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Sharifuzzaman M, Mun HS, Ampode KMB, Lagua EB, Park HR, Kim YH, Hasan MK, Yang CJ. Technological Tools and Artificial Intelligence in Estrus Detection of Sows-A Comprehensive Review. Animals (Basel) 2024; 14:471. [PMID: 38338113 PMCID: PMC10854728 DOI: 10.3390/ani14030471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 01/30/2024] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
In animal farming, timely estrus detection and prediction of the best moment for insemination is crucial. Traditional sow estrus detection depends on the expertise of a farm attendant which can be inconsistent, time-consuming, and labor-intensive. Attempts and trials in developing and implementing technological tools to detect estrus have been explored by researchers. The objective of this review is to assess the automatic methods of estrus recognition in operation for sows and point out their strong and weak points to assist in developing new and improved detection systems. Real-time methods using body and vulvar temperature, posture recognition, and activity measurements show higher precision. Incorporating artificial intelligence with multiple estrus-related parameters is expected to enhance accuracy. Further development of new systems relies mostly upon the improved algorithm and accurate data provided. Future systems should be designed to minimize the misclassification rate, so better detection is achieved.
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Affiliation(s)
- Md Sharifuzzaman
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (M.S.); (H.-S.M.); (K.M.B.A.); (E.B.L.); (H.-R.P.); (M.K.H.)
- Department of Animal Science and Veterinary Medicine, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh
| | - Hong-Seok Mun
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (M.S.); (H.-S.M.); (K.M.B.A.); (E.B.L.); (H.-R.P.); (M.K.H.)
- Department of Multimedia Engineering, Sunchon National University, Suncheon 57922, Republic of Korea
| | - Keiven Mark B. Ampode
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (M.S.); (H.-S.M.); (K.M.B.A.); (E.B.L.); (H.-R.P.); (M.K.H.)
- Department of Animal Science, College of Agriculture, Sultan Kudarat State University, Tacurong 9800, Philippines
| | - Eddiemar B. Lagua
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (M.S.); (H.-S.M.); (K.M.B.A.); (E.B.L.); (H.-R.P.); (M.K.H.)
- Interdisciplinary Program in IT-Bio Convergence System (BK21 Plus), Sunchon National University, Suncheon 57922, Republic of Korea
| | - Hae-Rang Park
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (M.S.); (H.-S.M.); (K.M.B.A.); (E.B.L.); (H.-R.P.); (M.K.H.)
- Interdisciplinary Program in IT-Bio Convergence System (BK21 Plus), Sunchon National University, Suncheon 57922, Republic of Korea
| | - Young-Hwa Kim
- Interdisciplinary Program in IT-Bio Convergence System (BK21 Plus), Chonnam National University, Gwangju 61186, Republic of Korea;
| | - Md Kamrul Hasan
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (M.S.); (H.-S.M.); (K.M.B.A.); (E.B.L.); (H.-R.P.); (M.K.H.)
- Department of Poultry Science, Sylhet Agricultural University, Sylhet 3100, Bangladesh
| | - Chul-Ju Yang
- Animal Nutrition and Feed Science Laboratory, Department of Animal Science and Technology, Sunchon National University, Suncheon 57922, Republic of Korea; (M.S.); (H.-S.M.); (K.M.B.A.); (E.B.L.); (H.-R.P.); (M.K.H.)
