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Computer Vision Systems for Practica...
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Rao, Prajwal,
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Computer Vision Systems for Practical Applications in Precision Livestock Farming /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Computer Vision Systems for Practical Applications in Precision Livestock Farming // Prajwal Rao.
作者:
Rao, Prajwal,
面頁冊數:
1 electronic resource (75 pages)
附註:
Source: Masters Abstracts International, Volume: 86-04.
Contained By:
Masters Abstracts International86-04.
標題:
Wrist. -
電子資源:
https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31606873
ISBN:
9798342140812
Computer Vision Systems for Practical Applications in Precision Livestock Farming /
Rao, Prajwal,
Computer Vision Systems for Practical Applications in Precision Livestock Farming /
Prajwal Rao. - 1 electronic resource (75 pages)
Source: Masters Abstracts International, Volume: 86-04.
The use of advanced imaging technology and algorithms for managing and monitoring livestock improves various aspects of livestock, such as health monitoring, behavioral analysis, early disease detection, feed management, and overall farming efficiency. Leveraging computer vision techniques such as keypoint detection, and depth estimation for these problems help to automate repeatable tasks, which in turn improves farming efficiency. In this thesis, we delve into two main aspects that are early disease detection, and feed management:Phenotyping Ducks using Keypoint Detection:A platform to measure uck phenotypes such as wingspan, back length, and hip width packaged in an online user interface for ease of use. Real-Time Cattle Intake Monitoring Using Computer Vision:A complete end-to-end real-time monitoring system to measure cattle feed intake using stereo cameras.Furthermore, considering the above implementations and their drawbacks, we propose a cost-effective simulation environment for feed estimation to conduct extensive experiments prior to real-world implementation. This approach allows us to test and refine the computer vision systems under controlled conditions, identify potential issues, and optimize performance without the high costs and risks associated with direct deployment on farms. By simulating various scenarios and conditions, we can gather valuable data, improve algorithm accuracy, and ensure the system's robustness. Ultimately, this preparatory step will facilitate a smoother transition to real-world applications, enhancing the reliability and effectiveness of computer vision in precision livestock farming.
English
ISBN: 9798342140812Subjects--Topical Terms:
3681930
Wrist.
Computer Vision Systems for Practical Applications in Precision Livestock Farming /
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The use of advanced imaging technology and algorithms for managing and monitoring livestock improves various aspects of livestock, such as health monitoring, behavioral analysis, early disease detection, feed management, and overall farming efficiency. Leveraging computer vision techniques such as keypoint detection, and depth estimation for these problems help to automate repeatable tasks, which in turn improves farming efficiency. In this thesis, we delve into two main aspects that are early disease detection, and feed management:Phenotyping Ducks using Keypoint Detection:A platform to measure uck phenotypes such as wingspan, back length, and hip width packaged in an online user interface for ease of use. Real-Time Cattle Intake Monitoring Using Computer Vision:A complete end-to-end real-time monitoring system to measure cattle feed intake using stereo cameras.Furthermore, considering the above implementations and their drawbacks, we propose a cost-effective simulation environment for feed estimation to conduct extensive experiments prior to real-world implementation. This approach allows us to test and refine the computer vision systems under controlled conditions, identify potential issues, and optimize performance without the high costs and risks associated with direct deployment on farms. By simulating various scenarios and conditions, we can gather valuable data, improve algorithm accuracy, and ensure the system's robustness. Ultimately, this preparatory step will facilitate a smoother transition to real-world applications, enhancing the reliability and effectiveness of computer vision in precision livestock farming.
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