Presentations -
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A Study on Automatic Individual Identification of Wild Horses International conference
K. Shiiya, R. Yamada, Thi Thi Zin, I. Kobayashi
The 2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech 2022) (Osaka, Japan) 2022.3.8 IEEE Life Sciences Technical Community
Event date： 2022.3.7 - 2022.3.9
Language：English Presentation type：Oral presentation (general)
Venue：Osaka, Japan Country：Japan
Wild horses called "Misaki-uma" are inhabited Cape Toi in the southern part of Miyazaki Prefecture in Japan. Although wild horse, it needs health care. It is a difficult task for aging management association members to monitor the vast habitat range of horses and to identify individual during routine management. In addition, the current methods of individual identification because of contact with horses or to requiring specialized knowledge, ordinary people cannot perform it. In this paper we propose a method for automatic individual identification of wild horses without contact using an RGB camera.
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A Deep Learning-based solution to Cattle Region Extraction for Lameness Detection International conference
Su Myat Noe, Thi Thi Zin, Pyke Tin, I. Kobayashi
The 2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech 2022) (Osaka, Japan) 2022.3.8 IEEE Life Sciences Technical Community
Event date： 2022.3.7 - 2022.3.9
Language：English Presentation type：Oral presentation (general)
Venue：Osaka, Japan Country：Japan
In precision livestock farming, lameness detection in cattle is particularly important for breeding management. The accurate detection of lameness is crucial for delivering effective and economical treatment and for preventing future diseases. The noticeable sign of lameness is that their speed of walking, arching their backs and drop their heads during walking. Here, we emphasis on lameness of dairy cattle by implementing the intelligent visual perception system on the laneways after milking process. Employing a deep learning technique of Mask-RCNN for cattle region detection and identification. The novelty of this work noticeably implies that deep learning instance segmentation could be effectively employed as a cattle region extraction from complex background prior to using identification and tracking.
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An Intelligent Method for Detecting Lameness in Modern Dairy Industry International conference
Thi Thi Zin, Moe Zet Pwint, Su Myat Noe, I. Kobayashi
The 2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech 2022) (Osaka, Japan) 2022.3.8 IEEE Life Sciences Technical Community
Event date： 2022.3.7 - 2022.3.9
Language：English Presentation type：Oral presentation (general)
Venue：Osaka, Japan Country：Japan
Lameness is one of the major welfare concerns in the modern dairy industry. In addition, lameness makes severe health and economic problems causing losses in milk production. Although there has a sizable amount of methods, it remains some worthwhile open problems. Therefore, in this paper, we propose an intelligent method for detecting the lameness of dairy cow by establishing a visual monitoring system on the laneways after milking process. We employ a technique of Mask-RCNN for cow region extraction and utilize features based on head bob patterns. Our real-life experimental results show that the proposed method has detection accuracy of 95.5% on cow’s region extraction and can classify 80% of the lameness levels correctly.
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Individual Identification of Cow Using Image Processing Techniques International conference
Y. Kawagoe, Thi Thi Zin, I. Kobayashi
The 2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech 2022) (Osaka, Japan) 2022.3.8 IEEE Life Sciences Technical Community
Event date： 2022.3.7 - 2022.3.9
Language：English Presentation type：Oral presentation (general)
Venue：Osaka, Japan Country：Japan
Cow identification has become important in recent years due to outbreaks of diseases such as bovine spongiform encephalopathy. Conventional identification methods available are not efficient, affordable, non-invasive, and cost- effective. Among them, some methods are based on biological markers, such as muzzle point matching and facial recognition. Facial images are the most common biometric characteristics used by humans to identify individuals, and they have received much attention. In this study, we used RGB camera to identify individual cow by their faces and confirmed the effectiveness of this method.
