Yongliang Qiao

Dr Yongliang Qiao

Research Fellow

Australian Institute for Machine Learning - Projects

Faculty of Sciences, Engineering and Technology

My research focuses on agricultural artificial intelligence and robotics, which optimize agricultural production and improve the sustainability of farming practices.

My research interests include artificial intelligence, agricultural robots, smart farming, and intelligent perception. My vision is to integrate cutting-edge technologies such as artificial intelligence and robotics with agriculture to realize smart farming. The ultimate goal is to develop intelligent agricultural systems covering "information-agronomy-agricultural machinery" to support sustainable production and promote unmanned farms.

  • Journals
    Year Citation
    2024 Wang, L., Zheng, Y., Jin, D., Li, F., Qiao, Y., & Pan, S. (2024). Contrastive Graph Similarity Networks. ACM Transactions on the Web, 18(2), 17-1-17-20.
    DOI Scopus3
    2024 Hu, N., Su, D., Wang, S., Wang, X., Zhong, H., Wang, Z., . . . Tan, Y. (2024). Segmentation and tracking of vegetable plants by exploiting vegetable shape feature for precision spray of agricultural robots. Journal of Field Robotics, 41(3), 491-845.
    DOI
    2024 Lin, D., Chen, Y., Qiao, Y., Qin, D., Miao, Y., Sheng, K., . . . Wang, Y. (2024). A study on an accurate modeling for distinguishing nitrogen, phosphorous and potassium status in summer maize using in situ canopy hyperspectral data. Computers and Electronics in Agriculture, 221, 14 pages.
    DOI Scopus1
    2024 Zhang, B., & Qiao, Y. (2024). AI, Sensors, and Robotics for Smart Agriculture. Agronomy, 14(6), 3 pages.
    DOI
    2023 Fu, J., Chen, C., Zhao, R., Chen, Z., Li, D., & Qiao, Y. (2023). Frame vibration states identification for corn harvester based on joint improved empirical mode decomposition - Support vector machine method. Frontiers in Plant Science, 14, 15 pages.
    DOI Scopus2
    2023 Guo, Y., Du, S., Qiao, Y., & Liang, D. (2023). Advances in the Applications of Deep Learning Technology for Livestock Smart Farming. Smart Agriculture, 5(1), 52-65.
    DOI Scopus2
    2023 Jiang, H., Yang, X., Ding, R., Wang, D., Mao, W., & Qiao, Y. (2023). Identification of Apple Leaf Diseases Based on Improved ResNetl8. Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery, 54(4), 295-303.
    DOI Scopus7
    2023 Su, D., Qiao, Y., Jiang, Y., Valente, J., Zhang, Z., & He, D. (2023). Editorial: AI, sensors and robotics in plant phenotyping and precision agriculture, volume II. Frontiers in Plant Science, 14, 1215899.
    DOI Scopus1 Europe PMC1
    2023 Wang, S., Su, D., Jiang, Y., Tan, Y., Qiao, Y., Yang, S., . . . Hu, N. (2023). Fusing Vegetation Index and Ridge Segmentation for Robust Vision Based Autonomous Navigation of Agricultural Robots in Vegetable Farms.
    DOI
    2023 Wang, S., Jiang, H., Qiao, Y., & Jiang, S. (2023). A Method for Obtaining 3D Point Cloud Data by Combining 2D Image Segmentation and Depth Information of Pigs. Animals, 13(15), 17 pages.
    DOI Scopus1
    2023 Feng, T., Guo, Y., Huang, X., & Qiao, Y. (2023). Cattle Target Segmentation Method in Multi-Scenes Using Improved DeepLabV3+ Method. Animals, 13(15), 15 pages.
    DOI Scopus2
    2023 Fu, J., Li, J., Fu, Q., & Qiao, Y. (2023). Development and verification of adhesion models for track shoes operating on clay soils. Biosystems Engineering, 235, 69-82.
    DOI Scopus2
    2023 Guo, Y., Aggrey, S. E., Yang, X., Oladeinde, A., Qiao, Y., & Chai, L. (2023). Detecting broiler chickens on litter floor with the YOLOv5-CBAM deep learning model. Artificial Intelligence in Agriculture, 9, 36-45.
    DOI Scopus12
    2023 Wang, S., Su, D., Jiang, Y., Tan, Y., Qiao, Y., Yang, S., . . . Hu, N. (2023). Fusing vegetation index and ridge segmentation for robust vision based autonomous navigation of agricultural robots in vegetable farms. Computers and Electronics in Agriculture, 213, 15 pages.
    DOI Scopus6
    2023 Zhang, L., Han, G., Qiao, Y., Xu, L., Chen, L., & Tang, J. (2023). Interactive Dairy Goat Image Segmentation for Precision Livestock Farming. Animals, 13(20), 1-17.
