Multiview Monitoring of Individual Cattle Behavior Based on Action Recognition in Closed Barns Using Deep Learning

Fuentes, Alvaro; Han, Shujie; Nasir, Muhammad Fahad; Park, Jongbin; Yoon, Sook; Park, Dong Sun

doi:10.3390/ani13122020

Cited by 8 publications

(4 citation statements)

References 33 publications

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“…The data obtained are used to extract video frames. More precise variations can be captured by extracting images at a rate of 15 frames per second (Fuentes et al, 2023). Optionally, filtering of frames is done to remove noise or irrelevant content.…”

Section: Annotation and Labellingmentioning

confidence: 99%

Application of Artificial Intelligence in Monitoring of Animal Health and Welfare

AlZubi,

Al-Zu'bi

2023

IJAR

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Blackground: With the advent of AI technology, great strides have been made in the realm of animal healthcare. This article delves into the numerous uses of AI in veterinary medicine and demonstrates its revolutionary potential in the field. Artificial intelligence (AI) algorithms have shown impressive skills in illness detection, using medical imaging analysis to help veterinarians discover and categorise diseases with greater accuracy and efficiency. Furthermore, predictive analytics algorithms use various data sources, such as electronic health records and genetic profiles, to recognise trends and forecast illness outbreaks, allowing veterinarians to remotely monitor vital signs and act swiftly paving the way for preventative measures and individualised treatment. Methods: The purpose of this article is to offer a synopsis of the many ways in which artificial intelligence (AI) is being used to improve the health and well-being of animals. Understanding the effects of AI in animal healthcare and setting the stage for its further development will be accomplished via an examination of the present state of the subject. Results: It is evident that through mountains of data from studies and clinical trials, AI is helping to speed up the discovery of novel treatments and improve the understanding of animal health. A responsible and useful application of AI in animal healthcare requires the establishment of ethical concerns, data protection, and regulatory frameworks. Method: This document provides a synopsis of the many ways in which artificial intelligence (AI) is being used to improve the health and well-being of animals.

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Section: Annotation and Labellingmentioning

confidence: 99%

Application of Artificial Intelligence in Monitoring of Animal Health and Welfare

AlZubi,

Al-Zu'bi

2023

IJAR

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“…Despite its importance, the detection and identification of animals have yet to be thoroughly investigated promptly [7]. Implementing an advanced surveillance system capable of automatically monitoring the area and detecting animal presence is becoming crucial to address this issue effectively [8]. The development of various technical alternatives for intrusion detection has frequently surpassed the limitations associated with traditional methods.…”

Section: Introductionmentioning

confidence: 99%

IoT-Based Object-Detection System to Safeguard Endangered Animals and Bolster Agricultural Farm Security

Raiaan,

Fahad,

Chowdhury

et al. 2023

Future Internet

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Significant threats to ecological equilibrium and sustainable agriculture are posed by the extinction of animal species and the subsequent effects on farms. Farmers face difficult decisions, such as installing electric fences to protect their farms, although these measures can harm animals essential for maintaining ecological equilibrium. To tackle these essential issues, our research introduces an innovative solution in the form of an object-detection system. In this research, we designed and implemented a system that leverages the ESP32-CAM platform in conjunction with the YOLOv8 object-detection model. Our proposed system aims to identify endangered species and harmful animals within farming environments, providing real-time alerts to farmers and endangered wildlife by integrating a cloud-based alert system. To train the YOLOv8 model effectively, we meticulously compiled diverse image datasets featuring these animals in agricultural settings, subsequently annotating them. After that, we tuned the hyperparameter of the YOLOv8 model to enhance the performance of the model. The results from our optimized YOLOv8 model are auspicious. It achieves a remarkable mean average precision (mAP) of 92.44% and an impressive sensitivity rate of 96.65% on an unseen test dataset, firmly establishing its efficacy. After achieving an optimal result, we employed the model in our IoT system and when the system detects the presence of these animals, it immediately activates an audible buzzer. Additionally, a cloud-based system was utilized to notify neighboring farmers effectively and alert animals to potential danger. This research’s significance lies in its potential to drive the conservation of endangered species while simultaneously mitigating the agricultural damage inflicted by these animals.

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“…Within the domain of computer vision, recognized for its intricate tasks spanning object detection, action recognition, multi-object tracking, and more, AI has demonstrated successful applications across diverse agricultural domains. These applications encompass crucial areas such as plant disease detection [1], pig behavior recognition [2], cattle behavior recognition [3], and livestock tracking [4], among others. It is paramount to underscore the pivotal role of livestock farming in the broader agricultural landscape, serving as a primary source of meat production for a significant portion of the global population.…”

Section: Introductionmentioning

confidence: 99%

Pig Movement Estimation by Integrating Optical Flow with a Multi-Object Tracking Model

Zhou,

Chung,

Kakar

et al. 2023

Sensors

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Pig husbandry constitutes a significant segment within the broader framework of livestock farming, with porcine well-being emerging as a paramount concern due to its direct implications on pig breeding and production. An easily observable proxy for assessing the health of pigs lies in their daily patterns of movement. The daily movement patterns of pigs can be used as an indicator of their health, in which more active pigs are usually healthier than those who are not active, providing farmers with knowledge of identifying pigs’ health state before they become sick or their condition becomes life-threatening. However, the conventional means of estimating pig mobility largely rely on manual observations by farmers, which is impractical in the context of contemporary centralized and extensive pig farming operations. In response to these challenges, multi-object tracking and pig behavior methods are adopted to monitor pig health and welfare closely. Regrettably, these existing methods frequently fall short of providing precise and quantified measurements of movement distance, thereby yielding a rudimentary metric for assessing pig health. This paper proposes a novel approach that integrates optical flow and a multi-object tracking algorithm to more accurately gauge pig movement based on both qualitative and quantitative analyses of the shortcomings of solely relying on tracking algorithms. The optical flow records accurate movement between two consecutive frames and the multi-object tracking algorithm offers individual tracks for each pig. By combining optical flow and the tracking algorithm, our approach can accurately estimate each pig’s movement. Moreover, the incorporation of optical flow affords the capacity to discern partial movements, such as instances where only the pig’s head is in motion while the remainder of its body remains stationary. The experimental results show that the proposed method has superiority over the method of solely using tracking results, i.e., bounding boxes. The reason is that the movement calculated based on bounding boxes is easily affected by the size fluctuation while the optical flow data can avoid these drawbacks and even provide more fine-grained motion information. The virtues inherent in the proposed method culminate in the provision of more accurate and comprehensive information, thus enhancing the efficacy of decision-making and management processes within the realm of pig farming.

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Multiview Monitoring of Individual Cattle Behavior Based on Action Recognition in Closed Barns Using Deep Learning

Cited by 8 publications

References 33 publications

Application of Artificial Intelligence in Monitoring of Animal Health and Welfare

Application of Artificial Intelligence in Monitoring of Animal Health and Welfare

IoT-Based Object-Detection System to Safeguard Endangered Animals and Bolster Agricultural Farm Security

Pig Movement Estimation by Integrating Optical Flow with a Multi-Object Tracking Model

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