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Sensors for Animal Health Monitoring and Precision Livestock Farming

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

Deadline for manuscript submissions: closed (15 June 2022) | Viewed by 43078

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Special Issue Editors

Prof. Dr. Craig Michie

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Guest Editor

Department of Electronic & Electrical Engineering, University of Strathclyde, 204 George Street, Glasgow G1 1XW, UK
Interests: wireless sensor networks; Internet-of-Things; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Ivan Andonovic

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Guest Editor

Department of Electrical and Electronic Engineering, University of Strathclyde, Glasgow G1 1RD, UK
Interests: wireless sensors; machine learning; sensor system; agriculture
Special Issues, Collections and Topics in MDPI journals

Dr. Christos Tachtatzis

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Guest Editor

Department of Electronic and Electrical Engineering, University of Strathclyde, 16 Richmond Street, Glasgow G1 1XQ, UK
Interests: machine learning; partial discharge monitoring; wireless technologies; data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The global population is predicted to increase to 9 Billion by 2050. Combined with climate change and pressure on available land, this is placing increasing demands on the agricultural sector to produce high quality foods efficiently to feed this growing population with minimal environmental footprint. In the dairy sector, the average farm size has grown as farms have combined and consolidated to reap the benefit of scale. While this can be beneficial, increases in farm size mean that farmers have less time to undertake essential routine tasks traditionally carried out by visually observing their herds. Technology has played a key supporting role in this context, enabling farmers to monitor cattle 24 hours per day to detect when they are coming into heat thus optimising the insemination process and overall herd fertility. In addition, equipment vendors are increasingly offering solutions that identify the location of animals, or give an indication of the time that they spend feeding, ruminating, lying or walking. All of these parameters give insights into behaviours that relate to operational efficiency and animal welfare.

This Special Issue will contribute to the state-of-the-art and present precision farming solutions and applications enabled by monitoring technologies within animal monitoring. The Guest Editors invite papers related to the following topics:

Localisation of animals
Wireless sensor technologies
Machine Learning and Data Processing in agriculture
Image processing and Computer Vision
Detection of animal behaviour
Measurement of parameters that relate to animal welfare
Technologies that optimise the use of feed
Technologies that aid the quantification of greenhouse gases
Algorithms and data analytics that relate to any of the above

Prof. Craig Michie
Prof. Ivan Andonovic
Dr. Christos Tachtatzis
Guest Editors

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Keywords

Animal welfare
Activity monitoring
Green house gases
Feed efficiency
Wireless communications
Artificial intelligence
Machine learning
Data analytics
Computer vision

Published Papers (13 papers)

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9 pages, 3167 KiB

Open AccessCommunication

Periodicity Intensity of the 24 h Circadian Rhythm in Newborn Calves Show Indicators of Herd Welfare

by Victoria Rhodes, Maureen Maguire, Meghana Shetty, Conor McAloon and Alan F. Smeaton

Sensors 2022, 22(15), 5843; https://doi.org/10.3390/s22155843 - 04 Aug 2022

Cited by 6 | Viewed by 1694

Abstract

Circadian rhythms are a process of the sleep-wake cycle that regulates the physical, mental and behavioural changes in all living beings with a period of roughly 24 h. Wearable accelerometers are typically used in livestock applications to record animal movement from which we can estimate the activity type. Here, we use the overall movement recorded by accelerometers worn on the necks of newborn calves for a period of 8 weeks. From the movement data, we calculate 24 h periodicity intensities corresponding to circadian rhythms, from a 7-day window that slides through up to 8-weeks of data logging. The strength or intensity of the 24 h periodicity is computed at intervals as the calves become older, which is an indicator of individual calf welfare. We observe that the intensities of these 24 h periodicities for individual calves, derived from movement data, increase and decrease synchronously in a herd of 19 calves. Our results show that external factors affecting the welfare of the herd can be observed by processing and visualising movement data in this way and our method reveals insights that are not observable from movement data alone. Full article

(This article belongs to the Special Issue Sensors for Animal Health Monitoring and Precision Livestock Farming)