- Interdisciplinary Program in IT-Bio Convergence System (BK21 Plus), Sunchon National University, Suncheon 57922, Republic of Korea
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Olğaç KT, Yazlik MO, Özkan H, Kaya U, Tirpan MB. Determination of expression patterns of miR-26a, and preimplantation factor levels for early pregnancy detection in nulliparous and multiparous cows. Reprod Domest Anim 2024; 59:e14521. [PMID: 38268207 DOI: 10.1111/rda.14521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 12/08/2023] [Accepted: 12/11/2023] [Indexed: 01/26/2024]
Abstract
For maximum productivity in a dairy farm, the earliest and the most accurate detection of pregnancy is essential. The aim of this study was to determine the efficacy of expression patterns of miR-26a, and serum Preimplantation Factor (PIF) levels for pregnancy diagnosis during the early pregnancy in nulliparous and multiparous cows. A total of 60 cows (30 nulliparous and 30 multiparous Holstein cows) were enrolled in the study. Blood samples were collected for miR-26a on days 8 and 16 (D8 and D16), and for the PIF on days 10 and 20 (D10 and D20) following insemination (D0). Pregnancies were determined by ultrasonography on the 28th day after insemination. Expression levels of miR-26a determined by qPCR. PIF levels were assessed by using commercial ELISA kits. All data were analyzed by using the MIXED procedure of SPSS. The expression levels of miR-26a were 6.64 folds higher on D16 in pregnant compared to non-pregnant multiparous cows (p < .05). On D8 and D16, miR-26a expression levels were found higher 13 folds in pregnant compared to non-pregnant nulliparous cows (p < .05). Additionally, miR-26a expressions were higher 5.42 folds (p < .05) on D8, 7.19 folds higher (p < .01) on D16 in pregnant nulliparous and multiparous cows, and were 6.30 folds higher (p < .001) on D8 and D16 according to non-pregnant animals. PIF levels were greater in pregnant animals (p < .05). Analyzing miR-26a on D8 might be considered as sufficient in nulliparous cows. Pregnancy detection in multiparous cows can be made on the 16th day with this method. Furthermore, PIF evaluations may be sufficient on D10 in multiparous cows. Besides, PIF levels and miR-26a expression levels might be used safely in field conditions and clinical applications.
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Affiliation(s)
- Kemal Tuna Olğaç
- Department of Reproduction and Artificial Insemination, Faculty of Veterinary Medicine, Ankara University, Ankara, Turkey
| | - Murat Onur Yazlik
- Department of Obstetrics and Gyneacology, Faculty of Veterinary Medicine, Ankara University, Ankara, Turkey
| | - Hüseyin Özkan
- Department of Genetics, Faculty of Veterinary Medicine, Hatay Mustafa Kemal University, Antakya, Hatay, Turkey
| | - Ufuk Kaya
- Department of Biostatistics, Faculty of Veterinary Medicine, Hatay Mustafa Kemal University, Antakya, Hatay, Turkey
| | - Mehmet Borga Tirpan
- Department of Reproduction and Artificial Insemination, Faculty of Veterinary Medicine, Ankara University, Ankara, Turkey
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3
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Arıkan İ, Ayav T, Seçkin AÇ, Soygazi F. Estrus Detection and Dairy Cow Identification with Cascade Deep Learning for Augmented Reality-Ready Livestock Farming. SENSORS (BASEL, SWITZERLAND) 2023; 23:9795. [PMID: 38139641 PMCID: PMC10747260 DOI: 10.3390/s23249795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/09/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023]
Abstract
Accurate prediction of the estrus period is crucial for optimizing insemination efficiency and reducing costs in animal husbandry, a vital sector for global food production. Precise estrus period determination is essential to avoid economic losses, such as milk production reductions, delayed calf births, and disqualification from government support. The proposed method integrates estrus period detection with cow identification using augmented reality (AR). It initiates deep learning-based mounting detection, followed by identifying the mounting region of interest (ROI) using YOLOv5. The ROI is then cropped with padding, and cow ID detection is executed using YOLOv5 on the cropped ROI. The system subsequently records the identified cow IDs. The proposed system accurately detects mounting behavior with 99% accuracy, identifies the ROI where mounting occurs with 98% accuracy, and detects the mounting couple with 94% accuracy. The high success of all operations with the proposed system demonstrates its potential contribution to AR and artificial intelligence applications in livestock farming.
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Affiliation(s)
- İbrahim Arıkan
- Computer Engineering Department, İzmir Institute of Technology, Izmir 35430, Türkiye; (İ.A.); (T.A.)
| | - Tolga Ayav
- Computer Engineering Department, İzmir Institute of Technology, Izmir 35430, Türkiye; (İ.A.); (T.A.)
