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Yongliang Qiao
Yongliang Qiao
Australian Institute for Machine Learning (AIML) ,The University of Adelaide
在 adelaide.edu.au 的电子邮件经过验证
标题
引用次数
引用次数
年份
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
1742019
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
1632020
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
1032021
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
742019
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
692021
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
452022
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
412022
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
392021
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
352022
Weed recognition based on SVM-DS multi-feature fusion.
HDJ He DongJian, QYL Qiao YongLiang, LP Li Pan, GZ Gao Zhan, ...
342013
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
332021
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
322020
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
302023
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
292013
Multiframe-based high dynamic range monocular vision system for advanced driver assistance systems
Y Li, Y Qiao, Y Ruichek
IEEE Sensors Journal 15 (10), 5433-5441, 2015
252015
ConvNet and LSH-based visual localization using localized sequence matching
Y Qiao, C Cappelle, Y Ruichek, T Yang
Sensors 19 (11), 2439, 2019
232019
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
222020
Deep learning based automatic grape downy mildew detection
Z Zhang, Y Qiao, Y Guo, D He
Frontiers in Plant Science 13, 872107, 2022
192022
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
192022
Filtering for systems subject to unknown inputs without a priori initial information
H Kong, M Shan, D Su, Y Qiao, A Al-Azzawi, S Sukkarieh
Automatica 120, 109122, 2020
182020
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