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13 pages, 1844 KiB

Open AccessArticle

Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks

by Nina Volkmann, Claudius Zelenka, Archana Malavalli Devaraju, Johannes Brünger, Jenny Stracke, Birgit Spindler, Nicole Kemper and Reinhard Koch

Sensors 2022, 22(14), 5188; https://doi.org/10.3390/s22145188 - 11 Jul 2022

Cited by 8 | Viewed by 1831

Abstract

Injurious pecking against conspecifics is a serious problem in turkey husbandry. Bloody injuries act as a trigger mechanism to induce further pecking, and timely detection and intervention can prevent massive animal welfare impairments and costly losses. Thus, the overarching aim is to develop a camera-based system to monitor the flock and detect injuries using neural networks. In a preliminary study, images of turkeys were annotated by labelling potential injuries. These were used to train a network for injury detection. Here, we applied a keypoint detection model to provide more information on animal position and indicate injury location. Therefore, seven turkey keypoints were defined, and 244 images (showing 7660 birds) were manually annotated. Two state-of-the-art approaches for pose estimation were adjusted, and their results were compared. Subsequently, a better keypoint detection model (HRNet-W48) was combined with the segmentation model for injury detection. For example, individual injuries were classified using “near tail” or “near head” labels. Summarizing, the keypoint detection showed good results and could clearly differentiate between individual animals even in crowded situations. Full article

(This article belongs to the Special Issue Sensors for Animal Health Monitoring and Precision Livestock Farming)

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27 pages, 6074 KiB

Open AccessArticle

Evaluation of a Binary Classification Approach to Detect Herbage Scarcity Based on Behavioral Responses of Grazing Dairy Cows

by Leonie Hart, Uta Dickhoefer, Esther Paulenz and Christina Umstaetter

Sensors 2022, 22(3), 968; https://doi.org/10.3390/s22030968 - 26 Jan 2022

Cited by 4 | Viewed by 2868

Abstract

In precision grazing, pasture allocation decisions are made continuously to ensure demand-based feed allowance and efficient grassland utilization. The aim of this study was to evaluate existing prediction models that determine feed scarcity based on changes in dairy cow behavior. During a practice-oriented experiment, two groups of 10 cows each grazed separate paddocks in half-days in six six-day grazing cycles. The allocated grazing areas provided 20% less feed than the total dry matter requirement of the animals for each entire grazing cycle. All cows were equipped with noseband sensors and pedometers to record their head, jaw, and leg activity. Eight behavioral variables were used to classify herbage sufficiency or scarcity using a generalized linear model and a random forest model. Both predictions were compared to two individual-animal and day-specific reference indicators for feed scarcity: reduced milk yields and rumen fill scores that undercut normal variation. The predictive performance of the models was low. The two behavioral variables “daily rumination chews” and “bite frequency” were confirmed as suitable predictors, the latter being particularly sensitive when new feed allocation is present in the grazing set-up within 24 h. Important aspects were identified to be considered if the modeling approach is to be followed up. Full article

(This article belongs to the Special Issue Sensors for Animal Health Monitoring and Precision Livestock Farming)

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16 pages, 2225 KiB

Open AccessArticle

What Are Sheep Doing? Tri-Axial Accelerometer Sensor Data Identify the Diel Activity Pattern of Ewe Lambs on Pasture

by Seer J. Ikurior, Nelly Marquetoux, Stephan T. Leu, Rene A. Corner-Thomas, Ian Scott and William E. Pomroy

