FLYOLOv3 deep learning for key parts of dairy cow body detection

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

doi:10.1016/j.compag.2019.104982

Cited by 83 publications

(33 citation statements)

References 21 publications

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“…This is also a problem in the identification of other breeds with uniform markings and colors or both, such as Aubrac, Grey, Angus, Limousine, or Brown Swiss cattle. Jiang et al [ 87 ] further classified cows’ heads, backs, and legs from images by training a FLYOLOv3 model; they achieved an accuracy of 99.18%, a recall rate of 97.51%, and an average precision of 93.73%. Particularly, the combination of animal identification with additional algorithms, such as the combination with BCS, offers novel ways of using physiological and behavioral traits for management decisions [ 40 ].…”

Section: Resultsmentioning

confidence: 99%

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%

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

Cockburn

2020

Animals

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“…Many studies exist that focus on using image processing techniques to extract better cow lameness features from videos [ 53 , 61 , 65 , 66 , 67 ]. This is the fundamental technique for automatic detection.…”

Section: Methodsmentioning

confidence: 99%

A Review: Development of Computer Vision-Based Lameness Detection for Dairy Cows and Discussion of the Practical Applications

Zhang

Liu

2021

Sensors

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The computer vision technique has been rapidly adopted in cow lameness detection research due to its noncontact characteristic and moderate price. This paper attempted to summarize the research progress of computer vision in the detection of lameness. Computer vision lameness detection systems are not popular on farms, and the accuracy and applicability still need to be improved. This paper discusses the problems and development prospects of this technique from three aspects: detection methods, verification methods and application implementation. The paper aims to provide the reader with a summary of the literature and the latest advances in the field of computer vision detection of lameness in dairy cows.

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“…In reducing the network input, the images are uniformly converted to 1280 × 720 pixels. Moreover, Gaussian noise and salt-and-pepper noise (as shown in Figure 3) with a variance of 0.001 are added to the original images to expand the dataset and increase the robustness of the network model [31]. The final dataset includes 6004 frames of original images, enhanced images, Gaussian noise images, and salt-and-pepper noise images.…”

Section: Dataset Productionmentioning

confidence: 99%

Automatic Fish Population Counting by Machine Vision and a Hybrid Deep Neural Network Model

Zhang

Yang

Wang

et al. 2020

Animals

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In intensive aquaculture, the number of fish in a shoal can provide valuable input for the development of intelligent production management systems. However, the traditional artificial sampling method is not only time consuming and laborious, but also may put pressure on the fish. To solve the above problems, this paper proposes an automatic fish counting method based on a hybrid neural network model to realize the real-time, accurate, objective, and lossless counting of fish population in far offshore salmon mariculture. A multi-column convolution neural network (MCNN) is used as the front end to capture the feature information of different receptive fields. Convolution kernels of different sizes are used to adapt to the changes in angle, shape, and size caused by the motion of fish. Simultaneously, a wider and deeper dilated convolution neural network (DCNN) is used as the back end to reduce the loss of spatial structure information during network transmission. Finally, a hybrid neural network model is constructed. The experimental results show that the counting accuracy of the proposed hybrid neural network model is up to 95.06%, and the Pearson correlation coefficient between the estimation and the ground truth is 0.99. Compared with CNN- and MCNN-based methods, the accuracy and other evaluation indices are also improved. Therefore, the proposed method can provide an essential reference for feeding and other breeding operations.

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FLYOLOv3 deep learning for key parts of dairy cow body detection

Cited by 83 publications

References 21 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

A Review: Development of Computer Vision-Based Lameness Detection for Dairy Cows and Discussion of the Practical Applications

Automatic Fish Population Counting by Machine Vision and a Hybrid Deep Neural Network Model

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