| | - Ahmet Çağdaş Seçkin
- Computer Engineering Department, Aydın Adnan Menderes University, Aydın 09100, Türkiye;
| | - Fatih Soygazi
- Computer Engineering Department, Aydın Adnan Menderes University, Aydın 09100, Türkiye;
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4
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Bretas IL, Dubeux JCB, Cruz PJR, Queiroz LMD, Ruiz-Moreno M, Knight C, Flynn S, Ingram S, Pereira Neto JD, Oduor KT, Loures DRS, Novo SF, Trumpp KR, Acuña JP, Bernardini MA. Monitoring the Effect of Weed Encroachment on Cattle Behavior in Grazing Systems Using GPS Tracking Collars. Animals (Basel) 2023; 13:3353. [PMID: 37958108 PMCID: PMC10649354 DOI: 10.3390/ani13213353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 10/23/2023] [Accepted: 10/26/2023] [Indexed: 11/15/2023] Open
Abstract
Weed encroachment on grasslands can negatively affect herbage allowance and animal behavior, impacting livestock production. We used low-cost GPS collars fitted to twenty-four Angus crossbred steers to evaluate the effects of different levels of weed encroachment on animal activities and spatial distribution. The experiment was established with a randomized complete block design, with three treatments and four blocks. The treatments were paddocks free of weeds (weed-free), paddocks with weeds established in alternated strips (weed-strips), and paddocks with weeds spread throughout the entire area (weed-infested). Animals in weed-infested paddocks had reduced resting time and increased grazing time, distance traveled, and rate of travel (p < 0.05) compared to animals in weed-free paddocks. The spatial distribution of the animals was consistently greater in weed-free paddocks than in weed-strips or weed-infested areas. The effects of weed encroachment on animal activities were minimized after weed senescence at the end of the growing season. Pasture weed encroachment affected cattle behavior and their spatial distribution across the pasture, potentially impacting animal welfare. Further long-term studies are encouraged to evaluate the impacts of weed encroachment on animal performance and to quantify the effects of behavioral changes on animal energy balance.
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Affiliation(s)
- Igor L. Bretas
- North Florida Research and Education Center, University of Florida, Marianna, FL 32446, USA (L.M.D.Q.); (M.R.-M.); (K.T.O.); (S.F.N.); (K.R.T.); (J.P.A.); (M.A.B.)
| | - Jose C. B. Dubeux
- North Florida Research and Education Center, University of Florida, Marianna, FL 32446, USA (L.M.D.Q.); (M.R.-M.); (K.T.O.); (S.F.N.); (K.R.T.); (J.P.A.); (M.A.B.)
| | - Priscila J. R. Cruz
- Range Cattle Research and Education Center, University of Florida, Ona, FL 33865, USA;
| | - Luana M. D. Queiroz
- North Florida Research and Education Center, University of Florida, Marianna, FL 32446, USA (L.M.D.Q.); (M.R.-M.); (K.T.O.); (S.F.N.); (K.R.T.); (J.P.A.); (M.A.B.)
| | - Martin Ruiz-Moreno
- North Florida Research and Education Center, University of Florida, Marianna, FL 32446, USA (L.M.D.Q.); (M.R.-M.); (K.T.O.); (S.F.N.); (K.R.T.); (J.P.A.); (M.A.B.)
| | - Colt Knight
- University of Maine Cooperative Extension, Orono, ME 04469, USA;
| | - Scott Flynn
- Corteva Agriscience, Lee’s Summit, MO 64015, USA; (S.F.); (S.I.)
| | - Sam Ingram
- Corteva Agriscience, Lee’s Summit, MO 64015, USA; (S.F.); (S.I.)
| | | | - Kenneth T. Oduor
- North Florida Research and Education Center, University of Florida, Marianna, FL 32446, USA (L.M.D.Q.); (M.R.-M.); (K.T.O.); (S.F.N.); (K.R.T.); (J.P.A.); (M.A.B.)
| | - Daniele R. S. Loures
- Departament of Animal Science, Universidade Federal do Recôncavo da Bahia, Cruz das Almas 44430-622, BA, Brazil;
| | - Sabina F. Novo
- North Florida Research and Education Center, University of Florida, Marianna, FL 32446, USA (L.M.D.Q.); (M.R.-M.); (K.T.O.); (S.F.N.); (K.R.T.); (J.P.A.); (M.A.B.)
| | - Kevin R. Trumpp
- North Florida Research and Education Center, University of Florida, Marianna, FL 32446, USA (L.M.D.Q.); (M.R.-M.); (K.T.O.); (S.F.N.); (K.R.T.); (J.P.A.); (M.A.B.)
| | - Javier P. Acuña
- North Florida Research and Education Center, University of Florida, Marianna, FL 32446, USA (L.M.D.Q.); (M.R.-M.); (K.T.O.); (S.F.N.); (K.R.T.); (J.P.A.); (M.A.B.)
| | - Marilia A. Bernardini
- North Florida Research and Education Center, University of Florida, Marianna, FL 32446, USA (L.M.D.Q.); (M.R.-M.); (K.T.O.); (S.F.N.); (K.R.T.); (J.P.A.); (M.A.B.)
