Automatic Cow Location Tracking System Using Ear Tag Visual Analysis

Zin, Thi Thi; Pwint, Moe; Seint, Pann Thinzar; Thant, Shin; Misawa, Shuhei; Sumi, Kosuke; Yoshida, Kyohiro

doi:10.3390/s20123564

Cited by 28 publications

(15 citation statements)

References 15 publications

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“…In recent years, OCR concepts have been used in applications for banking, healthcare, and the legal industry, e.g., for invoice imaging [11]. The authors in [12] and [13] apply various projection methods for the segmentation of digits and CNN for recognition. In another work, Kannada optical character recognition was implemented on the Android operating system for Kannada sign boards [14].…”

Section: Related Workmentioning

confidence: 99%

Handwritten Character Recognition on Android for Basic Education Using Convolutional Neural Network

et al. 2021

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An international initiative called Education for All (EFA) aims to create an environment in which everyone in the world can get an education. Especially in developing countries, many children lack access to a quality education. Therefore, we propose an offline self-learning application to learn written English and basic calculation for primary level students. It can also be used as a supplement for teachers to make the learning environment more interactive and interesting. In our proposed system, handwritten characters or words written on tablets were saved as input images. Then, we performed character segmentation by using our proposed character segmentation methods. For the character recognition, the Convolutional Neural Network (CNN) was used for recognizing segmented characters. For building our own dataset, handwritten data were collected from primary level students in developing countries. The network model was trained on a high-end machine to reduce the workload on the Android tablet. Various types of classifiers (digit and special characters, uppercase letters, lowercase letters, etc.) were created in order to reduce the incorrect classification. According to our experimental results, the proposed system achieved 95.6% on the 1000 randomly selected words and 98.7% for each character.

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Section: Related Workmentioning

confidence: 99%

Handwritten Character Recognition on Android for Basic Education Using Convolutional Neural Network

et al. 2021

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“…For ear tags of different colors modifying the color thresholds will be easy to implement with some tuning of these parameters. Other studies have used alternative methods to find the ear tag or recognize digits such as template matching (Ilestrand, 2017;Zin et al, 2020). For instance, Zin et al (2020), used the shape of the head to find the location of ear tags, combined with color detection in a following step.…”

Section: Discussionmentioning

confidence: 99%

“…Other studies have used alternative methods to find the ear tag or recognize digits such as template matching (Ilestrand, 2017;Zin et al, 2020). For instance, Zin et al (2020), used the shape of the head to find the location of ear tags, combined with color detection in a following step. They found 100% accuracy in detection cow heads and subsequently 92.5% accuracy for digit recognition which is similar but higher than the 87% found in this study for digit recognition.…”

Section: Discussionmentioning

confidence: 99%

“…A method for reading ear tag numbers from images was described by Ilestrand (2017) who tested several optical character recognition methods. Zin et al (2020) used selected images from 4k videos and developed an algorithm for head detection, ear tag detection, and reading the ear tag number using a Convolutional Neural Network (CNN). These methods have been applied to static datasets of previously collected images.…”

Section: Introductionmentioning

confidence: 99%

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Continuous real-time cow identification by reading ear tags from live-stream video

Bastiaansen¹,

Hulsegge²,

Schokker³

et al. 2022

Front. Anim. Sci.

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In precision dairy farming there is a need for continuous and real-time availability of data on cows and systems. Data collection using sensors is becoming more common and it can be difficult to connect sensor measurements to the identification of the individual cow that was measured. Cows can be identified by RFID tags, but ear tags with identification numbers are more widely used. Here we describe a system that makes the ear tag identification of the cow continuously available from a live-stream video so that this information can be added to other data streams that are collected in real-time. An ear tag reading model was implemented by retraining and existing model, and tested for accuracy of reading the digits on cows ear tag images obtained from two dairy farms. The ear tag reading model was then combined with a video set up in a milking robot on a dairy farm, where the identification by the milking robot was considered ground-truth. The system is reporting ear tag numbers obtained from live-stream video in real-time. Retraining a model using a small set of 750 images of ear tags increased the digit level accuracy to 87% in the test set. This compares to 80% accuracy obtained with the starting model trained on images of house numbers only. The ear tag numbers reported by real-time analysis of live-stream video identified the right cow 93% of the time. Precision and sensitivity were lower, with 65% and 41%, respectively, meaning that 41% of all cow visits to the milking robot were detected with the correct cow’s ear tag number. Further improvement in sensitivity needs to be investigated but when ear tag numbers are reported they are correct 93% of the time which is a promising starting point for future system improvements.

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“…However, this location method cannot well-adapt to distortion deformation of ID tags. Zin et al [23] proposed a tracking system for individual cows using visual ear tag analysis. First, the head and ear tag are detected.…”

Section: Introductionmentioning

confidence: 99%

A Cascaded Model Based on EfficientDet and YOLACT++ for Instance Segmentation of Cow Collar ID Tag in an Image

Zhao

Zhang

2021

Sensors

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In recent years, many imaging systems have been developed to monitor the physiological and behavioral status of dairy cows. However, most of these systems do not have the ability to identify individual cows because the systems need to cooperate with radio frequency identification (RFID) to collect information about individual animals. The distance at which RFID can identify a target is limited, and matching the identified targets in a scenario of multitarget images is difficult. To solve the above problems, we constructed a cascaded method based on cascaded deep learning models, to detect and segment a cow collar ID tag in an image. First, EfficientDet-D4 was used to detect the ID tag area of the image, and then, YOLACT++ was used to segment the area of the tag to realize the accurate segmentation of the ID tag when the collar area accounts for a small proportion of the image. In total, 938 and 406 images of cows with collar ID tags, which were collected at Coldstream Research Dairy Farm, University of Kentucky, USA, in August 2016, were used to train and test the two models, respectively. The results showed that the average precision of the EfficientDet-D4 model reached 96.5% when the intersection over union (IoU) was set to 0.5, and the average precision of the YOLACT++ model reached 100% when the IoU was set to 0.75. The overall accuracy of the cascaded model was 96.5%, and the processing time of a single frame image was 1.92 s. The performance of the cascaded model proposed in this paper is better than that of the common instance segmentation models, and it is robust to changes in brightness, deformation, and interference around the tag.

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Automatic Cow Location Tracking System Using Ear Tag Visual Analysis

Cited by 28 publications

References 15 publications

Handwritten Character Recognition on Android for Basic Education Using Convolutional Neural Network

Handwritten Character Recognition on Android for Basic Education Using Convolutional Neural Network

Continuous real-time cow identification by reading ear tags from live-stream video

A Cascaded Model Based on EfficientDet and YOLACT++ for Instance Segmentation of Cow Collar ID Tag in an Image

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