Monitoring the respiratory behavior of multiple cows based on computer vision and deep learning

Wu, Dihua; Han, Mengxuan; Song, Huaibo; Song, Lei; Duan, Yuanchao

doi:10.3168/jds.2022-22501

Cited by 23 publications

(10 citation statements)

References 43 publications

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“…Among the research on such technologies, the study of D. Wu et al (2023), considers automated respiratory monitoring of dairy cows and claims that it helps to increase the degree of automation of their health assessment and also reduces manual labour. The reasoning states that cows are often crowded on farms, and this creates difficulties for conventional health monitoring due to the physical limitation of free view and access to the cows.…”

Section: Discussionmentioning

confidence: 99%

Innovation enterprise development strategy in animal husbandry

Mukhamedkhanova,

Seidakhmetov,

Tulemetova

et al. 2024

Scientific Horizons

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The research relevance is determined by the need to create a strategy for the development of innovative entrepreneurial structures in livestock farming, the application of which can significantly increase the economic return on farms. The study aims to develop a strategy for the development of progressive livestock farms. The following general scientific methods were used for the study: analysis, synthesis, deduction, induction, and generalisation. Among the innovations considered were various genetic studies to improve the animal genome (the amount of meat and milk, the efficiency of feed assimilation), as well as the selection of genetically healthy embryos for breeding; the use of modern technologies in livestock farming, such as Radio Frequency Identification, Augmented Reality, Convolutional Neural Networks and Global Positioning System; technologies aimed at automating animal feeding processes. Particular attention was paid to various livestock monitoring systems created using advanced algorithms. A strategy for the development of innovative livestock farms was also presented, with drones, CCTV cameras, mixed reality glasses and scanner gloves as the main elements. Other necessary elements included a drone control module and software. Relevant calculations to prove the feasibility of implementing this strategy and its financial potential were also carried out. Provided that 82 units of various equipment are used, with a total cost of $187,970, the strategy provides for a profit of $1,072,030, with a 3-year implementation period and a payback period of less than 6 months. The practical significance of the information obtained is that it can be considered by researchers to further study the specifics of implementing innovative projects in livestock production, as well as to further develop various strategies for agricultural modernisation in Kazakhstan

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Section: Discussionmentioning

confidence: 99%

Innovation enterprise development strategy in animal husbandry

Mukhamedkhanova,

Seidakhmetov,

Tulemetova

et al. 2024

Scientific Horizons

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“…Artificial intelligence in veterinary medicine is an emerging field that has mostly been applied to large animals, clinical and anatomical pathology and radiology 25–36 . The use of AI for skin disease diagnosis in dogs is rare 37–39 .…”

Section: Discussionmentioning

confidence: 99%

“…Artificial intelligence in veterinary medicine is an emerging field that has mostly been applied to large animals, clinical and anatomical pathology and radiology. [25][26][27][28][29][30][31][32][33][34][35][36] The use of AI for skin disease diagnosis in dogs is rare. [37][38][39] Previous studies used image classification to label entire images, while the current approach uses object detection to not only locate, but also label individual objects within an image.…”

Section: Modelmentioning

confidence: 99%

Computer vision model for the detection of canine pododermatitis and neoplasia of the paw

Smith,

Carroll,

Aravamuthan

et al. 2023

Veterinary Dermatology

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BackgroundArtificial intelligence (AI) has been used successfully in human dermatology. AI utilises convolutional neural networks (CNN) to accomplish tasks such as image classification, object detection and segmentation, facilitating early diagnosis. Computer vision (CV), a field of AI, has shown great results in detecting signs of human skin diseases. Canine paw skin diseases are a common problem in general veterinary practice, and computer vision tools could facilitate the detection and monitoring of disease processes. Currently, no such tool is available in veterinary dermatology.AnimalsDigital images of paws from healthy dogs and paws with pododermatitis or neoplasia were used.ObjectivesWe tested the novel object detection model Pawgnosis, a Tiny YOLOv4 image analysis model deployed on a microcomputer with a camera for the rapid detection of canine pododermatitis and neoplasia.Materials and MethodsThe prediction performance metrics used to evaluate the models included mean average precision (mAP), precision, recall, average precision (AP) for accuracy and frames per second (FPS) for speed.ResultsA large dataset labelled by a single individual (Dataset A) used to train a Tiny YOLOv4 model provided the best results with a mean mAP of 0.95, precision of 0.86, recall of 0.93 and 20 FPS.Conclusions and Clinical RelevanceThis novel object detection model has the potential for application in the field of veterinary dermatology.

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“…Health and behavior : many studies proposed AI and sensor-based alternatives to identify, monitor and control health-related features, such as early mastitis identification in dairy cows ( Wang et al, 2022 ), gait analysis and lameness detection ( Zhao et al, 2018 ; Nejati et al, 2023 ), respiratory pattern ( Wu et al, 2020 , 2023 ), diarrhea and respiratory disease in calves ( Ghaffari et al, 2022 ), body condition score ( Huang et al, 2019 ), body temperature measurement ( Giro et al, 2019 ; Barreto et al, 2022 ), and behavior monitoring ( Li et al, 2019 ; Watanabe et al, 2021 ; Gong et al, 2022 ).…”

Section: Applicationsmentioning

confidence: 99%

Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview

Curti,

Selli,

Pinto

et al. 2023

Anim. Reprod.

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Some sectors of animal production and reproduction have shown great technological advances due to the development of research areas such as Precision Livestock Farming (PLF). PLF is an innovative approach that allows animals to be monitored, through the adoption of cutting-edge technologies that continuously collect real-time data by combining the use of sensors with advanced algorithms to provide decision tools for farmers. Artificial Intelligence (AI) is a field that merges computer science and large datasets to create expert systems that are able to generate predictions and classifications similarly to human intelligence. In a simplified manner, Machine Learning (ML) is a branch of AI, and can be considered as a broader field that encompasses Deep Learning (DL, a Neural Network formed by at least three layers), generating a hierarchy of subsets formed by AI, ML and DL, respectively. Both ML and DL provide innovative methods for analyzing data, especially beneficial for large datasets commonly found in livestock-related activities. These approaches enable the extraction of valuable insights to address issues related to behavior, health, reproduction, production, and the environment, facilitating informed decision-making. In order to create the referred technologies, studies generally go through five steps involving data processing: acquisition, transferring, storage, analysis and delivery of results. Although the data collection and analysis steps are usually thoroughly reported by the scientific community, a good execution of each step is essential to achieve good and credible results, which impacts the degree of acceptance of the proposed technologies in real life practical circumstances. In this context, the present work aims to describe an overview of the current implementations of ML/DL in livestock reproduction and production, as well to identify potential challenges and critical points in each of the five steps mentioned, which can affect results and application of AI techniques by farmers in practical situations.

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Monitoring the respiratory behavior of multiple cows based on computer vision and deep learning

Cited by 23 publications

References 43 publications

Innovation enterprise development strategy in animal husbandry

Innovation enterprise development strategy in animal husbandry

Computer vision model for the detection of canine pododermatitis and neoplasia of the paw

Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview

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