Automatic Detection Method of Dairy Cow Feeding Behaviour Based on YOLO Improved Model and Edge Computing

Yu, Zhenwei; Liu, Yuehua; Yu, Sufang; Wang, Ruixue; Song, Zhanhua; Yan, Yinfa; Li, Fade; Wang, Zhonghua; Tian, Fuyang

doi:10.3390/s22093271

Cited by 24 publications

(11 citation statements)

References 27 publications

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“…The second work, presented by Yu et al [15], introduces a method for monitoring dairy cow feeding behavior using edge computing and deep learning algorithms based on the characteristics of dairy cow feeding behavior. Although the themes of our work and theirs are similar, the approaches are different, and we aim to conduct a detailed comparison between our work and theirs in our future research.…”

Section: Discussionmentioning

confidence: 99%

Facial Region Analysis for Individual Identification of Cows and Feeding Time Estimation

2023

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With the increasing number of cows per farmer in Japan, an automatic cow monitoring system is being introduced. One important aspect of such a system is the ability to identify individual cows and estimate their feeding time. In this study, we propose a method for achieving this goal through facial region analysis. We used a YOLO detector to extract the cow head region from video images captured during feeding with the head region cropped as a face region image. The face region image was used for cow identification and transfer learning was employed for identification. In the context of cow identification, transfer learning can be used to train a pre-existing deep neural network to recognize individual cows based on their unique physical characteristics, such as their head shape, markings, or ear tags. To estimate the time of feeding, we divided the feeding area into vertical strips for each cow and established a horizontal line just above the feeding materials to determine whether a cow was feeding or not by using Hough transform techniques. We tested our method using real-life data from a large farm, and the experimental results showed promise in achieving our objectives. This approach has the potential to diagnose diseases and movement disorders in cows and could provide valuable insights for farmers.

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Section: Discussionmentioning

confidence: 99%

Facial Region Analysis for Individual Identification of Cows and Feeding Time Estimation

2023

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“…, ρ(q, q obs ) ≤ ρ 0 0 , ρ(q, q obs ) > ρ 0 (10) where (X − X goal ) is the distance between the robot and the target, and n is a constant and greater than 0. Similarly, the repulsive force on the mobile robot is the negative gradient of the repulsive force field, and the repulsive force F rep (q) is expressed as:…”

Section: Improvement Of Artificial Potential Field Methodsmentioning

confidence: 99%

“…The simulation space adopts a 10 × 10 grid map. It sets the coordinates of the starting position of the robot to be (0, 0) marked with a square, and the coordinates of the end point of the target point to be (10,10), marked with a triangle, and the obstacle coordinate points marked with circles are set between the two to simulate the actual situation.…”

Section: Simulation Analysis Of Obstacle Avoidance Algorithmmentioning

confidence: 99%

“…The accuracy and execution efficiency of multimedia target recognition technology have been greatly improved with the development of deep learning (DL) and machine learning, under the circumstances [10][11][12]; the application of the technology has been extended to the fields of medical imaging [13], video surveillance [14], and robot navigation [15]. In the wave of technological change, traditional agricultural machinery has ushered in a new opportunity for development.…”

Section: Introductionmentioning

confidence: 99%

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Research on Navigation Path Extraction and Obstacle Avoidance Strategy for Pusher Robot in Dairy Farm

