THI THI ZIN

写真a

Affiliation

Engineering educational research section Information and Communication Technology Program

Title

Professor

External Link

Degree 【 display / non-display

  • Doctor of Engineering ( 2007.3   Osaka City University )

  • Master of Engineering ( 2004.3   Osaka City University )

  • Master of Information Science ( 1999.5   University of Computer Studies, Yangon (UCSY) )

  • B.Sc (Hons) (Mathematics) ( 1995.5   University of Yangon (UY) )

Research Areas 【 display / non-display

  • Informatics / Perceptual information processing  / Image Processing

  • Informatics / Database

  • Life Science / Animal production science

 

Papers 【 display / non-display

  • HMM-Based Action Recognition System for Elderly Healthcare by Colorizing Depth Map Reviewed

    Ye Htet, Thi Thi Zin, Pyke Tin, H. Tamura, K. Kondo, E. Chosa

    International Journal of Environmental Research and Public Health   19 ( 19 )   2022.10

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:International Journal of Environmental Research and Public Health  

    Addressing the problems facing the elderly, whether living independently or in managed care facilities, is considered one of the most important applications for action recognition research. However, existing systems are not ready for automation, or for effective use in continuous operation. Therefore, we have developed theoretical and practical foundations for a new real-time action recognition system. This system is based on Hidden Markov Model (HMM) along with colorizing depth maps. The use of depth cameras provides privacy protection. Colorizing depth images in the hue color space enables compressing and visualizing depth data, and detecting persons. The specific detector used for person detection is You Look Only Once (YOLOv5). Appearance and motion features are extracted from depth map sequences and are represented with a Histogram of Oriented Gradients (HOG). These HOG feature vectors are transformed as the observation sequences and then fed into the HMM. Finally, the Viterbi Algorithm is applied to recognize the sequential actions. This system has been tested on real-world data featuring three participants in a care center. We tried out three combinations of HMM with classification algorithms and found that a fusion with Support Vector Machine (SVM) had the best average results, achieving an accuracy rate (84.04%).

    DOI: 10.3390/ijerph191912055

    Scopus

  • An Intelligent Method for Detecting Lameness in Modern Dairy Industry Reviewed

    Thi Thi Zin, Moe Zet Pwint, Su Myat Noe, I. Kobayashi

    2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech)   564 - 565   2022.3

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (international conference proceedings)   Publisher:LifeTech 2022 - 2022 IEEE 4th Global Conference on Life Sciences and Technologies  

    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.

    DOI: 10.1109/LifeTech53646.2022.9754941

    Scopus

  • Real-time action recognition system for elderly people using stereo depth camera Reviewed

    Thi Thi Zin, Ye Htet, Akagi Y., Tamura H., Kondo K., Araki S., Chosa E.

    Sensors   21 ( 17 )   2021.9

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Sensors  

    Smart technologies are necessary for ambient assisted living (AAL) to help family mem-bers, caregivers, and health-care professionals in providing care for elderly people independently. Among these technologies, the current work is proposed as a computer vision-based solution that can monitor the elderly by recognizing actions using a stereo depth camera. In this work, we intro-duce a system that fuses together feature extraction methods from previous works in a novel combination of action recognition. Using depth frame sequences provided by the depth camera, the system localizes people by extracting different regions of interest (ROI) from UV-disparity maps. As for feature vectors, the spatial-temporal features of two action representation maps (depth motion appearance (DMA) and depth motion history (DMH) with a histogram of oriented gradients (HOG) descriptor) are used in combination with the distance-based features, and fused together with the automatic rounding method for action recognition of continuous long frame sequences. The experimental results are tested using random frame sequences from a dataset that was collected at an elder care center, demonstrating that the proposed system can detect various actions in real-time with reasonable recognition rates, regardless of the length of the image sequences.