Sensors 2021, 21(20), 6816; https://doi.org/10.3390/s21206816 - 13 Oct 2021

Cited by 9 | Viewed by 3357

Abstract

Monitoring activity patterns of animals offers the opportunity to assess individual health and welfare in support of precision livestock farming. The purpose of this study was to use a triaxial accelerometer sensor to determine the diel activity of sheep on pasture. Six Perendale ewe lambs, each fitted with a neck collar mounting a triaxial accelerometer, were filmed during targeted periods of sheep activities: grazing, lying, walking, and standing. The corresponding acceleration data were fitted using a Random Forest algorithm to classify activity (=classifier). This classifier was then applied to accelerometer data from an additional 10 ewe lambs to determine their activity budgets. Each of these was fitted with a neck collar mounting an accelerometer as well as two additional accelerometers placed on a head halter and a body harness over the shoulders of the animal. These were monitored continuously for three days. A classification accuracy of 89.6% was achieved for the grazing, walking and resting activities (i.e., a new class combining lying and standing activity). Triaxial accelerometer data showed that sheep spent 64% (95% CI 55% to 74%) of daylight time grazing, with grazing at night reduced to 14% (95% CI 8% to 20%). Similar activity budgets were achieved from the halter mounted sensors, but not those on a body harness. These results are consistent with previous studies directly observing daily activity of pasture-based sheep and can be applied in a variety of contexts to investigate animal health and welfare metrics e.g., to better understand the impact that young sheep can suffer when carrying even modest burdens of parasitic nematodes. Full article

(This article belongs to the Special Issue Sensors for Animal Health Monitoring and Precision Livestock Farming)

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16 pages, 7868 KiB

Open AccessArticle

Visual Diagnosis of the Varroa Destructor Parasitic Mite in Honeybees Using Object Detector Techniques

by Simon Bilik, Lukas Kratochvila, Adam Ligocki, Ondrej Bostik, Tomas Zemcik, Matous Hybl, Karel Horak and Ludek Zalud

Sensors 2021, 21(8), 2764; https://doi.org/10.3390/s21082764 - 14 Apr 2021

Cited by 27 | Viewed by 5124

Abstract

The Varroa destructor mite is one of the most dangerous Honey Bee (Apis mellifera) parasites worldwide and the bee colonies have to be regularly monitored in order to control its spread. In this paper we present an object detector based method for health state monitoring of bee colonies. This method has the potential for online measurement and processing. In our experiment, we compare the YOLO and SSD object detectors along with the Deep SVDD anomaly detector. Based on the custom dataset with 600 ground-truth images of healthy and infected bees in various scenes, the detectors reached the highest F1 score up to 0.874 in the infected bee detection and up to 0.714 in the detection of the Varroa destructor mite itself. The results demonstrate the potential of this approach, which will be later used in the real-time computer vision based honey bee inspection system. To the best of our knowledge, this study is the first one using object detectors for the Varroa destructor mite detection on a honey bee. We expect that performance of those object detectors will enable us to inspect the health status of the honey bee colonies in real time. Full article

(This article belongs to the Special Issue Sensors for Animal Health Monitoring and Precision Livestock Farming)

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10 pages, 592 KiB

Open AccessCommunication

Relation of Automated Body Condition Scoring System and Inline Biomarkers (Milk Yield, β-Hydroxybutyrate, Lactate Dehydrogenase and Progesterone in Milk) with Cow’s Pregnancy Success

by Ramūnas Antanaitis, Vida Juozaitienė, Dovilė Malašauskienė, Mindaugas Televičius, Mingaudas Urbutis and Walter Baumgartner

Sensors 2021, 21(4), 1414; https://doi.org/10.3390/s21041414 - 18 Feb 2021

Cited by 5 | Viewed by 2426

Abstract

The aim of the current study was to evaluate the relation of automatically determined body condition score (BCS) and inline biomarkers such as β-hydroxybutyrate (BHB), milk yield (MY), lactate dehydrogenase (LDH), and progesterone (mP4) with the pregnancy success of cows. The cows (n = 281) had 2.1 ± 0.1. lactations on average, were 151.6 ± 0.06 days postpartum, and were once tested with “Easy scan” ultrasound (IMV imaging, Scotland) at 30–35 d post-insemination. According to their reproductive status, cows were grouped into two groups: non-pregnant (n = 194 or 69.0% of cows) and pregnant (n = 87 or 31.0% of cows). Data concerning their BCS, mP4, MY, BHB, and LDH were collected each day from the day of insemination for 7 days. The BCS was collected with body condition score camera (DeLaval Inc., Tumba, Sweden); mP4, MY, BHB, and LDH were collected with the fully automated real-time analyzer Herd Navigator™ (Lattec I/S, Hillerød, Denmark) in combination with a DeLaval milking robot (DeLaval Inc., Tumba, Sweden). Of all the biomarkers, three differences between groups were significant. The body condition score (BCS) of the pregnant cows was higher (+0.49 score), the milk yield (MY) was lower (−4.36 kg), and milk progesterone in pregnant cows was (+6.11 ng/mL) higher compared to the group of non-pregnant cows (p < 0.001). The pregnancy status of the cows was associated with their BCS assessment (p < 0.001). We estimated that cows with BCS > 3.2 were 22 times more likely to have reproductive success than cows with BCS ≤ 3.2. Full article

