| CNN feature based graph convolutional network for weed and crop recognition in smart farming H Jiang, C Zhang, Y Qiao, Z Zhang, W Zhang, C Song Computers and electronics in agriculture 174, 105450, 2020 | 231 | 2020 |
| Cattle segmentation and contour extraction based on Mask R-CNN for precision livestock farming Y Qiao, M Truman, S Sukkarieh Computers and Electronics in Agriculture 165, 104958, 2019 | 199 | 2019 |
| Intelligent perception for cattle monitoring: A review for cattle identification, body condition score evaluation, and weight estimation Y Qiao, H Kong, C Clark, S Lomax, D Su, S Eiffert, S Sukkarieh Computers and electronics in agriculture 185, 106143, 2021 | 153 | 2021 |
| Data augmentation for deep learning based semantic segmentation and crop-weed classification in agricultural robotics D Su, H Kong, Y Qiao, S Sukkarieh Computers and Electronics in Agriculture 190, 106418, 2021 | 118 | 2021 |
| Individual cattle identification using a deep learning based framework Y Qiao, D Su, H Kong, S Sukkarieh, S Lomax, C Clark IFAC-PapersOnLine 52 (30), 318-323, 2019 | 94 | 2019 |
| An improved YOLOv5-based vegetable disease detection method J Li, Y Qiao, S Liu, J Zhang, Z Yang, M Wang Computers and Electronics in Agriculture 202, 107345, 2022 | 90 | 2022 |
| The research progress of vision-based artificial intelligence in smart pig farming S Wang, H Jiang, Y Qiao, S Jiang, H Lin, Q Sun Sensors 22 (17), 6541, 2022 | 67 | 2022 |
| Real time detection of inter-row ryegrass in wheat farms using deep learning D Su, Y Qiao, H Kong, S Sukkarieh Biosystems Engineering 204, 198-211, 2021 | 58 | 2021 |
| Cattle body detection based on YOLOv5-ASFF for precision livestock farming Y Qiao, Y Guo, D He Computers and Electronics in Agriculture 204, 107579, 2023 | 57 | 2023 |
| C3D-ConvLSTM based cow behaviour classification using video data for precision livestock farming Y Qiao, Y Guo, K Yu, D He Computers and electronics in agriculture 193, 106650, 2022 | 50 | 2022 |
| Intelligent perception-based cattle lameness detection and behaviour recognition: A review Y Qiao, H Kong, C Clark, S Lomax, D Su, S Eiffert, S Sukkarieh Animals 11 (11), 3033, 2021 | 43 | 2021 |
| BiLSTM-based individual cattle identification for automated precision livestock farming Y Qiao, D Su, H Kong, S Sukkarieh, S Lomax, C Clark 2020 IEEE 16th International Conference on Automation Science and …, 2020 | 43 | 2020 |
| Weed recognition based on SVM-DS multi-feature fusion. HDJ He DongJian, QYL Qiao YongLiang, LP Li Pan, GZ Gao Zhan, ... | 43 | 2013 |
| Deep learning based automatic grape downy mildew detection Z Zhang, Y Qiao, Y Guo, D He Frontiers in Plant Science 13, 872107, 2022 | 33 | 2022 |
| Identification method of multi-feature weed based on multi-spectral images and data mining C Zhao, D He, Y Qiao Transactions of the Chinese Society of Agricultural Engineering 29 (2), 192-198, 2013 | 30 | 2013 |
| Deep learning-based cow tail detection and tracking for precision livestock farming X Huang, Z Hu, Y Qiao, S Sukkarieh IEEE/ASME Transactions on Mechatronics 28 (3), 1213-1221, 2022 | 29 | 2022 |
| Enhanced LiteHRNet based sheep weight estimation using RGB-D images C He, Y Qiao, R Mao, M Li, M Wang Computers and Electronics in Agriculture 206, 107667, 2023 | 28 | 2023 |
| AI, sensors and robotics in plant phenotyping and precision agriculture Y Qiao, J Valente, D Su, Z Zhang, D He Frontiers in Plant Science 13, 1064219, 2022 | 28 | 2022 |
| Automated aerial animal detection when spatial resolution conditions are varied J Brown, Y Qiao, C Clark, S Lomax, K Rafique, S Sukkarieh Computers and Electronics in Agriculture 193, 106689, 2022 | 27 | 2022 |
| Data augmentation for deep learning based cattle segmentation in precision livestock farming Y Qiao, D Su, H Kong, S Sukkarieh, S Lomax, C Clark 2020 IEEE 16th International Conference on Automation Science and …, 2020 | 27 | 2020 |
Daobilige SuChina Agricultural UniversityVerified email at cau.edu.cn