    DOI: 10.3390/s21175895

    Scopus

  • Activity-integrated hidden markov model to predict calving time Reviewed

    K. Sumi, Swe Zar Maw, Thi Thi Zin, Pyke Tin, I. Kobayashi, Y. Horii

    Animals   11 ( 2 )   1 - 12   2021.2

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Animals  

    Accurately predicting when calving will occur can provide great value in managing a dairy farm since it provides personnel with the ability to determine whether assistance is necessary. Not providing such assistance when necessary could prolong the calving process, negatively affecting the health of both mother cow and calf. Such prolongation could lead to multiple illnesses. Calving is one of the most critical situations for cows during the production cycle. A precise video-monitoring system for cows can provide early detection of difficulties or health problems, and facilitates timely and appropriate human intervention. In this paper, we propose an integrated approach for predicting when calving will occur by combining behavioral activities extracted from recorded video sequences with a Hidden Markov Model. Specifically, two sub-systems comprise our proposed system: (i) Behaviors extraction such as lying, standing, number of changing positions between lying down and standing up, and other significant activities, such as holding up the tail, and turning the head to the side; and, (ii) using an integrated Hidden Markov Model to predict when calving will occur. The experiments using our proposed system were conducted at a large dairy farm in Oita Prefecture in Japan. Experimental results show that the proposed method has promise in practical applications. In particular, we found that the high frequency of posture changes has played a central role in accurately predicting the time of calving.

    DOI: 10.3390/ani11020385

    Scopus

  • Imaging tremor quantification for neurological disease diagnosis Reviewed

    Y. Mitsui, Thi Thi Zin, N. Ishii, H. Mochizuki

    Sensors (Switzerland)   20 ( 22 )   1 - 14   2020.11

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    Authorship:Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Sensors (Switzerland)  

    In this paper, we introduce a simple method based on image analysis and deep learning that can be used in the objective assessment and measurement of tremors. A tremor is a neurological disorder that causes involuntary and rhythmic movements in a human body part or parts. There are many types of tremors, depending on their amplitude and frequency type. Appropriate treatment is only possible when there is an accurate diagnosis. Thus, a need exists for a technique to analyze tremors. In this paper, we propose a hybrid approach using imaging technology and machine learning techniques for quantification and extraction of the parameters associated with tremors. These extracted parameters are used to classify the tremor for subsequent identification of the disease. In particular, we focus on essential tremor and cerebellar disorders by monitoring the finger–nose–finger test. First of all, test results obtained from both patients and healthy individuals are analyzed using image processing techniques. Next, data were grouped in order to determine classes of typical responses. A machine learning method using a support vector machine is used to perform an unsupervised clustering. Experimental results showed the highest internal evaluation for distribution into three clusters, which could be used to differentiate the responses of healthy subjects, patients with essential tremor and patients with cerebellar disorders.

    DOI: 10.3390/s20226684

    Scopus

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Books 【 display / non-display

  • Big Data Analysis and Deep Learning Applications: Proceedings of the First International Conference on Big Data Analysis and Deep Learning (Advances in Intelligent Systems and Computing Book 744)

    Thi Thi Zin (Editor), Jerry Chun-Wei Lin (Editor) ( Role: Joint editor)

    Springer  2018.6 

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    Total pages:Springer   Language:English

    Other Link: https://www.amazon.com/Data-Analysis-Deep-Learning-Applications-ebook/dp/B07DL46RJX

  • Systems and Computing: A New Look into Web Page Ranking Systems

    Thi Thi Zin, Pyke Tin, H. Hama, T. Toriu( Role: Joint author)

    Springer International Publishing  2014.10 

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    Language:English Book type:Scholarly book

MISC 【 display / non-display

  • Introduction to IEEE LifeTech 2022 Overview Invited International coauthorship

    Thi Thi Zin and Ryota Nishimura

    IEEE LifeTech2022 Abstract Book   2022.3

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    Language:English   Publishing type:Research paper, summary (international conference)   Publisher:IEEE CT Soc  

    DOI: 10.1109/LifeTech53646.2022.9754806

  • 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   2022.2

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    Authorship:Lead author, Corresponding author   Language:Japanese   Publishing type:Lecture material (seminar, tutorial, course, lecture, etc.)   Publisher:戦略的情報通信研究開発推進事業(SCOPE)  

    高齢化、大規模化する現代の畜産で、24時間365日にわたり家畜の健康管理を適切に行い、異常や変化に留意し続けながら経営を継続することは容易でない。本研究開発では、家畜生産性の改善と地域活性化の実現を目的とする牛のモニタリングシステム構築に必要な要素技術の開発を行う。

