|
Affiliation |
Engineering educational research section Mechanical and Intelligent Engineering Program |
|
Title |
Assistant Professor |
|
Contact information |
|
|
Related SDGs |
|
KHIN DAGON WIN
|
|
|
Degree 【 display / non-display 】
-
Doctor of Philosophy ( 2021.9 University of Miyazaki )
-
Master of Engineering ( 2017.9 University of Miyazaki )
-
Master of Computer Science ( 2018.8 University Of Computer Studies, Mandalay )
-
Bachelor of Computer Science ( 2014.2 University Of Computer Studies, Mandalay )
Research Areas 【 display / non-display 】
-
Informatics / Intelligent informatics
-
Informatics / Robotics and intelligent system
Papers 【 display / non-display 】
-
Kawasue K., Win K.D., Tokunaga T.
Animals 16 ( 3 ) 2026.2
Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Animals
Live weight is widely used as a reference indicator for growth performance and for evaluating the accuracy of weight measurement technologies in pig production. However, live weight is not a fixed physiological quantity, and finishing pigs naturally experience substantial short-term mass fluctuations due to normal behaviors such as drinking, feeding, urination, and defecation. In this study, we integrated published physiological and behavioral parameters into a stochastic simulation model to quantify within-day live-weight dynamics in finishing pigs weighing approximately 100 kg. The simulation was conducted with 1-min temporal resolution over a 24-h period. The model demonstrated that short-term weight fluctuations of approximately ±3–5 kg can occur within a single day, even when measurement error is minimal. Across 1000 simulated pigs, the mean daily fluctuation range was 4.2 kg, confirming that kilogram-scale variation is physiologically expected under normal conditions. These results provide a plausible physiological basis for understanding the frequently reported discrepancies between camera-based weight estimates and instantaneous floor-scale measurements. Camera systems primarily reflect body mass derived from external morphology, whereas floor scales measure instantaneous total mass that includes transient contributions from gastrointestinal contents, ingested water, and retained waste. Consequently, direct comparisons based on instantaneous scale readings can be misleading when used as ground truth. Our findings indicate that commonly cited accuracy claims of ±2–3 kg for camera weighing systems should be interpreted with caution, as normal physiological weight variation often exceeds this range. Recognizing live weight as a dynamic physiological variable is essential for developing biologically meaningful evaluation frameworks and for the appropriate interpretation and comparison of weight measurement technologies in precision livestock farming.
DOI： 10.3390/ani16030498
-
Effect of Head Data Exclusion on Accuracy in Pig Body Weight Estimation Using RGB-D Cameras Reviewed
WIN Khin Dagon, KAWASUE Kikuhito, TOKUNAGA Tadaaki, IKI Yusuke
Journal of the Japanese Society of Agricultural Machinery and Food Engineers 87 ( 4 ) 407 - 409 2025.10
Language：Japanese Publishing type：Research paper (scientific journal) Publisher：Japanese Society of Agricultural Machinery and Food Engineers
-
Khin Dagon Win, Kikuhito Kawasue, Masatoki Kaneko
Sensors 25 ( 6 ) 2025.3
Authorship：Lead author Publishing type：Research paper (scientific journal)
Highlights: What are the main findings? A non-contact method for measuring neonatal head circumference using a 3D imaging sensor was developed. What is the implication of the main finding? A top-view 3D imaging system can be used to estimate the head circumferences of neonates without direct contact, ensuring safety for their delicate skin. Because some portions of the head may be missing in the top-view images, these missing areas are estimated using mat information. The extracted features and head surface values contribute to the head circumference measurement. This approach enhances the feasibility of non-contact head circumference measurement, which could improve neonatal monitoring in clinical settings. In Japan, birth rates are declining, but there are a rising number of underweight newborns who require specialized care in neonatal intensive care units (NICUs). Head circumference is an important indicator of brain development for low-birth-weight infants. However, measuring head circumference requires extreme care because low-birth-weight infants have fragile skin. Therefore, a non-contact measurement system using a 3D imaging sensor was developed. Using this system, three-dimensional data for a newborn’s head can be obtained from outside the incubator. Briefly, the images are taken from above the incubator, so there is an area behind the head that cannot be captured by the camera, but the head circumference estimation takes into account the fact that the head is in contact with the mat. The proposed method allows head circumference estimation without touching the newborn. This approach minimizes stress for both the neonate and the nurse and improves efficiency and safety in the NICU.
