Figure 1

20 pages, 8159 KiB

Open AccessArticle

Body Dimension Measurements of Qinchuan Cattle with Transfer Learning from LiDAR Sensing

by Lvwen Huang, Han Guo, Qinqin Rao, Zixia Hou, Shuqin Li, Shicheng Qiu, Xinyun Fan and Hongyan Wang

Sensors 2019, 19(22), 5046; https://0-doi-org.brum.beds.ac.uk/10.3390/s19225046 - 19 Nov 2019

Cited by 24 | Viewed by 5785

Abstract

For the time-consuming and stressful body measuring task of Qinchuan cattle and farmers, the demand for the automatic measurement of body dimensions has become more and more urgent. It is necessary to explore automatic measurements with deep learning to improve breeding efficiency and promote the development of industry. In this paper, a novel approach to measuring the body dimensions of live Qinchuan cattle with on transfer learning is proposed. Deep learning of the Kd-network was trained with classical three-dimensional (3D) point cloud datasets (PCD) of the ShapeNet datasets. After a series of processes of PCD sensed by the light detection and ranging (LiDAR) sensor, the cattle silhouettes could be extracted, which after augmentation could be applied as an input layer to the Kd-network. With the output of a convolutional layer of the trained deep model, the output layer of the deep model could be applied to pre-train the full connection network. The TrAdaBoost algorithm was employed to transfer the pre-trained convolutional layer and full connection of the deep model. To classify and recognize the PCD of the cattle silhouette, the average accuracy rate after training with transfer learning could reach up to 93.6%. On the basis of silhouette extraction, the candidate region of the feature surface shape could be extracted with mean curvature and Gaussian curvature. After the computation of the FPFH (fast point feature histogram) of the surface shape, the center of the feature surface could be recognized and the body dimensions of the cattle could finally be calculated. The experimental results showed that the comprehensive error of body dimensions was close to 2%, which could provide a feasible approach to the non-contact observations of the bodies of large physique livestock without any human intervention. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Agriculture)

► Show Figures

Figure 1

10 pages, 2049 KiB

Open AccessArticle

An ISE-based On-Site Soil Nitrate Nitrogen Detection System

by Yanhua Li, Qingliang Yang, Ming Chen, Maohua Wang and Miao Zhang

Sensors 2019, 19(21), 4669; https://0-doi-org.brum.beds.ac.uk/10.3390/s19214669 - 28 Oct 2019

Cited by 9 | Viewed by 5013

Abstract

Soil nitrate–nitrogen (NO₃⁻-N) is one of the primary factors used to control nitrogen topdressing application during the crop growth period. The ion-selective electrode (ISE) is a promising method for rapid lower-cost in-field detection. Due to the simplification of sample preparation, the accuracy and stability of ISE-based in-field detection is doubted. In this paper, a self-designed prototype system for on-site soil NO₃⁻-N detection was developed. The procedure of spinning centrifugation was used to avoid interference from soil slurry suspension. A modified Nernstian prediction model was quantitatively characterized with outputs from both the ISE and the soil moisture sensor. The measurement accuracy of the sensor fusion model was comparable with the laboratory ISE detections with standard sample pretreatment. Compared with the standard spectrometric method, the average absolute error (AE) and root-mean-square error (RMSE) were found to be less than 4.7 and 6.1 mg/L, respectively. The on-site soil testing efficiency was 4–5 min/sample, which reduced the operation time by 60% compared with manual sample preparation. The on-site soil NO₃⁻-N status was dynamically monitored for 42 consecutive days. The declining peak of NO₃⁻-N was observed. In all, the designed ISE-based detection system demonstrated a promising capability for the dynamic on-site monitoring of soil macronutrients. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Agriculture)

► Show Figures

Figure 1

16 pages, 6487 KiB

Open AccessArticle

Evaluating Soil-Borne Causes of Biomass Variability in Grassland by Remote and Proximal Sensing

by Sebastian Vogel, Robin Gebbers, Marcel Oertel and Eckart Kramer

Sensors 2019, 19(20), 4593; https://0-doi-org.brum.beds.ac.uk/10.3390/s19204593 - 22 Oct 2019