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高度な画像処理技術やAI技術を活用した研究開発 Invited
Thi Thi Zin
ICT研究開発支援セミナーin九州 (Zoom及びYouTubeによるオンライン配信) 2022.2.4 九州総合通信局
Event date： 2022.2.4
Language：Japanese Presentation type：Public lecture, seminar, tutorial, course, or other speech
Venue：Zoom及びYouTubeによるオンライン配信 Country：Japan
総務省の戦略的情報通信研究開発推進事業(SCOPE)「地域ICT振興型研究開発」採択課題である「ICTを活用した牛のモニタリングシステムの開発」を中心に、「自立生活を支援するための高齢者24時間見守りシステムの開発」等の高度な画像処理技術やAI技術を活用した研究について紹介。
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A Simple Random Walk Model for Dairy Cow Calving Time Prediction International conference
Thi Thi Zin, Pyke Tin, Pann Thinzar Seint, K. Sumi, I. Kobayashi, Y. Horii
2021 IEEE 10th Global Conference on Consumer Electronics (GCCE) (Kyoto, Japan) 2021.10.14 IEEE Consumer Technology Society
Event date： 2021.10.12 - 2021.10.15
Language：English Presentation type：Oral presentation (general)
Venue：Kyoto, Japan Country：Japan
In this paper, we propose a simple random walk model to predict time of calving event occurs for a pregnant dairy cow. Dairy farmers and experts have well recognized that an accurate calving time prediction is quite important in modern smart dairy farming. To meet these demands, we consider the number of posture changes of a pregnant cow during a few days before the expected dates as a random walk to predict the time at which the calving event occurs. For validation, we show some experimental results by using real life data collected from a large dairy farm in Japan.
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Evaluation of the severity of tremor based on each signal acquired from the displacement of the hand movements International conference
T. Hayashida, Thi Thi Zin, K. Sakai, H. Mochizuki
2021 IEEE 10th Global Conference on Consumer Electronics (GCCE) (Kyoto, Japan) 2021.10.14 IEEE Consumer Technology Society
Event date： 2021.10.12 - 2021.10.15
Language：English Presentation type：Oral presentation (general)
Venue：Kyoto, Japan Country：Japan
Discrepancy of exam findings at the same patient make it difficult to ascertain chronological change in the disease and the efficacy of the medicine. Quantitative evaluation of severity is important for improving the discrepancy. In this study, we examine the efficacy of quantitative evaluation of tremor when using single camera. Recording the hand movements of tremor with single camera, and the displacement, velocity, and acceleration signals are acquired using the hand shift between two adjacent video frames. Quantitative evaluation of tremor is performed based on features obtained from each signal. According to the validation results, our method using single camera is possible to classify with an accuracy of up to 82.6%.
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Cattle Region Extraction using Image Processing Technology International conference
Y. Motomura, Thi Thi Zin, Y. Horii
2021 IEEE 10th Global Conference on Consumer Electronics (GCCE) (Kyoto, Japan) 2021.10.14 IEEE Consumer Technology Society
Event date： 2021.10.12 - 2021.10.15
Language：English Presentation type：Oral presentation (general)
Venue：Kyoto, Japan Country：Japan
In recent years, the number of dairy and beef cattle farms has been decreasing, while the number of cattle and the number of cattle per farm have been increasing, so systems for automatically monitoring cattle have been actively introduced. However, most of them are contact type, which causes physical or mental stress to the cows and is costly when the equipment is damaged. Therefore, in this research, we proposed a method for extracting the approximate shape of cattle using a non-contact 360-degree camera to reduce the burden on livestock farmers and cattle, and confirmed its effectiveness through experiments.
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Application of Methods in Sequential Analysis to Dairy Cow Calving Events International conference
Thi Thi Zin, K. Sumi, Pann Thinzar Seint, Pyke Tin, I. Kobayashi, Y. Horii
15th International Conference on Innovative Computing, Information and Control (ICICIC2021) (Online) 2021.9.15 ICIC International
Event date： 2021.9.15 - 2021.9.16
Language：English Presentation type：Oral presentation (general)
Venue：Online
Apart from sequential sampling, methods in the sequential analysis have been widely and successfully used for various applications such as insurance problems, theory of storage, queuing theory, and many other stochastic models. Moreover, it is well recognized that Wald’s Fundamental Identity in sequential analysis can be used to derive approximate and some exact results in most situations wherein we have essentially a random sequence phenomenon. In this aspect, the fluctuations in motion of a pregnant cow around the calving event fall into a random sequence category. Therefore, in this paper, we explore and examine an application of Wald’s Fundamental Identity in sequential analysis to dairy cow calving time prediction models. Specifically, we will show this Fundamental Identity which can be used to derive results for predicted calving times at which an individual cow calving event occurs in a video-monitored maternity barn. The paper is pure of an expository nature and considers only simple illustrations and some real-life video data are used to confirm the proposed method.
