Detection system of dead and sick chickens in large scale farms based on artificial intelligence

Bao, Yiqin; Lu, Hongbing; Zhao, Qiang; Yang, Zhongxue; Xu, Wenbin

doi:10.3934/mbe.2021306

Cited by 15 publications

(7 citation statements)

References 20 publications

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“…They then designed and computed three-dimensional total variance to represent the animal’s level of activity. Finally, by taking a machine learning classification method, activity intensity is used to determine the condition of the chicken, enabling the determination of whether it is alive or dead [ 11 ]. In this technique, the chicken’s activity data are vulnerable to interference from the factory environment, human intervention, immune operation, and feeding restriction, which causes the system to misjudge and lowers judgment accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to image recognition, sensors combined with machine learning algorithms have also been used to identify dead and sick chickens. For example, Bao and Lu proposed an artificial intelligence-based sensor detection method [ 11 ]. The three-dimensional total variance is intended to represent the intensity of the chicken’s activity.…”

Section: Introductionmentioning

confidence: 99%

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Recognition of Abnormal-Laying Hens Based on Fast Continuous Wavelet and Deep Learning Using Hyperspectral Images

Qin

Lai

Pan

et al. 2023

Sensors

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The egg production of laying hens is crucial to breeding enterprises in the laying hen breeding industry. However, there is currently no systematic or accurate method to identify low-egg-production-laying hens in commercial farms, and the majority of these hens are identified by breeders based on their experience. In order to address this issue, we propose a method that is widely applicable and highly precise. First, breeders themselves separate low-egg-production-laying hens and normal-laying hens. Then, under a halogen lamp, hyperspectral images of the two different types of hens are captured via hyperspectral imaging equipment. The vertex component analysis (VCA) algorithm is used to extract the cockscomb end member spectrum to obtain the cockscomb spectral feature curves of low-egg-production-laying hens and normal ones. Next, fast continuous wavelet transform (FCWT) is employed to analyze the data of the feature curves in order to obtain the two-dimensional spectral feature image dataset. Finally, referring to the two-dimensional spectral image dataset of the low-egg-production-laying hens and normal ones, we developed a deep learning model based on a convolutional neural network (CNN). When we tested the model’s accuracy by using the prepared dataset, we found that it was 0.975 percent accurate. This outcome demonstrates our identification method, which combines hyperspectral imaging technology, an FCWT data analysis method, and a CNN deep learning model, and is highly effective and precise in laying-hen breeding plants. Furthermore, the attempt to use FCWT for the analysis and processing of hyperspectral data will have a significant impact on the research and application of hyperspectral technology in other fields due to its high efficiency and resolution characteristics for data signal analysis and processing.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recognition of Abnormal-Laying Hens Based on Fast Continuous Wavelet and Deep Learning Using Hyperspectral Images

Qin

Lai

Pan

et al. 2023

Sensors

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“…In addition, the high density of stacked-cage broilers (70 broilers/cage), and the cluster characteristics of broilers make the occlusion more serious [31]. Bao et al [23] measured the three-dimensional displacement of the chickens by fastening a foot ring on each chicken to identify dead and sick chickens in the flock. As the maximum displacement and three-dimensional total variance of the dead chickens were almost 0, this method could identify dead chickens in the flock with 100 percent accuracy.…”

Section: Analysis Of the Defects Of The Dead Broiler Detection Systemmentioning

confidence: 99%

“…The chicken inspection robot could walk in the corridor of the chicken house and monitor chickens' health and behavior with similar methods in real time. Bao et al [23] proposed a sensor detection method to detect sick and dead chickens: by fastening a foot ring on each chicken, the three-dimensional displacement of chickens was analyzed with a machine-learning classification method to detect the dead and sick chickens in the flocks. Liu et al [24] developed a small removal system for dead chickens for a Taiwanese poultry house, which could walk in the flat chicken house and collect dead chickens automatically.…”

Section: Introductionmentioning

confidence: 99%

A Dead Broiler Inspection System for Large-Scale Breeding Farms Based on Deep Learning

et al. 2022

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Stacked cage is the main breeding method of the large-scale farm in China. In broiler farms, dead broiler inspection is a routine task in the breeding process. It refers to the manual inspection of all cages and removal of dead broilers in the broiler house by the breeders every day. However, as the total amount of broilers is huge, the inspection work is not only time-consuming but also laborious. Therefore, a dead broiler inspection system is constructed in this study to replace the manual inspection work. It mainly consists of an autonomous inspection platform and a dead broiler detection model. The automatic inspection platform performs inspections at the speed of 0.2 m/s in the broiler house aisle, and simultaneously collects images of the four-layer broilers. The images are sent to a server and processed by a dead broiler detection model, which was developed based on the YOLOv3 network. A mosaic augment, the Swish function, an spatial pyramid pooling (SPP) module, and complete intersection over union (CIoU) loss are used to improve the YOLOv3 performance. It achieves a 98.6% mean average precision (intersection of union (IoU) = 0.5) and can process images at 0.007 s per frame. The dead broiler detection model is robust to broilers of different ages and can adapt to different lighting conditions. It is deployed on the server with a human–machine interface. By observing the processing results using the human–machine interface, the breeders could directly find the cage position of dead broilers and remove them, which could reduce the workload of breeders and promote the intelligent development of poultry breeding.

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“…The technological architecture consists of coordination between hardware and software components to capture images with the help of cameras, process and analyze the visual data (Neethirajan, 2022), (Monitoring et al, 2004). Camera, which acts as the eye of the system, is connected in a stationary position above the poultry environment depending on the individual design and method adopted (Eijk et al, 2022), (Bao et al, 2021). The system captures activities of the birds within a defined environment, ranging from movement, interaction within the flock, water consumption, and feeding patterns (Rushen et al, 2012), (Massari et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Review of Poultry Monitoring using Computer Vision

Olanrewaju,

Abdulhafiz,

Liman

2024

NJP

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Poultry farming is an important unit in global agronomy, contributing immensely to the production of meat and eggs. Safeguarding the health and welfare of poultry is vital for ethical and financial reasons. In recent years, computer vision awareness has gained prominence as a powerful tool for poultry monitoring. This review paper provides an outline of the application of computer vision in poultry monitoring. We explore the different phases of this technology, with real-time image acquisition, object recognition, and behavior analysis. By connecting cameras and sophisticated algorithms, a computer vision system can distinguish unusual behavior, track poultry activities and observe its environmental conditions. Furthermore, this study discovers the benefits of computer vision in poultry farming, including early detection of disease, better production efficiency and improved animal welfare. In conclusion, the application of computer vision in monitoring poultry holds immense potential for the industry; by providing a corridor to a more sustainable and ethical poultry farming system with increased productivity. This paper equally discusses recent developments, challenges, forthcoming prospects in the field and academic research gaps identified.

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Detection system of dead and sick chickens in large scale farms based on artificial intelligence

Cited by 15 publications

References 20 publications

Recognition of Abnormal-Laying Hens Based on Fast Continuous Wavelet and Deep Learning Using Hyperspectral Images

Recognition of Abnormal-Laying Hens Based on Fast Continuous Wavelet and Deep Learning Using Hyperspectral Images

A Dead Broiler Inspection System for Large-Scale Breeding Farms Based on Deep Learning

Review of Poultry Monitoring using Computer Vision

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