Cited by 9 | Viewed by 3666

Abstract

On a grassland field with sandy soils in Northeast Germany (Brandenburg), vegetation indices from multi-spectral UAV-based remote sensing were used to predict grassland biomass productivity. These data were combined with soil pH value and apparent electrical conductivity (ECa) from on-the-go proximal sensing serving as indicators for soil-borne causes of grassland biomass variation. The field internal magnitude of spatial variability and hidden correlations between the variables of investigation were analyzed by means of geostatistics and boundary-line analysis to elucidate the influence of soil pH and ECa on the spatial distribution of biomass. Biomass and pH showed high spatial variability, which necessitates high resolution data acquisition of soil and plant properties. Moreover, boundary-line analysis showed grassland biomass maxima at pH values between 5.3 and 7.2 and ECa values between 3.5 and 17.5 mS m⁻¹. After calibrating ECa to soil moisture, the ECa optimum was translated to a range of optimum soil moisture from 7% to 13%. This matches well with to the plant-available water content of the predominantly sandy soil as derived from its water retention curve. These results can be used in site-specific management decisions to improve grassland biomass productivity in low-yield regions of the field due to soil acidity or texture-related water scarcity. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Agriculture)

► Show Figures

Figure 1

12 pages, 2953 KiB

Open AccessArticle

Coupling Waveguide-Based Micro-Sensors and Spectral Multivariate Analysis to Improve Spray Deposit Characterization in Agriculture

by Anis Taleb Bendiab, Maxime Ryckewaert, Daphné Heran, Raphaël Escalier, Raphaël K. Kribich, Caroline Vigreux and Ryad Bendoula

Sensors 2019, 19(19), 4168; https://0-doi-org.brum.beds.ac.uk/10.3390/s19194168 - 26 Sep 2019

Cited by 4 | Viewed by 2902

Abstract

The leaf coverage surface is a key measurement of the spraying process to maximize spray efficiency. To determine leaf coverage surface, the development of optical micro-sensors that, coupled with a multivariate spectral analysis, will be able to measure the volume of the droplets deposited on their surface is proposed. Rib optical waveguides based on Ge-Se-Te chalcogenide films were manufactured and their light transmission was studied as a response to the deposition of demineralized water droplets on their surface. The measurements were performed using a dedicated spectrophotometric bench to record the transmission spectra at the output of the waveguides, before (reference) and after drop deposition, in the wavelength range between 1200 and 2000 nm. The presence of a hollow at 1450 nm in the relative transmission spectra has been recorded. This corresponds to the first overtone of the O–H stretching vibration in water. This result tends to show that the optical intensity decrease observed after droplet deposition is partly due to absorption by water of the light energy carried by the guided mode evanescent field. The probe based on Ge-Se-Te rib optical waveguides is thus sensitive throughout the whole range of volumes studied, i.e., from 0.1 to 2.5 μL. Principal Component Analysis and Partial Least Square as multivariate techniques then allowed the analysis of the statistics of the measurements and the predictive character of the transmission spectra. It confirmed the sensitivity of the measurement system to the water absorption, and the predictive model allowed the prediction of droplet volumes on an independent set of measurements, with a correlation of 66.5% and a precision of 0.39 μL. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Agriculture)

► Show Figures

Figure 1

19 pages, 4214 KiB

Open AccessArticle

Three-Dimensional Mapping of Clay and Cation Exchange Capacity of Sandy and Infertile Soil Using EM38 and Inversion Software

by Tibet Khongnawang, Ehsan Zare, Dongxue Zhao, Pranee Srihabun and John Triantafilis

Sensors 2019, 19(18), 3936; https://0-doi-org.brum.beds.ac.uk/10.3390/s19183936 - 12 Sep 2019