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Cattle Region Extraction Using Color Space Conversion International conference
Y. Hashimoto, H. Hama, Thi Thi Zin
15th International Conference on Innovative Computing, Information and Control (ICICIC2021) (Online) 2021.9.15 ICIC International
Event date： 2021.9.15 - 2021.9.16
Language：English Presentation type：Oral presentation (general)
Venue：Online
The Japanese livestock industry has problems such as difficulty of finding successors of farms, and the aging farmers. Therefore, development of a cattle monitoring system through non-contact and non-invasive methods to improve productivity and reduce labor burdens is a strong desire from farmers. As one of elemental technologies to realize it, we have focused on tracking of cattle for detecting characteristic behaviors using video camera. In this paper, we present an effective extracting method of the cattle region from video images of pasture. Inter-frame difference and background subtraction are widely used for detecting moving objects in video images; however, in our case the system is supposed to be used in dusty pasture, and they do not work well. Because Japanese black cattle move slowly in general, and the skin color is similar to soil, region extraction of them is very difficult, even if these conventional methods are used. Here, region extraction using Color Space Conversion is adopted. This method enables us to manipulate easily the image’s color and automatically extract cattle region; additionally, the movement of cattle is estimated from the change of the gravity center of the extracted region. To verify the effectiveness, we carried out an experiment using the videos taken at Sumiyoshi Livestock Science Station at University of Miyazaki. The experimental results show that this method is well suited for extracting cattle region. Further verification should be conducted to enhance robustness.
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Cow Region Segmentation in Cattle Farm by Using Semantic Segmentation Networks International conference
Swe Zar Maw, Thi Thi Zin, Pyke Tin
15th International Conference on Innovative Computing, Information and Control (ICICIC2021) (Online) 2021.9.15 ICIC International
Event date： 2021.9.15 - 2021.9.16
Language：English Presentation type：Oral presentation (general)
Venue：Online
When it comes to controlling a cattle farm, being able to accurately forecast when calving will happen can be quite beneficial because it allows employees to assess whether or not assistance is required. If such help is not provided when it is required, the calving process may be prolonged, severely impacting both the mother cow and the calf’s health. Multiple diseases may result from such a delay. During the production cycle, one of the most crucial events for cows is calving. An accurate video-monitoring technique for cows can spot abnormalities or health issues early, allowing for prompt and effective human interference. To make this surveillance automated, a crucial task is to detect the cows. For this purpose, in this research, we have proposed an effective semantic segmentation network for segmenting the cow from the 360-degree surveillance camera. The proposed network is a modified version of the U-Net architecture. An additional module is added in the U-Net architecture which is named as convolutional long short-term memory (ConvLSTM) block. The ConvLSTM block allows for effective feature sharing between the less dense layers and denser layers. Experiments with our suggested method were carried out at a big dairy farm in Japan’s Oita Prefecture. The suggested method’s experimental findings demonstrate that it holds promise in real-world applications.
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A Study on Working Group Detection after Helmet Extraction International conference
S. Inoue, I. Hidaka, Thi Thi Zin
15th International Conference on Innovative Computing, Information and Control (ICICIC2021) (Online) 2021.9.15 ICIC International
Event date： 2021.9.15 - 2021.9.16
Language：English Presentation type：Oral presentation (general)
Venue：Online
Due to the declining birthrate and increasing in aging population, the industries are facing seriously with manpower shortage problems. As a consequence, many small and medium-sized factories are introducing AI and IOT to automate their operations so that they can cope with fewer employees. However, some factories are not automating because it is more economically effective to have employees perform the work directly than to automate it. Such companies can create more economic benefits with less labor by eliminating waste from their current work. In this research, we focus on the movement of people during work by using information obtained from a 4K camera installed overhead that can detect work groups and acquire data for use in creating indicators for efficiency improvement. For this purpose, we attach a marker on the top of the worker’s helmet to detect the helmet and identify the work group. The effectiveness of the proposed method has been confirmed through simulation experiments.
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Holistically-Nested Deep Learning Model for Cow Region Detection and Motion Classification International conference
Thi Thi Zin, Saw Zay Maung Maung and Pyke Tin
15th International Conference on Innovative Computing, Information and Control (ICICIC2021) (Online) 2021.9.15 ICIC International
Event date： 2021.9.15 - 2021.9.16
Language：English Presentation type：Oral presentation (general)
Venue：Online
In dairy farming, monitoring an individual cow is a critical component for each and every aspect of precision dairy farm management system since it can make farmers and managers learn cow’s health conditions, body conditions even the occurrences of calving difficulties in times. Such monitoring is often performed visually because animal appearance and behavior are key indicators of analyzing animal conditions. According to the latest computer vision and image processing algorithms, it is now possible to implement a monitoring system that can detect the status of cows at a low cost. In this aspect, one of the important steps is to detect and segment the cows within the video sequences. Moreover, how an individual cow behaves or makes movements such as lying, standing, and changes from one posture to another is also equally important for further analysis. Therefore, in this paper, we propose a deep learning method of edge-based cow region detection and multiple linear models for the classification of cow movements. Specifically, we establish a deep learning model of holistically-nested edge detection (HED) that performs image-to-image prediction by using fully convolutional neural networks and deeply supervised nets. In the cow motion classification process, we propose multiple linear models in which the coefficients of independent variables are utilized as features for classification. According to our experimental results, the proposed detection system is promising and provides robust performance. Similar experiments are also performed to validate the proposed multiple linear models for the classification of cow motions with high accuracy.