(This article belongs to the Special Issue Sensors for Animal Health Monitoring and Precision Livestock Farming)

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21 pages, 2027 KiB

Open AccessArticle

The Importance of Low Daily Risk for the Prediction of Treatment Events of Individual Dairy Cows with Sensor Systems

by Christian Post, Christian Rietz, Wolfgang Büscher and Ute Müller

Sensors 2021, 21(4), 1389; https://doi.org/10.3390/s21041389 - 17 Feb 2021

Cited by 6 | Viewed by 2619

Abstract

The prediction of health disorders is the goal of many sensor systems in dairy farming. Although mastitis and lameness are the most common health disorders in dairy cows, these diseases or treatments are a rare event related to a single day and cow. A number of studies already developed and evaluated models for classifying cows in need of treatment for mastitis and lameness with machine learning methods, but few have illustrated the effects of the positive predictive value (PPV) on practical application. The objective of this study was to investigate the importance of low-frequency treatments of mastitis or lameness for the applicability of these classification models in practice. Data from three German dairy farms contained animal individual sensor data (milkings, activity, feed intake) and were classified using machine learning models developed in a previous study. Subsequently, different risk criteria (previous treatments, information from milk recording, early lactation) were designed to isolate high-risk groups. Restricting selection to cows with previous mastitis or hoof treatment achieved the highest increase in PPV from 0.07 to 0.20 and 0.15, respectively. However, the known low daily risk of a treatment per cow remains the critical factor that prevents the reduction of daily false-positive alarms to a satisfactory level. Sensor systems should be seen as additional decision-support aid to the farmers’ expert knowledge. Full article

(This article belongs to the Special Issue Sensors for Animal Health Monitoring and Precision Livestock Farming)

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18 pages, 3585 KiB

Open AccessArticle

Feasibility Study on the Use of Infrared Thermography to Classify Fattening Pigs into Feeding Groups According Their Body Composition

by Alexandra Lengling, Antonius Alfert, Bernd Reckels, Julia Steinhoff-Wagner and Wolfgang Büscher

Sensors 2020, 20(18), 5221; https://doi.org/10.3390/s20185221 - 13 Sep 2020

Cited by 3 | Viewed by 2491

Abstract

Fattening pig husbandry and associated negative environmental impacts due to nitrogen inputs by ammonia emissions are current issues of social discussion. New resource-efficient feeding systems offer great potential to reduce excess nutrient inputs into the environment. Using ultrasound measurements, fattening pigs can be divided into performance groups based on their backfat/muscle ratio to feed them according to their nutritional needs. Ultrasound measurements are not suitable for practical use, so alternatives have to be found. As a non-invasive, contactless method, infrared thermography offers many advantages. This study investigated whether infrared thermography can be used to differentiate between “fat” and “lean” animals. Two evaluation methods with different measurement spot sizes were compared. During a fattening period, 980 pigs were examined three times with an infrared camera. Both methods showed significant differences. Body surface temperature was influenced by factors like measurement spot size and soiling of the animals. Body surface temperature decreased (−5.5 °C), while backfat thickness increased (+0.7 cm) in the course of the fattening period. Significant correlations (R > |0.5|; p < 0.001) between both parameters were found. Differentiation between “fat” and “lean” animals, based on temperature data, was not possible. Nevertheless, the application of thermography should be investigated further with the aim of resource-efficient feeding. The results of this feasibility study can serve as a basis for this. Full article

(This article belongs to the Special Issue Sensors for Animal Health Monitoring and Precision Livestock Farming)