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Wang J, Chen H, Wang J, Zhao K, Li X, Liu B, Zhou Y. Identification of oestrus cows based on vocalisation characteristics and machine learning technique using a dual-channel-equipped acoustic tag. Animal 2023; 17:100811. [PMID: 37150135 DOI: 10.1016/j.animal.2023.100811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 03/31/2023] [Accepted: 04/03/2023] [Indexed: 05/09/2023] Open
Abstract
Timely and accurate detection of oestrus in cows is an essential element of the good management of dairy farms. At present, the detection of cows in oestrus by acoustic means is impeded by the problems of filtering, incomplete feature selection, and poor recognition accuracy. To overcome these difficulties, this study proposes a sound detection method for cows in oestrus based on machine learning technology using an optimal feature combination and an optimal time window. Firstly, a dual-channel sound detection tag consisting of a unidirectional microphone and an omnidirectional microphone (OM) was developed. The Least Mean Squares adaptive algorithm based on wavelet thresholds was used to filter the signals from the OM, and the dual-channel endpoint detection algorithm was used to identify the lowing of individual cows. The Friedman analysis was then used to select the sound features with significant differences before and after oestrus in terms of time, frequency, and cepstrum, and these were used to determine the most acceptable feature combination. We then analysed the effects of Back Propagation Neural Network (BPNN), Cartesian Regression Tree, Support Vector Machine, and Random Forest classification on the accuracy, precision, sensitivity, specificity, and F1 score of oestrus discrimination. Different time windows were used, and the discrimination performance of these algorithms was evaluated using the Area under Receiver Operating Characteristic Curve to find the most satisfactory match between the time window and the recognition algorithm. The dual-channel acoustic tag's accuracy, precision, sensitivity, and specificity results were 91.25, 98.83, 91.75, and 83.68%, respectively. BPNN with the 70 ms time window and the feature combination (spectral roll-off + spectral flatness + Mel-Frequency Cepstrum Coefficients) was confirmed as the most suitable oestrus recognition method. The average accuracy, precision, sensitivity, specificity, and F1 score of this method were 97.62, 98.07, 97.17, 97.19, and 97.63%, respectively. Based on these results, the approach was shown to be a feasible means of oestrus detection in dairy cows. Based on its ability to differentiate cows and its consistency, it was demonstrated that sound has the potential to replace accelerometers as an early indicator of oestrus in dairy cows.
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Affiliation(s)
- Jun Wang
- School of Information Engineering, Henan University of Science and Technology, Luoyang, Henan 471003, PR China.
| | - Haoran Chen
- School of Information Engineering, Henan University of Science and Technology, Luoyang, Henan 471003, PR China
| | - Jianping Wang
- School of Animal Science and Technology, Henan University of Science and Technology, Luoyang, Henan 471003, PR China
| | - Kaixuan Zhao
- School of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, Henan 471003, PR China
| | - Xiaoxia Li
- School of Animal Science and Technology, Henan University of Science and Technology, Luoyang, Henan 471003, PR China
| | - Bo Liu
- School of Information Engineering, Henan University of Science and Technology, Luoyang, Henan 471003, PR China
| | - Yu Zhou
- School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, Henan 471003, PR China
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Bloch V, Frondelius L, Arcidiacono C, Mancino M, Pastell M. Development and Analysis of a CNN- and Transfer-Learning-Based Classification Model for Automated Dairy Cow Feeding Behavior Recognition from Accelerometer Data. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23052611. [PMID: 36904813 PMCID: PMC10006954 DOI: 10.3390/s23052611] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 06/12/2023]
Abstract
Due to technological developments, wearable sensors for monitoring the behavior of farm animals have become cheaper, have a longer lifespan and are more accessible for small farms and researchers. In addition, advancements in deep machine learning methods provide new opportunities for behavior recognition. However, the combination of the new electronics and algorithms are rarely used in PLF, and their possibilities and limitations are not well-studied. In this study, a CNN-based model for the feeding behavior classification of dairy cows was trained, and the training process was analyzed considering a training dataset and the use of transfer learning. Commercial acceleration measuring tags, which were connected by BLE, were fitted to cow collars in a research barn. Based on a dataset including 33.7 cow × days (21 cows recorded during 1-3 days) of labeled data and an additional free-access dataset with similar acceleration data, a classifier with F1 = 93.9% was developed. The optimal classification window size was 90 s. In addition, the influence of the training dataset size on the classifier accuracy was analyzed for different neural networks using the transfer learning technique. While the size of the training dataset was being increased, the rate of the accuracy improvement decreased. Beginning from a specific point, the use of additional training data can be impractical. A relatively high accuracy was achieved with few training data when the classifier was trained using randomly initialized model weights, and a higher accuracy was achieved when transfer learning was used. These findings can be used for the estimation of the necessary dataset size for training neural network classifiers intended for other environments and conditions.