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Automatic Detection of Mounting Behavior in Cattle using Semantic Segmentation and Classification International conference
Su Myat Noe, Thi Thi Zin, Ikuo Kobayashi, Pyke Tin
The 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech 2021) (Nara Royal Hotel (Nara, Japan)) IEEE Life Sciences Technical Community
Event date： 2021.3.9 - 2021.3.11
Language：English Presentation type：Oral presentation (general)
Venue：Nara Royal Hotel (Nara, Japan)
In cattle farming sector, the accurate detection of estrus plays a vital role because incorrect timing for artificial insemination affects the cattle business. The noticeable sign of estrus is the standing heat, where the cattle standing to be mounted by other cows for a couple of seconds. In this paper, we proposed cattle region detection using deep learning semantic segmentation model and automatic detection of mounting behavior with machine learning classification methods. Based on the conducted experiment, the results show that a mean Intersection of Union (IoU) of 98% on the validation set. The pixel-wise accuracy for two classes (cattle and background) was found to be both 98%, respectively. For the classification, the proposed method compares the four supervised machine learning methods which can detect with the accuracy rate of Support Vector Machine, Naïve Bayes, Logistic Regression and Linear Regression are 87%, 96%, 90%, and 80% respectively. Among them, Naïve Bayes algorithm perform the best. The novelty of this work noticeably implies that deep learning semantic segmentation could be effectively employed as a pre-processing step in segmenting the cattle and background prior to using various classification models.
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Petrochemical Characteristics of the Granitoid Rocks of Northern Myanmar International conference
Htin Lynn Aung, Thaire Phyu Win, Thi Thi Zin
The 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech 2021) (Nara Royal Hotel (Nara, Japan)) IEEE Life Sciences Technical Community
Event date： 2021.3.9 - 2021.3.11
Language：English Presentation type：Oral presentation (general)
Venue：Nara Royal Hotel (Nara, Japan)
The research area is located on the Mogaung - Kamaing-Hpakant road in Hpakant Township, Kachin State, northern Myanmar. The dominant lithologic units comprise igneous and metamorphic rocks. The present work is mainly intended to establish the petrogenesis of the igneous rocks based on the petrochemical analysis results. The igneous rocks are mainly microgranite and serpentinite. Major element analysis of some rocks was determined by XRF spectrometer and interpreted the genesis of these rock units. On the basis of the petrochemical characteristics, the microgranite of the study area is I-type peraluminous granitoid formed by partial melting of mantle and / or lower crust in the extensional tectonics.
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Markov Chain Monte Carlo Method for the Modeling of Posture Changes Prior to Calving International conference
Thi Thi Zin, Pyke Tin, Pann Thinzar Seint, Yoichiro Horii
The 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech 2021) (Nara Royal Hotel (Nara, Japan)) IEEE Life Sciences Technical Community
Event date： 2021.3.9 - 2021.3.11
Language：English Presentation type：Oral presentation (general)
Venue：Nara Royal Hotel (Nara, Japan)
An accurate and careful analysis of posture changes for a dairy cow prior to calving plays an important role in making calving time prediction. The patterns of activities such as frequent changes in postures of a pregnant cows during the time closer to calving are utilized as indicators to predict the time of calving. In this paper, we introduce Markov Chain Monte Carlo (MCMC) method to generate the patterns of four states activities such lying, transitions from lying to standing, standing itself and transitions from standing to lying based on the monitored cow activity changes data three days prior to calving. The validity of the generated cow activities in posture changes data is compared with the actual collected data in terms of Euclidean and Cosine distance measures. The experimental results show that the method in this paper can be used as a generalized method to generate synthetic data series of dairy cow activities prior to calving.