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11 pages, 913 KiB

Open AccessArticle

Dynamic Changes in Progesterone Concentration in Cows’ Milk Determined by the At-Line Milk Analysis System Herd Navigator^TM

by Ramūnas Antanaitis, Dovilė Malašauskienė, Mindaugas Televičius, Vida Juozaitienė, Henrikas Žilinskas and Walter Baumgartner

Sensors 2020, 20(18), 5020; https://doi.org/10.3390/s20185020 - 04 Sep 2020

Cited by 6 | Viewed by 3407

Abstract

The aim of the current instant study was to evaluate relative at-line milk progesterone dynamic changes according to parity and status of reproduction and to estimate the relationship with productivity in dairy cows by at-line milk analysis system Herd Navigator^TM. According to the progesterone assay, experimental animals were divided into three periods: postpartum, after insemination, and pregnancy. In the first stage of the postpartum period, progesterone levels in milk were monitored every 5 days. This period of reproductive cycle recovery was followed for 30 days (days 0–29). The second stage of the postpartum period (30–65 days) lasted until cows were inseminated. In the period (0–45 days) after cow insemination, progesterone levels were distributed according to whether or not cows became pregnant. For milk progesterone detection, the fully automated real-time progesterone analyzer Herd Navigator^TM (Lattec I/S, Hillerød, Denmark) was used in combination with a DeLaval milking robot (DeLaval Inc., Tumba, Sweden). We found that an at-line progesterone concentration is related to different parities, reproductive statuses, and milk yield of cows: the 12.88% higher concentration of progesterone in milk was evaluated in primiparous cows. The average milk yield in non-pregnant primiparous cows was 4.64% higher, and in non-pregnant multiparous cows 6.87% higher than in pregnant cows. Pregnancy success in cows can be predicted 11–15 days after insemination, when a significant increase in progesterone is observed in the group of pregnant cows. Full article

(This article belongs to the Special Issue Sensors for Animal Health Monitoring and Precision Livestock Farming)

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11 pages, 913 KiB

Open AccessArticle

Preliminary Experiment Using Sensors for Cow Health Monitoring after Surgical Treatment for the Left Displacement of the Abomasum

by Ramūnas Antanaitis, Vida Juozaitienė, Mindaugas Televičius, Dovilė Malašauskienė, Mantvydas Merkis, Eitvydas Merkis and Walter Baumgartner

Sensors 2020, 20(16), 4416; https://doi.org/10.3390/s20164416 - 07 Aug 2020

Cited by 7 | Viewed by 3270 | Correction

Abstract

The aim of the current study was to determine the effectiveness of two surgical techniques regarding cow respiratory rates, heart rates, and rumination time using two sensors: an experimental device created by the Institute of Biomedical Engineering of Kaunas University of Technology (Lithuania) and the Hi-Tag rumination monitoring system (SCR) produced by SCR Engineers Ltd., Netanya, Israel. The cows were divided into two groups: the PA1 group, containing cows treated by percutaneous abomasopexy (n = 10), and the RSO2 group, containing cows treated by right side omentopexy (n = 8). For the control group (KH), according to the principle of analogs (number of lactations, breed, and days in milk), we selected clinically healthy cows (n = 9). After the surgical treatment for the abomasal displacement, the experimental device was applied for the recording of the heart and breathing rates, 12 h tracking of the rumination time was implemented using the SCR, and the body temperature was measured. After 12 h, the blood was taken for biochemical and morphological tests. With the help of experimental sensors, we found that the more efficient abomasal displacement surgical method was the right side omentopexy: During the first 12 h after right side omentopexy, we found a 5.19 beats/min lower (1.10 times lower) average value of the respiratory rate, a 1.13 times higher level of the heart rate, a 0.15 °C higher temperature, and a 3.29 times lower rumination time compared to the clinically healthy cows. During the first 12 h after percutaneous abomasopexy, we found a 5.19 beats/min higher (1.07 times) average value of heart rate, a 0.02 °C higher temperature, a 6.21 times lower rumination time, and a 0.12 beats/min lower (1.01 times lower) average value of respiratory rate compared to the clinically healthy cows. Full article