Tian

Wang

et al. 2022

Agriculture

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Existing push robots mainly use magnetic induction technology. These devices are susceptible to external electromagnetic interference and have a low degree of intelligence. To make up for the insufficiency of the existing material pushing robots, and at the same time solve the problems of labor-intensive, labor-intensive, and inability to push material in time at night, etc., in this study, an autonomous navigation pusher robot based on 3D lidar is designed, and an obstacle avoidance strategy based on the improved artificial potential field method is proposed. Firstly, the 3D point cloud data of the barn is collected by the self-designed pushing robot, the point cloud data of the area of interest is extracted using a direct-pass filtering algorithm, and the 3D point cloud of the barn is segmented using a height threshold. Secondly, the Least-Squares Method (LSM) and Random Sample Consensus (RANSAC) were used to extract fence lines, and then the boundary contour features were extracted by projection onto the ground. Finally, a target influence factor is added to the repulsive potential field function to determine the principle of optimal selection of the parameters of the improved artificial potential field method and the repulsive direction, and to clarify the optimal obstacle avoidance strategy for the pusher robot. It can verify the obstacle avoidance effect of the improved algorithm. The experimental results showed that under three different environments: no noise, Gaussian noise, and artificial noise, the fence lines were extracted using RANSAC. Taking the change in the slope as an indicator, the obtained results were about −0.058, 0.058, and −0.061, respectively. The slope obtained by the RANSAC method has less variation compared to the no-noise group. Compared with LSM, the extraction results did not change significantly, indicating that RANSAC has a certain resistance to various noises, but RANSAC performs better in extraction effect and real-time performance. The simulation and actual test results show that the improved artificial potential field method can select reasonable parameters and repulsive force directions. The optimized path increases the shortest distance of the obstacle point cloud from the navigation path from 0.18 to 0.41 m, where the average time is 0.059 s, and the standard deviation is 0.007 s. This shows that the optimization method can optimize the path in real time to avoid obstacles, basically meet the requirements of security and real-time performance, and effectively avoid the local minimum problem. This research will provide corresponding technical references for pusher robots to overcome the problems existing in the process of autonomous navigation and pushing operation in complex open scenarios.

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“…MEC is significantly expected to be utilized by future Internet applications. Therefore, various uses of MEC have been proposed [52][53][54][55]. Especially, machine learning and artificial intelligence are effective in a MEC platform [56][57][58][59].…”

Section: Related Workmentioning

confidence: 99%

Optimal Task Allocation Algorithm Based on Queueing Theory for Future Internet Application in Mobile Edge Computing Platform

Katayama

Tachibana

2022

Sensors

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For 5G and future Internet, in this paper, we propose a task allocation method for future Internet application to reduce the total latency in a mobile edge computing (MEC) platform with three types of servers: a dedicated MEC server, a shared MEC server, and a cloud server. For this platform, we first calculate the delay between sending a task and receiving a response for the dedicated MEC server, shared MEC server, and cloud server by considering the processing time and transmission delay. Here, the transmission delay for the shared MEC server is derived using queueing theory. Then, we formulate an optimization problem for task allocation to minimize the total latency for all tasks. By solving this optimization problem, tasks can be allocated to the MEC servers and cloud server appropriately. In addition, we propose a heuristic algorithm to obtain the approximate optimal solution in a shorter time. This heuristic algorithm consists of four algorithms: a main algorithm and three additional algorithms. In this algorithm, tasks are divided into two groups, and task allocation is executed for each group. We compare the performance of our proposed heuristic algorithm with the solution obtained by three other methods and investigate the effectiveness of our algorithm. Numerical examples are used to demonstrate the effectiveness of our proposed heuristic algorithm. From some results, we observe that our proposed heuristic algorithm can perform task allocation in a short time and can effectively reduce the total latency in a short time. We conclude that our proposed heuristic algorithm is effective for task allocation in a MEC platform with multiple types of MEC servers.

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Automatic Detection Method of Dairy Cow Feeding Behaviour Based on YOLO Improved Model and Edge Computing

Cited by 24 publications

References 27 publications

Facial Region Analysis for Individual Identification of Cows and Feeding Time Estimation

Facial Region Analysis for Individual Identification of Cows and Feeding Time Estimation

Research on Navigation Path Extraction and Obstacle Avoidance Strategy for Pusher Robot in Dairy Farm

Optimal Task Allocation Algorithm Based on Queueing Theory for Future Internet Application in Mobile Edge Computing Platform

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