Research on broiler health status recognition method based on improved YOLOv5

Cui, Yue; Kong, Xiangchao; Chen, Changxi; Li, Yiwei

doi:10.1016/j.atech.2023.100324

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Several studies sought to track chickens in different rearing environments [6,[18][19][20][21][22]. A common problem in tracking organisms is the loss of detection caused by traditional methods, causing ID changes during tracking.…”

Section: Introductionmentioning

confidence: 99%

Chicken Tracking and Individual Bird Activity Monitoring Using the BoT-SORT Algorithm

Siriani,

Miranda,

Mehdizadeh

et al. 2023

AgriEngineering

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The analysis of chicken movement on the farm has several applications in evaluating the well-being and health of birds. Low locomotion may be associated with locomotor problems, and undesirable bird movement patterns may be related to environmental discomfort or fear. Our objective was to test the BoT-SORT object tracking architecture embedded in Yolo v8 to monitor the movement of cage-free chickens and extract measures to classify running, exploring, and resting behaviors, the latter of which includes all other behaviors that do not involve displacement. We trained a new model with a dataset of 3623 images obtained with a camera installed on the ceiling (top images) from an experiment with layers raised cage-free in small-scale aviaries and housed in groups of 20 individuals. The model presented a mAP of 98.5%, being efficient in detecting and tracking the chickens in the video. From the tracking, it was possible to record the movements and directions of individual birds, and we later classified the movement. The results obtained for a group of 20 chickens demonstrated that approximately 84% of the time, the birds remained resting, 10% of the time exploring, and 6% of the time running. The BoT-SORT algorithm was efficient in maintaining the identification of the chickens, and our tracking algorithm was efficient in classifying the movement, allowing us to quantify the time of each movement class. Our algorithm and the measurements we extract to classify bird movements can be used to assess the welfare and health of chickens and contribute to establishing standards for comparisons between individuals and groups raised in different environmental conditions.

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Section: Introductionmentioning

confidence: 99%

Chicken Tracking and Individual Bird Activity Monitoring Using the BoT-SORT Algorithm

Siriani,

Miranda,

Mehdizadeh

et al. 2023

AgriEngineering

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Research on 10 kinds of beverage target detection based on YOLOv8

Zhang

2023

2023 IEEE 6th International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

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Automatic Perception of Typical Abnormal Situations in Cage-Reared Ducks Using Computer Vision

Zhao,

Bai,

Huo

et al. 2024

Animals

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Overturning and death are common abnormalities in cage-reared ducks. To achieve timely and accurate detection, this study focused on 10-day-old cage-reared ducks, which are prone to these conditions, and established prior data on such situations. Using the original YOLOv8 as the base network, multiple GAM attention mechanisms were embedded into the feature fusion part (neck) to enhance the network’s focus on the abnormal regions in images of cage-reared ducks. Additionally, the Wise-IoU loss function replaced the CIoU loss function by employing a dynamic non-monotonic focusing mechanism to balance the data samples and mitigate excessive penalties from geometric parameters in the model. The image brightness was adjusted by factors of 0.85 and 1.25, and mainstream object-detection algorithms were adopted to test and compare the generalization and performance of the proposed method. Based on six key points around the head, beak, chest, tail, left foot, and right foot of cage-reared ducks, the body structure of the abnormal ducks was refined. Accurate estimation of the overturning and dead postures was achieved using the HRNet-48. The results demonstrated that the proposed method accurately recognized these states, achieving a mean Average Precision (mAP) value of 0.924, which was 1.65% higher than that of the original YOLOv8. The method effectively addressed the recognition interference caused by lighting differences, and exhibited an excellent generalization ability and comprehensive detection performance. Furthermore, the proposed abnormal cage-reared duck pose-estimation model achieved an Object Key point Similarity (OKS) value of 0.921, with a single-frame processing time of 0.528 s, accurately detecting multiple key points of the abnormal cage-reared duck bodies and generating correct posture expressions.

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Research on broiler health status recognition method based on improved YOLOv5

Cited by 3 publications

References 17 publications

Chicken Tracking and Individual Bird Activity Monitoring Using the BoT-SORT Algorithm

Chicken Tracking and Individual Bird Activity Monitoring Using the BoT-SORT Algorithm

Research on 10 kinds of beverage target detection based on YOLOv8

Automatic Perception of Typical Abnormal Situations in Cage-Reared Ducks Using Computer Vision

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