  • Classification of People’s Emotions during Natural Disasters International coauthorship

    Nann Hwan Khun, Thi Thi Zin, M. Yokota, Hninn Aye Thant

    宮崎大学工学部紀要   ( 49 )   85 - 90   2021.9

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    Language:English   Publishing type:Article, review, commentary, editorial, etc. (bulletin of university, research institution)   Publisher:宮崎大学  

  • Predicting Calving Time of Dairy Cows by Time Series Model

    Tunn Cho Lwin, Thi Thi Zin , M. Yokota

    宮崎大学工学部紀要   ( 50 )   87 - 94   2021.9

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    Language:English   Publishing type:Article, review, commentary, editorial, etc. (bulletin of university, research institution)   Publisher:宮崎大学  

  • Recognition Based Segmentation of Handwritten Alphanumeric Characters Entry on Tablet PC International coauthorship

    Myat Thiri Wai, Thi Thi Zin, M. Yokota, Khin Than Mya

    宮崎大学工学部紀要   ( 49 )   79 - 84   2021.9

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    Language:English   Publishing type:Article, review, commentary, editorial, etc. (bulletin of university, research institution)   Publisher:宮崎大学  

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Presentations 【 display / non-display

  • Gama-Markov Branching Process Approach to the Novel Coronavirus (COVID-19) Pandemic International conference

    Thi Thi Zin, Pyke Tin and H. Hama

    The Fifth International Symposium on Information and Knowledge Management (ISIKM2022)  (onsite (Guangzhou, China / Kumamoto, Japan) and online)  2022.3.26  ICIC International

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    Event date: 2022.3.25 - 2022.3.26

    Language:English   Presentation type:Oral presentation (general)  

    Venue:onsite (Guangzhou, China / Kumamoto, Japan) and online  

    The spreads of the Novel Coronavirus (COVID-19) infectious disease have been increased exponentially in almost all countries around the world. Due to fast in transmission, big in mass and transform in variety, the world health organization has recognized the COVID-19 as a global pandemic. Since then, variety of measures such as mask wearing campaigns, social distance movements, mass vaccination programs and many others were taken globally to reduce the effective reproduction number of an infection below unity. In this paper, we propose a special type of Gama-Markov branching process model to monitor the reproduction mean rate by using data on outbreak size and outbreak duration. We develop a novel method for computing the
    probabilities of the rate of spreads, outbreak sizes and outbreak duration. Some simulations based on the daily data appearing on the Internet are performed. The results show that the proposed model has promising potentials for the real-life applications.

  • Predicting Calving Time of Dairy Cows by Autoregressive Integrated Moving Average (ARIMA) Model and Double Exponential Smoothing (DES) Model International conference

    Tunn Cho Lwin, Thi Thi Zin and Pyke Tin

    The Fifth International Symposium on Information and Knowledge Management (ISIKM2022)  (onsite (Guangzhou, China / Kumamoto, Japan) and online)  2022.3.26  ICIC International

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    Event date: 2022.3.25 - 2022.3.26

    Language:English   Presentation type:Oral presentation (general)  

    Venue:onsite (Guangzhou, China / Kumamoto, Japan) and online  

  • Cattle Face Identification with Ear Tags Using YOLOv5s Model International conference

    Wai Hnin Eaindrar Mg and Thi Thi Zin

    The Fifth International Symposium on Information and Knowledge Management (ISIKM2022)  (onsite (Guangzhou, China / Kumamoto, Japan) and online)  2022.3.26  ICIC International

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    Event date: 2022.3.25 - 2022.3.26

    Language:English   Presentation type:Oral presentation (general)  

    Venue:onsite (Guangzhou, China / Kumamoto, Japan) and online  

  • Black Cow Localization and Tracking with YOLOv5 and Deep SORT International conference

    Cho Cho Aye, Thi Thi Zin, I. Kobayashi

    The Fifth International Symposium on Information and Knowledge Management (ISIKM2022)  (onsite (Guangzhou, China / Kumamoto, Japan) and online)  2022.3.26  ICIC International