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Affiliation(s)
- Victor Bloch
- Natural Resources Institute Luke (Finland), Latokartanonkaari 9, 00790 Helsinki, Finland
| | - Lilli Frondelius
- Natural Resources Institute Luke (Finland), Latokartanonkaari 9, 00790 Helsinki, Finland
| | - Claudia Arcidiacono
- Department of Agriculture, Food and Environment (Di3A), Building and Land Engineering Section, University of Catania, Via Santa Sofia 100, 95123 Catania, Italy
| | - Massimo Mancino
- Department of Agriculture, Food and Environment (Di3A), Building and Land Engineering Section, University of Catania, Via Santa Sofia 100, 95123 Catania, Italy
| | - Matti Pastell
- Natural Resources Institute Luke (Finland), Latokartanonkaari 9, 00790 Helsinki, Finland
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Mapping Welfare: Location Determining Techniques and Their Potential for Managing Cattle Welfare—A Review. DAIRY 2022. [DOI: 10.3390/dairy3040053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023] Open
Abstract
Several studies have suggested that precision livestock farming (PLF) is a useful tool for animal welfare management and assessment. Location, posture and movement of an individual are key elements in identifying the animal and recording its behaviour. Currently, multiple technologies are available for automated monitoring of the location of individual animals, ranging from Global Navigation Satellite Systems (GNSS) to ultra-wideband (UWB), RFID, wireless sensor networks (WSN) and even computer vision. These techniques and developments all yield potential to manage and assess animal welfare, but also have their constraints, such as range and accuracy. Combining sensors such as accelerometers with any location determining technique into a sensor fusion system can give more detailed information on the individual cow, achieving an even more reliable and accurate indication of animal welfare. We conclude that location systems are a promising approach to determining animal welfare, especially when applied in conjunction with additional sensors, but additional research focused on the use of technology in animal welfare monitoring is needed.
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Shine P, Murphy MD. Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study. SENSORS (BASEL, SWITZERLAND) 2021; 22:52. [PMID: 35009593 PMCID: PMC8747441 DOI: 10.3390/s22010052] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 12/17/2021] [Accepted: 12/19/2021] [Indexed: 05/06/2023]
Abstract
Machine learning applications are becoming more ubiquitous in dairy farming decision support applications in areas such as feeding, animal husbandry, healthcare, animal behavior, milking and resource management. Thus, the objective of this mapping study was to collate and assess studies published in journals and conference proceedings between 1999 and 2021, which applied machine learning algorithms to dairy farming-related problems to identify trends in the geographical origins of data, as well as the algorithms, features and evaluation metrics and methods used. This mapping study was carried out in line with PRISMA guidelines, with six pre-defined research questions (RQ) and a broad and unbiased search strategy that explored five databases. In total, 129 publications passed the pre-defined selection criteria, from which relevant data required to answer each RQ were extracted and analyzed. This study found that Europe (43% of studies) produced the largest number of publications (RQ1), while the largest number of articles were published in the Computers and Electronics in Agriculture journal (21%) (RQ2). The largest number of studies addressed problems related to the physiology and health of dairy cows (32%) (RQ3), while the most frequently employed feature data were derived from sensors (48%) (RQ4). The largest number of studies employed tree-based algorithms (54%) (RQ5), while RMSE (56%) (regression) and accuracy (77%) (classification) were the most frequently employed metrics used, and hold-out cross-validation (39%) was the most frequently employed evaluation method (RQ6). Since 2018, there has been more than a sevenfold increase in the number of studies that focused on the physiology and health of dairy cows, compared to almost a threefold increase in the overall number of publications, suggesting an increased focus on this subdomain. In addition, a fivefold increase in the number of publications that employed neural network algorithms was identified since 2018, in comparison to a threefold increase in the use of both tree-based algorithms and statistical regression algorithms, suggesting an increasing utilization of neural network-based algorithms.