DOI： 10.3390/s25061869
-
Robust pig extraction using ground base depth images for automatic weight estimation Reviewed
Khin Dagon Win, Kikuhito Kawasue, Tadaaki Tokunaga
Artificial Life and Robotics 30 ( 1 ) 42 - 50 2025.2
Authorship：Lead author Publishing type：Research paper (scientific journal)
Dark colored pigs (Berkshire, Duroc, etc.) are widely recognized nationwide in Japan for their exceptional taste, with the southern Kyushu region being a renowned production area for these esteemed breeds. However, estimating the weight of these pigs using a camera presents a unique challenge. The key process in a camera-based weight estimation system is the precise extraction of the target pig from the background. Typically, cameras capture images from above, as the top-view images provide the most specific growth indicators. However, the image from above contains a ground image. Since Berkshire and Duroc pigs are black and red, respectively, they blend into the ground, making it difficult to accurately segment the pigs in the images. Thus, it is crucial to perfectly distinguish between the ground and the pigs. Therefore, a new extraction method is proposed to distinguish between the ground and pigs by converting depth data based on the pig's position. To enhance the efficiency of pig farming and alleviate the burden on workers, our goal is to develop a system that automatically measures the weight of Berkshire pigs for shipment without background interference. In this study, we installed the system at a Berkshire pig farm and demonstrated the effectiveness of this innovative extraction method for camera-based weight estimation.
-
Handheld Pig-Weight Estimation Using an RGB-D Sensor Reviewed
Khin Dagon Win, Kikuhito Kawasue, Tadaaki Tokunaga, Yusuke Iki
Lecture Notes in Electrical Engineering 1322 LNEE 95 - 103 2025
Authorship：Lead author Publishing type：Research paper (international conference proceedings)
Although mechanical weighing machines specialized for pigs are normally used to measure animal weights on pig farms, guiding those animals onto the weighing machines is a difficult and unpleasant chore that normally takes 20 seconds to complete after the pig is positioned on the load cell. Additionally, pig weighing machines are prone to mechanical breakdowns and are unpleasant to repair because of the farm residue they collect. Therefore, the development of a more practical and robust weight measurement apparatus is desired. In the present paper, we report on a handheld weight estimation device using a red-green-blue-depth (RGB-D) sensor with a laser slit system. An RGB-D sensor system is used to estimate pig weights, and the laser slit is used to align the measurement direction of the RGB-D sensor along the pig’s body. In operation, this system captures a depth image of the pig under examination and then uses it to create three-dimensional (3D) data. The 3D body parameters are extracted using image processing, and those extracted 3D body parameters for the pig are then fed into a random forest algorithm to produce the weight estimation. We also report on experimental results that demonstrate the reliability of our pig weight estimation system and its suitability for practical use.