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    Event date: 2022.3.25 - 2022.3.26

    Language:English   Presentation type:Oral presentation (general)  

    Venue:onsite (Guangzhou, China / Kumamoto, Japan) and online  

    Nowadays, precision livestock farming becomes popular fields in agriculture to get a high-quality product. Lacking monitoring livestock overtime can lead them to unexpectedly suffer from serious illness or even life-threatening. Therefore, individual livestock tracking systems can help to understand their actions. The proposed system is emphasized tracking individual black cows after performing cow detection. In this work, YOLOv5 (You Only Look Once) is used for detection and the Deep SORT (Simple Online Real-time Tracking) algorithm is utilized for tracking. In order to enhance the accuracy of the models, transfer learning is adopted. In the detection phase, the YOLOv5 model and appear feature model in Deep SORT were trained with a manually annotated dataset with help of transfer learning. The experimental results reveal that YOLOv5 got detection accuracy of 0.995 mAP@.5 and tracking with Deep SORT achieves the tracking accuracy of 99.4% in testing video-1 and 98.9% in testing video-2.

  • 工場での作業の見える化 - 作業員のグループ識別及び追跡 - Invited

    Thi Thi Zin

    令和3年度先端技術研究開発促進・人材育成支援事業:IoT等先端技術利活用セミナー  2022.3.17 

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    Event date: 2022.3.17

    Language:Japanese   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

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Awards 【 display / non-display

  • Best Presentation Award

    2022.3   The Fifth International Symposium on Information and Knowledge Management (ISIKM2022)   Black Cow Localization and Tracking with YOLOv5 and Deep SORT

    Cho Cho Aye, Thi Thi Zin, I. Kobayashi

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    Award type:Award from international society, conference, symposium, etc.  Country:Japan

  • IEEE LifeTech 2022 WIE Excellent Paper Award

    2022.3   IEEE 4th Global Conference on Life Sciences and Technologies(LifeTech2022)   A Hybrid Approach: Image Processing Techniques and Deep Learning Method for Cow Detection and Tracking System

    Cho Cho Mar, Thi Thi Zin, I. Kobayashi, Y. Horii

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    Award type:Award from international society, conference, symposium, etc.  Country:Japan

    Cow detection and tracking system plays an important role in cattle farming and diary community to reduce expenses and workload. This research presents how the conventional image processing techniques can be combined with deep learning concepts to establish cow detection and tracking system. Specifically, we first employ a Hybrid Task Cascade (HTC) instance segmentation network for cow detection. We then built the multiple objects tracking (MOT) algorithm utilizing location and appearance cues (color and CNN features) to carry out cow tracking process. To leverage the robustness of the system, we also considered the recent features from the previous tracked cow.

  • IEEE GCCE2021 Excellent Paper Award Gold Prize

    2021.10  

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    Award type:Award from international society, conference, symposium, etc.  Country:Japan

  • Best Presentation Award

    2021.9   15th International Conference on Innovative Computing, Information and Control (ICICIC2021)   Application of Methods in Sequential Analysis to Dairy Cow Calving Events

    Thi Thi Zin, K. Sumi, Pann Thinzar Seint, Pyke Tin, I. Kobayashi

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    Award type:Award from international society, conference, symposium, etc.  Country:Japan

  • IEEE LifeTech 2021 WIE Paper Award

    2021.3   IEEE 3rd Global Conference on Life Sciences and Technologies(LifeTech2021)   Automatic Detection of Mounting Behavior in Cattle using Semantic Segmentation and Classification

    Su Myat Noe, Thi Thi Zin, Ikuo Kobayashi, Pyke Tin

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    Award type:Award from international society, conference, symposium, etc.  Country:Japan

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Grant-in-Aid for Scientific Research 【 display / non-display

  • 画像処理技術と非接触センサを用いた牛の発情検知及び分娩監視システムの開発

    2017.04 - 2021.03

    科学研究費補助金  基盤研究(C)

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    Authorship:Principal investigator 