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Image Technology Based Detection of Infected Shrimp in Adverse Environments International conference
Thi Thi Zin, Takehiro Morimoto, Naraid Suanyuk, Toshiaki Itami, Chutima Tantikitti
The 1st International Conference on Sustainable Agriculture and Aquaculture: For Well Being and Food Security (Prince of Songkla University) www.psu.ac.th, www.kku.ac.th, www.ku.ac.th, www.cmu.ac.th,
Event date： 2021.1.11 - 2021.1.12
Language：English Presentation type：Oral presentation (general)
Venue：Prince of Songkla University
In recent years, the cultivation of white leg shrimp (Litopenaeus vannamei) has become popular in countries around Japan, especially in Southeast Asia, and at the same time, various diseases have occurred in the farms [1]. In the early stages of infection, shrimp show three abnormal behaviors: (1) they appear in the shallow waters of the farm, (2) they do not move and do not eat even when fed, and (3) they suddenly start moving. Early detection is important step to control this disease because there are no preventive measures. In addition, we are currently visually confirming shrimp that show characteristic of the disease. However, these lead to a burden on the farmers and delay in discovery [2]. Therefore, we propose an image technology based monitoring system for detecting shrimp showing the characteristics of diseases.
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Consumer Behavior Analyzer in Internet of Things (IoT) Environments International conference
Swe Nwe Nwe Htun, Thi Thi Zin and Pyke Tin
4th International Symposium on Information and Knowledge Management (ISIKM2020) (Online Conference) ICIC International
Event date： 2020.12.12 - 2020.12.13
Language：English Presentation type：Oral presentation (general)
Venue：Online Conference
This paper proposes an analyzer of consumer behavior in Internet of Things (IoT) environments. This analyzer is most useful in predicting the intentions of users during searches, and especially during image searches. Since most technologies are connected on the internet, search results can be characterized using image-similarity measures. In this paper, information on image similarities is extracted using a Convolutional Neural Network (CNN) in IoT environments. In this proposed consumer behavior analyzer, the similarity measures characterizing the relationships between images are transformed into Markov Chain transition probabilities, and their stationary probabilities are then analyzed to describe the priority order for search results conforming with consumer intentions. In order to confirm the validity of the proposed method, the Yelp public dataset was used. The outcomes using this analyzer are promising, and this analyzer might be instrumental in making further improvements in practical applications of consumer technologies.
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Systematic Inclusion Study on Some Rare Gemstones of the Mogok Area, Mandalay Region, Myanmar International conference
Htin Lynn Aung, Thaire Phyu Win and Thi Thi Zin
4th International Symposium on Information and Knowledge Management (ISIKM2020) (Online Conference) ICIC International
Event date： 2020.12.12 - 2020.12.13
Language：English Presentation type：Oral presentation (general)
Venue：Online Conference
In this paper we shall explore and examine an inclusion aspect of some rare gemstones of Mogok area which is known as the Ruby Land of Myanmar where 90% of world rubies come from. The materials presented in this paper are the products of our research team investigated some rock sequences in the Mogok area situated about 280 miles north of the capital Naypyidaw, has unearthed some of the rarest and most luxurious rubies including the legendary 82-carat Nga Mauk ruby discovered centuries ago.. The rock sequence of the study area consists of medium to high grade metamorphic rocks, marble, gneiss and intrusive igneous rocks, mainly Kabaing granite, leucogranite and syenite. It is famous for presence of ruby and sapphire. Exceptionally some rare gemstones are also discovered. The present work is mainly intended to describe systematically the inclusions of some rare gemstones from the Mogok area. Liquid feather inclusions present in jeremejevite. Two-phase inclusions occur in morganite and petalite. In petalite, tube-like inclusions also present. Opaque inclusion and solid inclusion occur in rutile and treacle granular inclusion and finger print inclusion observe in sinhalite.
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Smart Irrigation: An Intelligent System for Growing Strawberry Plants in Different Seasons of the Year International conference
Ye Htet, Htin Kyaw Oo and Thi Thi Zin
4th International Symposium on Information and Knowledge Management (ISIKM2020) (Online Conference) ICIC International
Event date： 2020.12.12 - 2020.12.13
Language：English Presentation type：Oral presentation (general)
Venue：Online Conference
Agriculture is an important source of livelihood and varying the way of cultivating plants could provide more productivity and sustainability of foods than before and thus smart irrigation would be one of the best solutions. Therefore, the proposed system mainly focused on the strawberry plants to grow within small-scale farm using intelligent systems to bear and produce fruits in all seasons of the country. A challenging problem which arises for this objective is the precise temperature, water and fertilizer management for plants. So, this system emphasized on automatic environmental adjustment system integrated with sensors to control temperature and also drip irrigation system for efficient water and fertilizers usage. Moreover, leaf analysis using computer vision which is controlled by Raspberry Pi is implemented for detection of the nutrient deficiency symptoms of plants. As for the communication unit to inform the users via sensors and image processing, Internet of Things is adopted.