A RFID-based traceability system for cattle breeding in China

Wang, Zaiqiong; Fu, Zetian; Chen, Wei; Hu, Jinyou

doi:10.1109/iccasm.2010.5620675

Cited by 12 publications

(7 citation statements)

References 3 publications

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“…Insect Bee [67], [83], [105] Insect [50] Weevil [77] Bird Bird [32] Broiler chicken [53] Chicken [47], [73] Goose [69] Hen [68] Hummingbird [19] Laying hen [33], [71] Pigeon [49] Poultry [52] Wild bird [30], [99] Mammal Bison [107] Buffalo [95] Cattle [89], [36], [51], [94], [38], [3], [57], [43], [74], [91], [35], [76], [52], [90], [8], [18], [80], [7], [88], [42], [87], [85], [62], [58] Domestic animal [18], [61], [82] Elephant [63] Goat [57], …”

Section: Type Animals Referencesmentioning

confidence: 99%

RFID Technology for Animal Tracking: A Survey

Pereira,

Araújo,

Silva

et al. 2023

IEEE J. Radio Freq. Identif.

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The application of animal tracking holds significant importance across diverse economic domains, encompassing sectors including livestock husbandry, agricultural practices, and the conservation of wildlife populations. It aims to track and understand animal behavior, movement patterns, and health status. The predominant use of RFID technology is observed within the domains of logistics, localization, and the tracking of goods. Notably, the application of this technology in the field of animal tracking has experienced a significant surge in popularity in recent years. This paper conducts a systematic literature review focused on understanding how RFID technology is being applied in the field of animal tracking. We have conducted a stateof-the-art research regarding animal tracking solutions in the scientific literature and patents. We have analyzed these solutions targeting which animals are being tracked, which problems are addressed, operating frequency, and whether other technologies are combined with RFID for animal tracking purposes. Among the categories of problems addressed, livestock management emerged as the main area, followed by animal tracking and traceability. Mammals, especially cattle, are the most common type of animal monitored. Considering RFID technology, passive UHF tags appeared more often. Moreover, many works also employed cameras and GPS together with RFID. Finally, this work can significantly contribute to this field by systematically presenting a state-of-the-art application of RFID for animal tracking.

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Section: Type Animals Referencesmentioning

confidence: 99%

RFID Technology for Animal Tracking: A Survey

Pereira,

Araújo,

Silva

et al. 2023

IEEE J. Radio Freq. Identif.

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show abstract

“…Radio frequency identification (RFID) technology, ear-tags, and branding have been widely used to identify individual livestock. [7][8][9][10][11] As shown in Figure 1, however, ear-tags and branding are harmful to livestock, and, given the need for agricultural automation, RFID is more popular. The hardware component in RFID can make individual identification more accurate.…”

Section: Related Workmentioning

confidence: 99%

“…The hardware component in RFID can make individual identification more accurate. Although RFID technology is used to implement the automation process in livestock breeding, [8][9][10][11] it is expensive, and will be affected by temperature, circuit, electromagnetic field and other environmental problems found in sheep and cattle farms. Therefore, we decided that RFID technology was not suitable for this study or for large-scale…”

Section: Related Workmentioning

confidence: 99%

Individual identification of cashmere goats via method of fusion of multiple optimization

Shang

Hongke

Wang

et al. 2022

Computer Animation & Virtual

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Facial recognition technology and related research have matured over time, but research in the field of individual animal recognition is still very limited. Therefore, this article focuses on the identification of cashmere goats with similar characteristics. First, the single shot multibox detector network was used to process the dataset. Next, transfer learning was applied to learn the characteristics of the goats, as well as the loss function is composed of Triplet Loss and Label Smoothing CrossEntropy Loss function. The result of Label Smoothing CrossEntropy Loss function is fused by multiple different branches, which is convenient for classification. We added a small number of images of 24 different breeds of sheep to each cashmere goat dataset with different ID to promote the distance between training individuals, and then used the trained model to find the number of goats with the lowest recognition accuracy. The Cycle-Consistent Adversarial Network (Cycle-GAN) learned the goat dataset with a high error rate in individual identification. Unlike previous studies using the Cycle-GAN, we took the novel approach of using this network to learn and combine the features seen in photos of cashmere goats. Since the learned features were all observed in the same goats, this method achieved better results in learning the features of the goats. Finally, we found that recognition can be performed on this data with an accuracy of 93.75%. These results suggest that identification based on deep learning has a high accuracy rate, as well as great value in identifying individual cashmere goats.

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“…Classical livestock identification techniques, such as ear notching [10], ear tattooing [11], hot iron branding [12], and ear tags [13][14][15][16], is subject to equipment loss, duplication, fraud, animal welfare security, monitoring cost, and distance challenges. Instead, based on biometric traits, non-contact identification is a new trend in livestock identification due to its uniqueness, invariance, low cost and easy operation, and high animal welfare.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Deep Learning for Automatic Multi-View Face Detection in Cattle

Wang

Guo

et al. 2021

Agriculture

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Individual identification plays an important part in disease prevention and control, traceability of meat products, and improvement of agricultural false insurance claims. Automatic and accurate detection of cattle face is prior to individual identification and facial expression recognition based on image analysis technology. This paper evaluated the possibility of the cutting-edge object detection algorithm, RetinaNet, performing multi-view cattle face detection in housing farms with fluctuating illumination, overlapping, and occlusion. Seven different pretrained CNN models (ResNet 50, ResNet 101, ResNet 152, VGG 16, VGG 19, Densenet 121 and Densenet 169) were fine-tuned by transfer learning and re-trained on the dataset in the paper. Experimental results showed that RetinaNet incorporating the ResNet 50 was superior in accuracy and speed through performance evaluation, which yielded an average precision score of 99.8% and an average processing time of 0.0438 s per image. Compared with the typical competing algorithms, the proposed method was preferable for cattle face detection, especially in particularly challenging scenarios. This research work demonstrated the potential of artificial intelligence towards the incorporation of computer vision systems for individual identification and other animal welfare improvements.

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A RFID-based traceability system for cattle breeding in China

Cited by 12 publications

References 3 publications

RFID Technology for Animal Tracking: A Survey

RFID Technology for Animal Tracking: A Survey

Individual identification of cashmere goats via method of fusion of multiple optimization

Evaluation of Deep Learning for Automatic Multi-View Face Detection in Cattle

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