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Affiliation(s)
| | - Michael D. Murphy
- Department of Process, Energy and Transport Engineering, Munster Technological University, T12 P928 Cork, Ireland;
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9
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Zhang M, Zhang K, Yu D, Xie Q, Liu B, Chen D, Xv D, Li Z, Liu C. Computerized assisted evaluation system for canine cardiomegaly via key points detection with deep learning. Prev Vet Med 2021; 193:105399. [PMID: 34118647 DOI: 10.1016/j.prevetmed.2021.105399] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 05/21/2021] [Accepted: 06/01/2021] [Indexed: 10/21/2022]
Abstract
Cardiomegaly is the main imaging finding for canine heart diseases. There are many advances in the field of medical diagnosing based on imaging with deep learning for human being. However there are also increasing realization of the potential of using deep learning in veterinary medicine. We reported a clinically applicable assisted platform for diagnosing the canine cardiomegaly with deep learning. VHS (vertebral heart score) is a measuring method used for the heart size of a dog. The concrete value of VHS is calculated with the relative position of 16 key points detected by the system, and this result is then combined with VHS reference range of all dog breeds to assist in the evaluation of the canine cardiomegaly. We adopted HRNet (high resolution network) to detect 16 key points (12 and four key points located on vertebra and heart respectively) in 2274 lateral X-ray images (training and validation datasets) of dogs, the model was then used to detect the key points in external testing dataset (396 images), the AP (average performance) for key point detection reach 86.4 %. Then we applied an additional post processing procedure to correct the output of HRNets so that the AP reaches 90.9 %. This result signifies that this system can effectively assist the evaluation of canine cardiomegaly in a real clinical scenario.
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Affiliation(s)
- Mengni Zhang
- New Ruipeng Pet Healthcare Group Co. LTD., Beijing, 100010, China
| | - Kai Zhang
- New Ruipeng Pet Healthcare Group Co. LTD., Beijing, 100010, China.
| | - Deying Yu
- Hospital University Sains Malaysia, Kota Bharu, 16150, Kelantan, Malaysia
| | - Qianru Xie
- New Ruipeng Pet Healthcare Group Co. LTD., Beijing, 100010, China
| | - Binlong Liu
- New Ruipeng Pet Healthcare Group Co. LTD., Beijing, 100010, China
| | - Dacan Chen
- New Ruipeng Pet Healthcare Group Co. LTD., Beijing, 100010, China
| | - Dongxing Xv
- New Ruipeng Pet Healthcare Group Co. LTD., Beijing, 100010, China
| | - Zhiwei Li
- New Ruipeng Pet Healthcare Group Co. LTD., Beijing, 100010, China
| | - Chaofei Liu
- New Ruipeng Pet Healthcare Group Co. LTD., Beijing, 100010, China
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10
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Benos L, Tagarakis AC, Dolias G, Berruto R, Kateris D, Bochtis D. Machine Learning in Agriculture: A Comprehensive Updated Review. SENSORS (BASEL, SWITZERLAND) 2021; 21:3758. [PMID: 34071553 PMCID: PMC8198852 DOI: 10.3390/s21113758] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 05/21/2021] [Accepted: 05/24/2021] [Indexed: 01/05/2023]
Abstract
The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords' combinations of "machine learning" along with "crop management", "water management", "soil management", and "livestock management", and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018-2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.
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Affiliation(s)
- Lefteris Benos
- Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (L.B.); (A.C.T.); (G.D.); (D.K.)
| | - Aristotelis C. Tagarakis
- Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (L.B.); (A.C.T.); (G.D.); (D.K.)
| | - Georgios Dolias
- Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (L.B.); (A.C.T.); (G.D.); (D.K.)
| | - Remigio Berruto
- Department of Agriculture, Forestry and Food Science (DISAFA), University of Turin, Largo Braccini 2, 10095 Grugliasco, Italy;
| | - Dimitrios Kateris
- Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (L.B.); (A.C.T.); (G.D.); (D.K.)
| | - Dionysis Bochtis
- Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, Greece; (L.B.); (A.C.T.); (G.D.); (D.K.)
- FarmB Digital Agriculture P.C., Doiranis 17, GR 54639 Thessaloniki, Greece
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