     畜産は全国農業総生産額の3 割以上を占める重要な産業であるが、不適切な家畜管理による生産性の低下が大きな問題となっている。その主たる原因は飼養形態の変化による1 頭あたり観察時間の短縮であり、飼養頭数の多頭化・農家の高齢化が進む畜産現場において、365 日24 時間にわたり家畜の異常や変化を観察し続けることは困難である。
     申請者らは、主に非接触・非侵襲センサ情報のアルゴリズム解析技術に着目し、距離画像とビデオ画像を用いて牛の発情を検知できる独自アルゴリズムの開発に取り組んできた。本研究では、これらの技術を応用することで、牛の発情や分娩監視時の異常を自動検知できる省力的な24 時間
    家畜管理システムを開発する。

  • 牛の分娩監視システムに関する研究

    2018.04 - 2020.03

    科学研究費補助金  特別研究員奨励費

    須見 公祐、Thi Thi Zin(受入研究者)

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    Authorship:Coinvestigator(s) 

    精度や耐久性が不十分な割に高価なウェラブル型センサの装着や、肉体的・精神的に大きな負担を強いられる目視によるカメラ映像のモニタリング等は、大規模化する畜産現場において現実的なコストで利用できるものが極めて少ない。そこで本研究では、監視カメラから得られる映像を用いて非接触型の分娩管理システムを開発することで、農家そして牛、両方の負担を減らすことを目的とする。
    本来、牛は牛群と呼ばれるグループで行動を行う。そして、分娩が間近になると分娩室という分娩専用の牛舎に移される。分娩室には2 頭以上を同時に入れるケースも多く、どの牛で分娩が始まったかを識別する必要があることから、個体識別と追跡処理が必要となる。次に、分娩行動の段階を追って検知を行う。抽出する特徴としては、尻尾が上がっているかどうか、牛が立っているか座っているか、落ち着きがなくなり移動量が増加するか、子牛を出産したかどうか、親牛が子牛を舐めているかどうかなど、それぞれの過程で自動的に異常を見つけ通報を行うアルゴリズムの開発を進める。分娩行動が起きたかどうかの判断は、これらのデータから各特徴の重要度(重み)を学習させることによって行う。そして、最終目標として難産など異常行動の検知を行うために事例を蓄積しながら知識ベースを充実させ、異常事態の検知を行い、分娩の各段階を監視して異常事態の検知ならびに通報が可能なシステムの開発を目指す。

  • Study of interactive teaching systems using an image processing techniques

    2015.04 - 2018.03

    Grant-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research(C)

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    Authorship:Coinvestigator(s) 

  • Development of forensic imaging modality for person identification using integration method of feature correspondences between heterogeneous images

    2015.04 - 2018.03

    Grant-in-Aid for Scientific Research  Grant-in-Aid for challenging Exploratory Research

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    Authorship:Coinvestigator(s) 

  • Development of automatic estrus detection systems of cattle using complementary the plane distance image and video footage

    2015.04 - 2017.03

    Grant-in-Aid for Scientific Research  Grant-in-Aid for challenging Exploratory Research

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    Authorship:Principal investigator 

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Other research activities 【 display / non-display

  • The Fifth International Symposium on Information and Knowledge Management (ISIKM2022)

    2021.06 - 2022.03

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    Session Chair
    Artificial Intelligence (AI) and Imaging on Information and Knowledge Management

  • The Fifth International Symposium on Information and Knowledge Management (ISIKM2022)

    2021.06 - 2022.03

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    Publicity Chairs of ISIKM2022

    http://www.icicconference.org/isikm2022/

  • The 15th International Conference on Innovative Computing, Information and Control (ICICIC2021)

    2021.04 - 2022.03

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    Publicity Chairs of ICICIC2021

    The 15th International Conference on Innovative Computing, Information and Control (ICICIC2021)

    http://www.icicconference.org/icicic2021/

  • The 15th International Conference on Innovative Computing, Information and Control (ICICIC2021)

    2021.04 - 2022.03

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    Organized Session Chair of ICICIC2021

    AI, Image Technologies & Data Science for Multi- disciplinary Applications

    http://www.icicconference.org/icicic2021/

  • Information Processing in Agriculture

    2021.04 - 2022.03

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    Reviewer

    Information Processing in Agriculture

    https://www.journals.elsevier.com/information-processing-in-agriculture

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