MISC 【 display / non-display 】
-
バークシャー種肥育豚における3Dカメラを用いた体重推定 Reviewed
泊鞠沙, 仲間ひなた, 浦川啓介, 川末紀功仁, WIN Khin Dagon, 徳永忠昭
日本暖地畜産学会報 67 ( 2 ) 2024
Publishing type：Rapid communication, short report, research note, etc. (scientific journal)
-
Pig Weight Estimation Using Machine Learning Reviewed
金澤波音, 川末紀功仁, KHIN DAGON Win, 壱岐侑祐
計測自動制御学会システムインテグレーション部門講演会(CD-ROM) 22nd 2021
Publishing type：Rapid communication, short report, research note, etc. (scientific journal)
-
カメラを用いたオートソーティング装置による肥育豚省力出荷体系 Reviewed
岐本博紀, 岩切正芳, 壱岐侑祐, 三角久志, 加地雅也, 島村勝則, 松窪敬介, 吉田久美子, 冷水忠, 日高良太, 合志文利, 川島知之, 川末紀功仁, WIN Khin Dagon
九州沖縄農業試験研究の成果情報(Web) 2020 2020
Publishing type：Rapid communication, short report, research note, etc. (scientific journal)
Presentations 【 display / non-display 】
-
Feasibility Study on Automated Screening of Substandard Broiler Chickens Using YOLOv8
Takafumi KODAMA, Tsubasa NARITA, Khin Dagon Winb and Kikuhito KAWASUE
International Joint Conference on Advanced Mechatronics 2025 2026.11.27
Event date： 2026.11.27
Language：English Presentation type：Oral presentation (general)
-
Study on a Three-Dimensional Temperature Measurement Method Using Sensor Fusion of RGB-D Cameras and Thermal Imaging
Kentaro FUKUNISHI、Kikuhito KAWASUE、Ikkei KATO、 Kin Dagon Win
The 79th General Meeting and Lecture Meeting of the Kyushu Branch of the Japan Society of Mechanical Engineers 2025.5.30
Event date： 2026.3.10
Language：Japanese Presentation type：Oral presentation (general)
-
A 3D Point Cloud Segmentation Method with Attitude Correction for Measuring Pig Height Using an IMU- Integrated RGB-D Sensor
Ikkei KATO、Kikuhito KAWASUE、Kentaro FUKUNISHI、 Khin Dagon Win
The 79th General Meeting and Lecture Meeting of the Kyushu Branch of the Japan Society of Mechanical Engineers 2025.5.30
Event date： 2026.3.10
Language：Japanese Presentation type：Oral presentation (general)
-
移動式3Dカメラによる黒毛和種肥育牛体重推定の可能性
税所太一，小野芽生，KHIN DAGON WIN，川末紀功仁，徳永忠昭
第18回日本暖地畜産学会佐賀大会 2025.10.26
Event date： 2025.10.25 - 2025.10.26
Presentation type：Oral presentation (general)
-
バークシャー種肥育豚におけるセンサ情報を用いた体重や産肉形質の経時的変化
小島瑛里香，新美柚樹，浦川啓介，KHIN DAGON WIN，川末紀功仁，徳永忠昭
第18回日本暖地畜産学会佐賀大会 2025.10.26
Event date： 2025.10.25 - 2025.10.26
Language：Japanese Presentation type：Oral presentation (general)
Awards 【 display / non-display 】
-
8th Japan Open Innovation Awards, "Minister of Agriculture, Forestry and Fisheries Award"
2026.2 Cabinet Office スカブター：AI・AR 技術による非接触型体重推定デバイスの社会実装
川末 紀功仁、Khin Dagon Win、徳永 忠昭、金子 政時、 助川 慎
-
優秀講演賞
2021.11 SI2021 機械学習を用いた豚の体重推定
金澤波音、川末紀功仁、Khin Dagon Win
Award type：Award from Japanese society, conference, symposium, etc.
-
優秀講演賞
2019.11 SI2019 カメラによる豚の自動体重選別装置
吉田久美子、川末紀功仁、Khin Dagon Win
Award type：Award from Japanese society, conference, symposium, etc.
Grant-in-Aid for Scientific Research 【 display / non-display 】
-
オールイン・オールアウト方式養豚におけるペン平均体重の非接触・日次推定基盤の構築
Grant number：26K17393 2026.04 - 2029.12
Authorship：Principal investigator
-
低出生体重児の非接触身体測定システムの開発
Grant number：25K03484 2025.04 - 2029.03
Authorship：Principal investigator