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Affiliation |
Organization for Learning and Student Development Division for Educational Planning |
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Title |
Assistant Professor |
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Contact information |
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AIKAWA Masaru
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Research Areas 【 display / non-display 】
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Informatics / Learning support system
Papers 【 display / non-display 】
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Mg W.H.E., Zin T.T., Tin P., Aikawa M., Honkawa K., Horii Y.
Scientific Reports 15 ( 1 ) 2378 2025.12
Language：English Publishing type：Research paper (scientific journal) Publisher：Scientific Reports
Accurately predicting the calving time in cattle is essential for optimizing livestock management and ensuring animal welfare. Our research focuses on developing a robust system for calving cattle classification and calving time prediction, utilizing 12-h trajectory data for 20 cattle. Our system classifies cattle as abnormal (requiring human assistance) or normal (not requiring assistance) and predicts calving times based on their individual behaviors. We employed a tailored YOLOv8 model for efficient and precise cattle detection, effectively filtering out noise such as people and trucks. Our Customized Tracking Algorithm (CTA) maintains continuous identity tracking for each cow, enabling accurate re-identification even during occlusions. To minimize some ID switching errors over extended tracking periods, we integrated IDs optimization in the CTA utilizing Global IDs identification. We extracted and compared three total movement features for classifying cattle as abnormal or normal. For predicting calving times for each cow, we utilized and compared three cumulative movement features. Our system is fully automated, detecting and tracking all 20 cattle continuously for 12 h without manual assistance, and achieving an overall accuracy of 99%. By comparing three features derived from the trajectory tracking data for each point in a frame, we achieve 100%, 95%, 85% accuracy in classifying cattle as abnormal or normal and predict their calving times with a precision of within the next 6 h, within the next 9 h, within the next 8 h, respectively. Our system enables farmers to provide timely assistance, ensuring the health and safety of both the cow and the calf. Furthermore, it aids in optimizing resource allocation and enhancing overall farm efficiency, emphasizing the critical importance of calving time prediction in sustainable livestock farming.
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A Study on Health Management by Behavior Analysis of Calves Reviewed
Nishiyama T., Kazuhisa S., Aikawa M., Kobayashi I., Zin T.T.
Lecture Notes in Electrical Engineering 1322 LNEE 144 - 151 2025
Publishing type：Research paper (scientific journal) Publisher：Lecture Notes in Electrical Engineering
It is important to always monitor the health of cattle, especially calves, and the frequency of observation increases with environmental changes in addition to once a day. In addition, calves tend to be more susceptible to infectious diseases because of their immature immune systems. Therefore, rearing management is extremely important. And the number of dairy cattle-keeping households and the total number of cattle are decreasing, while the number of cattle per household is increasing, indicating that management is becoming larger in scale. In this study, we proposed the development of a health management system by analyzing calf behavior using a 3D camera. Experiments were conducted at the Sumiyoshi Field of Miyazaki University to confirm the effectiveness of the proposed method.
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Cow’s Back Surface Segmentation of Point-Cloud Image Using PointNet++ for Individual Identification Reviewed
Kyaw P.P., Tin P., Aikawa M., Kobayashi I., Zin T.T.
Lecture Notes in Electrical Engineering 1321 LNEE 199 - 209 2025
Publishing type：Research paper (scientific journal) Publisher：Lecture Notes in Electrical Engineering
An automatic cow health monitoring and management system cannot function effectively without an identification system in livestock farming. While 2D image-based computer vision currently achieves high accuracy in cow identification, its effectiveness can be significantly decreased by changes in lighting, environmental factors, and other limitations. To address these limitations, an identification system based on point-cloud images will be developed by using a combination of 3D TOF camera and 2D RGB camera. This system includes detection and segmentation stage, feature extraction stage, and identification stage. In this study, I focus on detecting and segmenting of cow back surface region from a point-cloud image using the PointNet+ + algorithm. Two segmentation models are trained and compared based on single-scale grouping (SSG) and multi-scale grouping (MSG) features. The extracted cow back surface region offers a rich set of features valuable for several applications, including individual cow identification, lameness detection, and body condition scoring.
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Cattle Lameness Detection Using Leg Region Keypoints from a Single RGB Camera Reviewed
Myint B.B., Zin T.T., Aikawa M., Kobayashi I., Tin P.
Lecture Notes in Electrical Engineering 1321 LNEE 180 - 189 2025
Publishing type：Research paper (scientific journal) Publisher：Lecture Notes in Electrical Engineering
The recent rise of machine learning and deep learning has significantly impacted the field of computer vision, particularly in tasks like object detection, object tracking, and classification. This surge in interest has underscored the critical role of feature extraction as a foundational step in these machine learning pipelines. Our research focuses on applying feature extraction techniques to a cattle lameness dataset. We specifically extract features related to the movement of key points on cattle legs across a sequence of video frames. By analyzing the variations in these points, we aim to identify features that can efficiently differentiate between lame and no lame cattle using popular machine learning algorithms. All four classifiers achieved strong testing accuracy above 75%, with SVM excelling at over 84%.
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Cattle Lameness Classification Using Cattle Back Depth Information Reviewed
Tun S.C., Tin P., Aikawa M., Kobayashi I., Zin T.T.
Lecture Notes in Electrical Engineering 1321 LNEE 160 - 170 2025
Publishing type：Research paper (scientific journal) Publisher：Lecture Notes in Electrical Engineering
The livestock industry plays a crucial role in sustaining agricultural production and rural economies. Monitoring cattle health, however, presents significant challenges on large farms where traditional methods require diagnosing each animal individually. Lameness is a major issue affecting cattle health, leading to decreased production performance on many farms. Timely detection of lameness is essential for providing effective early treatment. In this study, we propose a system using specialized depth cameras to monitor and analyze cattle back information for classifying lameness scores. We employ Detectron2 for cattle detection and segmentation, and the Intersection over Union (IOU) method for tracking, focusing solely on the cattle’s depth region. We extract various features from the cattle’s back depth data and utilize three different machine learning algorithms: K-Nearest Neighbor (KNN), Gradient Boosting, and Extra Trees for lameness score classification. The models KNN, Gradient Boosting, and Extra Trees showed strong training and validation results. Testing showed Extra Trees performing well with 88.2% morning and 89.0% evening accuracy. Our approach demonstrates the potential of depth camera in effectively classifying lameness scores, offering significant implications for livestock health management. This method not only improves the efficiency and accuracy of health monitoring in large-scale farming but also provides a practical solution for early detection and treatment of lameness, thereby enhancing overall farm productivity.
Presentations 【 display / non-display 】
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Automatic Cattle Detection and Tracking for Lameness Classification Using a Single Side-View Camera International conference
Bo Bo Myint, T. Onizuka, Pyke Tin, M. Aikawa, I. Kobayashi, Thi Thi Zin
Second International Symposium on Data-Driven Intelligent Optimization for Decision Making (DIODM2024) 2024.3.25
Event date： 2024.3.25 - 2024.3.29
Language：English Presentation type：Oral presentation (general)
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Kalman Velocity-based Multi-Stage Classification Approach for Recognizing Black Cow Actions International conference
Cho Cho Aye, Thi Thi Zin, M. Aikawa, I. Kobayashi
2024 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP'24) 2024.2.27
Event date： 2024.2.27 - 2024.3.1
Language：English Presentation type：Oral presentation (general)
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AI Driven Movement Rate Variability Analysis Around the Time of Calving Events in Cattle International conference
Wai Hnin Eaindrar Mg, Pyke Tin, M. Aikawa, I. Kobayashi, Y. Horii, K. Honkawa, Thi Thi Zin
The 15th International Conference Genetic and Evolutionary Computing 2023.10.6
Event date： 2023.10.6 - 2023.10.8
Language：English Presentation type：Oral presentation (general)
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Evaluation of Body Condition Score for walking dairy cows using 3D camera International conference
M. Chikunami, Thi Thi Zin, M. Aikawa, I. Kobayashi
17th International Conference on Innovative Computing, Information and Control (ICICIC2023) 2023.8.29
Event date： 2023.8.29 - 2023.8.31
Language：English Presentation type：Oral presentation (general)
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A Study on Early Detection of Otitis Media in Calves using RGB and Thermographic Cameras International conference
T. Nishiyama, K. Shiiya, M. Aikawa, I. Kobayashi, Thi Thi Zin
17th International Conference on Innovative Computing, Information and Control (ICICIC2023) 2023.8.29
Event date： 2023.8.29 - 2023.8.31
Language：English Presentation type：Oral presentation (general)
Grant-in-Aid for Scientific Research 【 display / non-display 】
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メニーコアCPUに適した並列幾何マルチグリッド前処理付きCG法に基づく人体内電流解析
Grant number：18H00541 2018
日本学術振興会 科学研究費助成事業（科学研究費補助金）（奨励研究） 奨励研究
相川 勝
Authorship：Principal investigator
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数値シミュレーション向け並列計算ライブラリの高性能化に関する研究
Grant number：17H00369 2017
日本学術振興会 科学研究費助成事業（科学研究費補助金）（奨励研究） 奨励研究
相川 勝
Authorship：Principal investigator
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教務電算システムと研究教育用ネットワーク間の安全なインターフェイスの開発研究
Grant number：09919091 1997
日本学術振興会 科学研究費助成事業（科学研究費補助金）（奨励研究） 奨励研究
相川 勝
Authorship：Principal investigator