Lameness detection of dairy cows based on the YOLOv3 deep learning algorithm and a relative step size characteristic vector

Wu, Dihua; Wu, Qian; Yin, Xuqiang; Jiang, Bo; Wang, Han; He, Dongjian; Song, Huaibo

doi:10.1016/j.biosystemseng.2019.11.017

Cited by 114 publications

(52 citation statements)

References 35 publications

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“…Most studies do not report on the efficiency of lameness detection, but rather give indications on behavioral changes, which makes it difficult to implement the findings in early warning systems [ 44 ], yet combining behavioral and gait parameters would offer the best approach for early lameness detection. Another approach is to fit deep learning algorithms, which detect lame dairy cows from video material [ 45 ]. This approach offers a low-cost and contactless alternative to sensors that need to be fixed to the animal [ 45 ].…”

Section: Resultsmentioning

confidence: 99%

“…Another approach is to fit deep learning algorithms, which detect lame dairy cows from video material [ 45 ]. This approach offers a low-cost and contactless alternative to sensors that need to be fixed to the animal [ 45 ]. The authors achieved a detection accuracy of 98.7% and a false positive rate of only 0.03.…”

Section: Resultsmentioning

confidence: 99%

“…The authors achieved a detection accuracy of 98.7% and a false positive rate of only 0.03. However, they only categorized “lame” and “not lame” cows in their dataset without specifying the score of lameness; the latter information is important for data interpretation, and as such, early detection [ 45 ]. Hudson et al [ 46 ] looked at lameness from a different angle and used a dataset from 12,515 dairy cows in 39 herds to evaluate the effect of clinical lameness on reproductive success.…”

Section: Resultsmentioning

confidence: 99%

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Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms

Cockburn

2020

Animals

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Dairy farmers use herd management systems, behavioral sensors, feeding lists, breeding schedules, and health records to document herd characteristics. Consequently, large amounts of dairy data are becoming available. However, a lack of data integration makes it difficult for farmers to analyze the data on their dairy farm, which indicates that these data are currently not being used to their full potential. Hence, multiple issues in dairy farming such as low longevity, poor performance, and health issues remain. We aimed to evaluate whether machine learning (ML) methods can solve some of these existing issues in dairy farming. This review summarizes peer-reviewed ML papers published in the dairy sector between 2015 and 2020. Ultimately, 97 papers from the subdomains of management, physiology, reproduction, behavior analysis, and feeding were considered in this review. The results confirm that ML algorithms have become common tools in most areas of dairy research, particularly to predict data. Despite the quantity of research available, most tested algorithms have not performed sufficiently for a reliable implementation in practice. This may be due to poor training data. The availability of data resources from multiple farms covering longer periods would be useful to improve prediction accuracies. In conclusion, ML is a promising tool in dairy research, which could be used to develop and improve decision support for farmers. As the cow is a multifactorial system, ML algorithms could analyze integrated data sources that describe and ultimately allow managing cows according to all relevant influencing factors. However, both the integration of multiple data sources and the obtainability of public data currently remain challenging.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms

Cockburn

2020

Animals

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“…In this paper, pedestrian detection is carried out under the you only look once (YOLO) v3 framework [19]. The aspect ratio was acquired from network output [x0, y0, x1, y1] by:…”

Section: Vector Graph Of Human Walking Featuresmentioning

confidence: 99%

Recognition and Difference Analysis of Human Walking Gaits Based on Intelligent Processing of Video Images

Wang¹,

Yang²,

Li³

2020

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Based on the residual network and long short-term memory (LSTM) network, this paper proposes a human walking gait recognition method, which relies on the vector image of human walking features and the dynamic lower limb model with multiple degrees-of-freedom (DOFs). Firstly, a human pose estimation algorithm was designed based on deep convolutional neural network (DCNN), and used to obtain the vector image of human walking features. Then, the movements of human lower limbs were described by a simplified model, and the dynamic eigenvectors of the simplified model were obtained by Lagrange method, revealing the mapping relationship between eigenvectors in gait fitting. To analyze the difference of human walking gaits more accurately, a feature learning and recognition algorithm was developed based on residual network, and proved accurate and robust through experiments on the data collected from a public gait database.

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“…Image processing using CNNs has been used in various aspects of agriculture since 2015 [20]. Innovative uses of this technology in agriculture have included livestock monitoring [21]- [23], plant disease detection [24]- [26], wild blueberry ripeness detection [27], and weed detection for strawberries [28], Florida vegetables [29], turfgrasses [30], [31], and ryegrass [32]. Reference [33] was the first to use CNNs for detecting weeds in wild blueberry fields.…”

Section: Introductionmentioning

confidence: 99%

Viability of using Convolutional Neural Networks for Real-Time Fescue and Sheep Sorrel Detection in Wild Blueberry Fields

Hennessy¹,

Esau²,

Zaman³

et al. 2020

Progress in Canadian Mechanical Engineering. Volume 3

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Wild blueberry (Vaccinium angustifolium Ait.) production is hindered by weeds such as fescue (Festuca filiformis Pourr.) and sheep sorrel (Rumex acetosella L.). Commercial sprayers provide a uniform application of herbicide regardless of the incidence of weed coverage. Traditional methods of spot-applying herbicide to target weed locations relied on colour co-occurrence matrices that were limited by long processing times in addition to green colour segmentation lacking the ability to discriminate between weeds and crop canopy of similar colour. Deep learning convolutional neural networks (CNNs) are a modern processing technique which often uses powerful graphics processing units (GPUs) to classify images or detect objects within images. This novel research study featured two object-detection CNNs, YOLOv3 and YOLOv3-Tiny, trained to detect fescue using images of wild blueberry fields captured during application timing intervals in the 2019 growing season. A custom-built desktop computer using the Ubuntu 16.04 operating system and an NVIDIA GeForce RTX 2080 Ti GPU was used to test the CNNs using 1280x720 resolution images. YOLOv3 classified fescue images with an F1-score of 0.95 and YOLOv3-Tiny classified fescue images with an F1-score of 0.97. YOLOv3 and YOLOv3-Tiny classified sheep sorrel images with F1scores of 0.93 and 0.89 respectively. A laptop running Windows 10 Pro with an NVIDIA Quadro RTX 5000 GPU was used to process video streams from four USB cameras simultaneously. YOLOv3 processed each video stream at an average framerate of 4.7 FPS using 9.2 GB of vRAM, while YOLOv3-Tiny processed each video stream at an average framerate of 20.5 FPS using 3.2 GB of vRAM. Initial results suggest that YOLOv3-Tiny can be deployed for use with a machine vision system to detect fescue and sheep sorrel in realtime for spot application of herbicide in wild blueberry fields. Using CNNs to selectively spray herbicide will appreciably reduce the volume of herbicides needed to manage wild blueberry fields, resulting in cost-savings for producers.

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Lameness detection of dairy cows based on the YOLOv3 deep learning algorithm and a relative step size characteristic vector

Cited by 114 publications

References 35 publications

Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms

Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms

Recognition and Difference Analysis of Human Walking Gaits Based on Intelligent Processing of Video Images

Viability of using Convolutional Neural Networks for Real-Time Fescue and Sheep Sorrel Detection in Wild Blueberry Fields

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