| Automatic detection and tracking of mounting behavior in cattle using a deep learning-based instance segmentation model SM Noe, TT Zin, P Tin, I Kobayashi Int. J. Innov. Comput. Inf. Control 18 (1), 211-220, 2022 | 19 | 2022 |
| Comparing state-of-the-art deep learning algorithms for the automated detection and tracking of black cattle S Myat Noe, TT Zin, P Tin, I Kobayashi Sensors 23 (1), 532, 2023 | 13 | 2023 |
| Automatic detection of mounting behavior in cattle using semantic segmentation and classification SM Noe, TT Zin, P Tin, I Kobayashi 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech …, 2021 | 6 | 2021 |
| A deep learning-based solution to cattle region extraction for lameness detection SM Noe, TT Zin, P Tin, I Kobayashi 2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech …, 2022 | 5 | 2022 |
| Detection of estrus in cattle by using image technology and machine learning methods SM Noe, TT Zin, P Tin, H Hama 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE), 320-321, 2020 | 5 | 2020 |
| An Intelligent Method for Detecting Lameness in Modern Dairy Industry TT Zin, MZ Pwint, SM Noe, I Kobayashi 2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech …, 2022 | 2 | 2022 |
| Efficient Segment-Anything Model for Automatic Mask Region Extraction in Livestock Monitoring SM Noe, TT Zin, P Tin, I Kobyashi 2023 IEEE 13th International Conference on Consumer Electronics-Berlin (ICCE …, 2023 | 1 | 2023 |
| Enhancing Precision Agriculture: Innovative Tracking Solutions for Black Cattle Monitoring IK Su Myat Noe, Thi Thi Zin, Pyke Tin | | |