Cited by 16 | Viewed by 3431

Abstract

Most cultivated upland areas of northeast Thailand are characterized by sandy and infertile soils, which are difficult to improve agriculturally. Information about the clay (%) and cation exchange capacity (CEC—cmol(+)/kg) are required. Because it is expensive to analyse these soil properties, electromagnetic (EM) induction instruments are increasingly being used. This is because the measured apparent soil electrical conductivity (EC_a—mS/m), can often be correlated directly with measured topsoil (0–0.3 m), subsurface (0.3–0.6 m) and subsoil (0.6–0.9 m) clay and CEC. In this study, we explore the potential to use this approach and considering a linear regression (LR) between EM38 acquired EC_a in horizontal (EC_ah) and vertical (EC_av) modes of operation and the soil properties at each of these depths. We compare this approach with a universal LR relationship developed between calculated true electrical conductivity (σ—mS/m) and laboratory measured clay and CEC at various depths. We estimate σ by inverting EC_ah and EC_av data, using a quasi-3D inversion algorithm (EM4Soil). The best LR between EC_a and soil properties was between EC_ah and subsoil clay (R² = 0.43) and subsoil CEC (R² = 0.56). We concluded these LR were unsatisfactory to predict clay or CEC at any of the three depths, however. In comparison, we found that a universal LR could be established between σ with clay (R² = 0.65) and CEC (R² = 0.68). The LR model validation was tested using a leave-one-out-cross-validation. The results indicated that the universal LR between σ and clay at any depth was precise (RMSE = 2.17), unbiased (ME = 0.27) with good concordance (Lin’s = 0.78). Similarly, satisfactory results were obtained by the LR between σ and CEC (Lin’s = 0.80). We conclude that in a field where a direct LR relationship between clay or CEC and EC_a cannot be established, can still potentially be mapped by developing a LR between estimates of σ with clay or CEC if they all vary with depth. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Agriculture)

► Show Figures

Figure 1

15 pages, 6537 KiB

Open AccessArticle

Deep Learning and Machine Vision Approaches for Posture Detection of Individual Pigs

by Abozar Nasirahmadi, Barbara Sturm, Sandra Edwards, Knut-Håkan Jeppsson, Anne-Charlotte Olsson, Simone Müller and Oliver Hensel

Sensors 2019, 19(17), 3738; https://0-doi-org.brum.beds.ac.uk/10.3390/s19173738 - 29 Aug 2019

Cited by 98 | Viewed by 7325

Abstract

Posture detection targeted towards providing assessments for the monitoring of health and welfare of pigs has been of great interest to researchers from different disciplines. Existing studies applying machine vision techniques are mostly based on methods using three-dimensional imaging systems, or two-dimensional systems with the limitation of monitoring under controlled conditions. Thus, the main goal of this study was to determine whether a two-dimensional imaging system, along with deep learning approaches, could be utilized to detect the standing and lying (belly and side) postures of pigs under commercial farm conditions. Three deep learning-based detector methods, including faster regions with convolutional neural network features (Faster R-CNN), single shot multibox detector (SSD) and region-based fully convolutional network (R-FCN), combined with Inception V2, Residual Network (ResNet) and Inception ResNet V2 feature extractions of RGB images were proposed. Data from different commercial farms were used for training and validation of the proposed models. The experimental results demonstrated that the R-FCN ResNet101 method was able to detect lying and standing postures with higher average precision (AP) of 0.93, 0.95 and 0.92 for standing, lying on side and lying on belly postures, respectively and mean average precision (mAP) of more than 0.93. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Agriculture)

► Show Figures

Figure 1

15 pages, 6469 KiB

Open AccessArticle

Automated Measurement and Control of Germination Paper Water Content

by Lina Owino, Marvin Hilkens, Friederike Kögler and Dirk Söffker

Sensors 2019, 19(10), 2232; https://0-doi-org.brum.beds.ac.uk/10.3390/s19102232 - 14 May 2019

Viewed by 2465

Abstract

Germination paper (GP) is used as a growth substrate in plant development studies. Current studies bear two limitations: (1) The actual GP water content and variations in GP water content are neglected. (2) Existing irrigation methods either maintain the GP water content at fully sufficient or at a constant deficit. Variation of the intensity of water deficit over time for plants grown on GP is not directly achievable using these methods. In this contribution, a new measurement and control approach was presented. As a first step, a more precise measurement of water content was realized by employing the discharging process of capacitors to determine the electrical resistance of GP, which is related to the water content. A Kalman filter using an evapotranspiration model in combination with experimental data was used to refine the measurements, serving as the input for a model predictive controller (MPC). The MPC was used to improve the dynamics of the irrigation amount to more precisely achieve the required water content for regulated water uptake in plant studies. This is important in studies involving deficit irrigation. The novel method described was capable of increasing the accuracy of GP water content control. As a first step, the measurement system achieved an improved accuracy of 0.22 g/g. The application of a MPC for water content control based on the improved measurement results in an overall control accuracy was 0.09 g/g. This method offers a new approach, allowing the use of GP for studies with varying water content. This addressed the limitations of existing plant growth studies and allowed the prospection of dependencies between dynamic water deficit and plant development using GP as a growth substrate for research studies. Full article

(This article belongs to the Special Issue Smart Sensing Technologies for Agriculture)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Smart Sensing Technologies for Agriculture

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (14 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI