An experimental study of stunned state detection for broiler chickens using an improved convolution neural network algorithm

Ye, Chang-wen; Yu, Zhenwei; Kang, Rui; Yousaf, Khurram; Chen, Qi; Chen, Kunjie; Huang, Yuping

doi:10.1016/j.compag.2020.105284

Cited by 22 publications

(16 citation statements)

References 40 publications

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“…Moreover, the results of this study indicate that it achieved high predictive value (R 2 = 0.99). Meanwhile, Amrei et al [49] achieved lower value (R 2 = 0.98) with the camera height of 2m above floor.…”

Section: Camera Positionmentioning

confidence: 97%

“…Computer vision has been widely used in various processes of different poultry production systems. It includes automation of the house management, behavior, and welfare [11,[29][30][31][32][33][34][35][36][37][38][39][40], disease detection [28,[41][42][43][44][45][46][47], weight measurement [27,[48][49], slaughtering process [50][51], carcass quality [52][53][54][55], and egg examination [56][57][58][59][60][61][62][63][64][65]. On the other hand, computer vision also popular on other livestock monitoring, such as pig [73][74][75][76][77][78][79][80], sheep or cattle …”

Section: Overview Of Computer Vision In Poultry Farmmentioning

confidence: 99%

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A Review on Computer Vision Technology for Monitoring Poultry Farm—Application, Hardware, and Software

et al. 2021

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Section: Camera Positionmentioning

confidence: 97%

Section: Overview Of Computer Vision In Poultry Farmmentioning

confidence: 99%

A Review on Computer Vision Technology for Monitoring Poultry Farm—Application, Hardware, and Software

et al. 2021

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“…Many versions of the YOLO algorithm have been developed to enhance the system effectiveness [ 6 , 8 , 36 ]. Therefore, the effectiveness of the proposed method was verified by comparing it with two deep learning algorithms, namely YOLO v3 and Tiny YOLO v4.…”

Section: Object Detectionmentioning

confidence: 99%

“…The development of a robot that can autonomously remove dead chickens through object detection in poultry houses would not only reduce the time and labor costs but also human–poultry contact to improve biological safety. Deep learning is widely adopted for object detection in various fields, such as medical care [ 5 , 6 ], autonomous driving [ 7 ], and chicken industry [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Identifying Images of Dead Chickens with a Chicken Removal System Integrated with a Deep Learning Algorithm

Liu

Chen

Tsai

et al. 2021

Sensors

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The chicken industry, in which broiler chickens are bred, is the largest poultry industry in Taiwan. In a traditional poultry house, breeders must usually observe the health of the broilers in person on the basis of their breeding experience at regular times every day. When a breeder finds unhealthy broilers, they are removed manually from the poultry house to prevent viruses from spreading in the poultry house. Therefore, in this study, we designed and constructed a novel small removal system for dead chickens for Taiwanese poultry houses. In the mechanical design, this system mainly contains walking, removal, and storage parts. It comprises robotic arms with a fixed end and sweep-in devices for sweeping dead chickens, a conveyor belt for transporting chickens, a storage cache for storing chickens, and a tracked vehicle. The designed system has dimensions of approximately 1.038 × 0.36 × 0.5 m3, and two dead chickens can be removed in a single operation. The walking speed of the chicken removal system is 3.3 cm/s. In order to enhance the automation and artificial intelligence in the poultry industry, the identification system was used in a novel small removal system. The conditions of the chickens in a poultry house can be monitored remotely by using a camera, and dead chickens can be identified through deep learning based on the YOLO v4 algorithm. The precision of the designed system reached 95.24% in this study, and dead chickens were successfully moved to the storage cache. Finally, the designed system can reduce the contact between humans and poultry to effectively improve the overall biological safety.

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“…In recent years, with the development of machine vision, domestic and foreign scholars have widely applied nondestructive machine vision testing to the identification and classification of agricultural products [13]. Based on texture uniformity measurement technology, Hassan and Nashat [14] achieved a 100% recognition rate of healthy olives.…”

Section: Introductionmentioning

confidence: 99%

Defect Classification of Green Plums Based on Deep Learning

Zhou

Zhuang

Liu

et al. 2020

Sensors

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The green plum is rich in amino acids, lipids, inorganic salts, vitamins, and trace elements. It has high nutritional value and medicinal value and is very popular among Chinese people. However, green plums are susceptible to collisions and pests during growth, picking, storage, and transportation, causing surface defects, affecting the quality of green plums and their products and reducing their economic value. In China, defect detection and grading of green plum products are still performed manually. Traditional manual classification has low accuracy and high cost, which is far from meeting the production needs of green plum products. In order to improve the economic value of green plums and their products and improve the automation and intelligence level of the product production process, this study adopted deep learning methods based on a convolutional neural network and cost-effective computer vision technology to achieve efficient classification of green plum defects. First, a camera and LEDs were used to collect 1240 green plum images of RGB, and the green plum experimental classification standard was formulated and divided into five categories, namely, rot, spot, scar, crack, and normal. Images were randomly divided into a training set and test set, and the number of images of the training set was expanded. Then, the stochastic weight averaging (SWA) optimizer and w-softmax loss function were used to improve the VGG network, which was trained and tested to generate a green plum defect detection network model. The average recognition accuracy of green plum defects was 93.8%, the test time for each picture was 84.69 ms, the recognition rate of decay defect was 99.25%, and the recognition rate of normal green plum was 95.65%. The results were compared with the source VGG network, resnet18 network, and green lemon network. The results show that for the classification of green plum defects, the recognition accuracy of the green plum defect detection network increased by 9.8% and 16.6%, and the test speed is increased by 1.87 and 6.21 ms, respectively, which has certain advantages.

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An experimental study of stunned state detection for broiler chickens using an improved convolution neural network algorithm

Cited by 22 publications

References 40 publications

A Review on Computer Vision Technology for Monitoring Poultry Farm—Application, Hardware, and Software

A Review on Computer Vision Technology for Monitoring Poultry Farm—Application, Hardware, and Software

Identifying Images of Dead Chickens with a Chicken Removal System Integrated with a Deep Learning Algorithm

Defect Classification of Green Plums Based on Deep Learning

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