(This article belongs to the Special Issue Sensors for Animal Health Monitoring and Precision Livestock Farming)

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Review

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23 pages, 4380 KiB

Open AccessReview

Factors Affecting Site Use Preference of Grazing Cattle Studied from 2000 to 2020 through GPS Tracking: A Review

by M. Jordana Rivero, Patricia Grau-Campanario, Siobhan Mullan, Suzanne D. E. Held, Jessica E. Stokes, Michael R. F. Lee and Laura M. Cardenas

Sensors 2021, 21(8), 2696; https://doi.org/10.3390/s21082696 - 11 Apr 2021

Cited by 30 | Viewed by 5297

Abstract

Understanding the behaviour of grazing animals at pasture is crucial in order to develop management strategies that will increase the potential productivity of grazing systems and simultaneously decrease the negative impact on the environment. The objective of this review was to summarize and analyse the scientific literature that has addressed the site use preference of grazing cattle using global positioning systems (GPS) collars in the past 21 years (2000–2020) to aid the development of more sustainable grazing livestock systems. The 84 studies identified were undertaken in several regions of the world, in diverse production systems, under different climate conditions and with varied methodologies and animal types. This work presents the information in categories according to the main findings reviewed, covering management, external and animal factors driving animal movement patterns. The results showed that some variables, such as stocking rate, water and shade location, weather conditions and pasture (terrain and vegetation) characteristics, have a significant impact on the behaviour of grazing cattle. Other types of bio-loggers can be deployed in grazing ruminants to gain insights into their metabolism and its relationship with the landscape they utilise. Changing management practices based on these findings could improve the use of grasslands towards more sustainable and productive livestock systems. Full article

(This article belongs to the Special Issue Sensors for Animal Health Monitoring and Precision Livestock Farming)

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Other

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6 pages, 682 KiB

Open AccessCase Report

Change of Ruminoreticular Temperature and Body Activity before and after Parturition in Hanwoo (Bos taurus coreanae) Cows

by Daehyun Kim, Jaejung Ha, Woo-Sung Kwon, Joonho Moon, Gyeong-Min Gim and Junkoo Yi

Sensors 2021, 21(23), 7892; https://doi.org/10.3390/s21237892 - 26 Nov 2021

Cited by 1 | Viewed by 1991

Abstract

How do body temperature and activity change before and after parturition in pregnant cows? Changes in body temperature such as ruminal, rectal, and vaginal temperature during the parturition have been reported, but there are no results of the simultaneous observation of body temperature and activity. The aim of this study was to simultaneously confirm changes in the ruminoreticular temperature and body activity before and after parturition using the ruminoreticular bio-capsule sensor every 1 h. The 55 pregnant cows were used for the experiment, the ruminoreticular bio-capsule sensor was inserted and stabilized, and the ruminoreticular temperature and body activity were measured. The ruminoreticular temperature was lower by 0.5° from −24 h to −3 h in parturition compared to 48 h before parturition and then recovered again after parturition. Body activity increased temporarily at the time of parturition and 12 h after parturition. Therefore, the ruminoreticular temperature and body activity before and after parturition was simultaneously confirmed in pregnant cows. Full article

(This article belongs to the Special Issue Sensors for Animal Health Monitoring and Precision Livestock Farming)

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1 pages, 155 KiB

Open AccessCorrection

Correction: Antanaitis, R., et al. Preliminary Experiment Using Sensors for Cow Health Monitoring after Surgical Treatment for the Left Displacement of the Abomasum. Sensors 2020, 20, 4416

by Ramūnas Antanaitis, Vida Juozaitienė, Mindaugas Televičius, Dovilė Malašauskienė, Mantvydas Merkis, Eitvydas Merkis and Walter Baumgartner

Sensors 2021, 21(3), 918; https://doi.org/10.3390/s21030918 - 29 Jan 2021

Viewed by 1544

Abstract

The authors wish to make the following corrections to this paper [...] Full article

(This article belongs to the Special Issue Sensors for Animal Health Monitoring and Precision Livestock Farming)

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