    DOI
    2023 Guo, Y., Hong, W., Wu, J., Huang, X., Qiao, Y., & Kong, H. (2023). Vision-Based Cow Tracking and Feeding Monitoring for Autonomous Livestock Farming: The YOLOv5s-CA+DeepSORT-Vision Transformer. IEEE Robotics and Automation Magazine, 30(4), 68-76.
    DOI Scopus2
    2023 Qiao, Y., Guo, Y., & He, D. (2023). Cattle body detection based on YOLOv5-ASFF for precision livestock farming. Computers and Electronics in Agriculture, 204, 9 pages.
    DOI Scopus43 WoS16
    2023 He, C., Qiao, Y., Mao, R., Li, M., & Wang, M. (2023). Enhanced LiteHRNet based sheep weight estimation using RGB-D images. Computers and Electronics in Agriculture, 206, 1-10.
    DOI Scopus15 WoS7
    2022 Brown, J., Qiao, Y., Clark, C., Lomax, S., Rafique, K., & Sukkarieh, S. (2022). Automated aerial animal detection when spatial resolution conditions are varied. Computers and Electronics in Agriculture, 193, 1-11.
    DOI Scopus19 WoS7
    2022 Qiao, Y., Guo, Y., Yu, K., & He, D. (2022). C3D-ConvLSTM based cow behaviour classification using video data for precision livestock farming. Computers and Electronics in Agriculture, 193, 11 pages.
    DOI Scopus43 WoS22
    2022 Xue, T., Qiao, Y., Kong, H., Su, D., Pan, S., Rafique, K., & Sukkarieh, S. (2022). One-Shot Learning-Based Animal Video Segmentation. IEEE Transactions on Industrial Informatics, 18(6), 3799-3807.
    DOI Scopus15 WoS12
    2022 Wang, S., Jiang, H., Qiao, Y., Jiang, S., Lin, H., & Sun, Q. (2022). The Research Progress of Vision-Based Artificial Intelligence in Smart Pig Farming. Sensors, 22(17), 23 pages.
    DOI Scopus31 WoS11 Europe PMC12
    2022 Liu, S., Qiao, Y., Li, J., Zhang, H., Zhang, M., & Wang, M. (2022). An Improved Lightweight Network for Real-Time Detection of Apple Leaf Diseases in Natural Scenes. Agronomy, 12(10), 17 pages.
    DOI Scopus11 WoS4
    2022 Qiao, Y., Xue, T., Kong, H., Clark, C., Lomax, S., Rafique, K., & Sukkarieh, S. (2022). One-Shot Learning with Pseudo-Labeling for Cattle Video Segmentation in Smart Livestock Farming. Animals, 12(5), 16 pages.
    DOI Scopus5 WoS2 Europe PMC3
    2022 Fu, J., Liu, J., Zhao, R., Chen, Z., Qiao, Y., & Li, D. (2022). Maize disease detection based on spectral recovery from RGB images. Frontiers in Plant Science, 13, 12 pages.
    DOI Scopus3 Europe PMC1
    2022 Huang, X., Hu, Z., Qiao, Y., & Sukkarieh, S. (2022). Deep Learning-Based Cow Tail Detection and Tracking for Precision Livestock Farming. IEEE/ASME Transactions on Mechatronics, 28(3), 1213-1221.
    DOI Scopus15 WoS4
    2022 Zhang, K., Fan, J., Huang, S., Qiao, Y., Yu, X., & Qin, F. (2022). CEKD:Cross ensemble knowledge distillation for augmented fine-grained data. Applied Intelligence, 52(14), 16640-16650.
    DOI
    2022 Qiao, Y., Valente, J., Su, D., Zhang, Z., & He, D. (2022). Editorial: AI, sensors and robotics in plant phenotyping and precision agriculture. Frontiers in Plant Science, 13, 5 pages.
    DOI Scopus10 Europe PMC4
    2022 Hu, N., Wang, S., Wang, X., Cai, Y., Su, D., Nyamsuren, P., . . . Wei, H. (2022). LettuceMOT: A dataset of lettuce detection and tracking with re-identification of re-occurred plants for agricultural robots. Frontiers in Plant Science, 13, 6 pages.
    DOI Scopus4 WoS1 Europe PMC1
    2022 Hu, N., Su, D., Wang, S., Nyamsuren, P., Qiao, Y., Jiang, Y., & Cai, Y. (2022). LettuceTrack: Detection and tracking of lettuce for robotic precision spray in agriculture. Frontiers in Plant Science, 13, 1-16.
    DOI Scopus16 Europe PMC5
    2022 Li, J., Qiao, Y., Liu, S., Zhang, J., Yang, Z., & Wang, M. (2022). An improved YOLOv5-based vegetable disease detection method. Computers and Electronics in Agriculture, 202, 1-10.
    DOI Scopus59 WoS31
    2022 Zhang, Z., Qiao, Y., Guo, Y., & He, D. (2022). Deep Learning Based Automatic Grape Downy Mildew Detection. Frontiers in Plant Science, 13, 1-12.
    DOI Scopus17 WoS7 Europe PMC5
    2021 Su, D., Kong, H., Qiao, Y., & Sukkarieh, S. (2021). Data augmentation for deep learning based semantic segmentation and crop-weed classification in agricultural robotics. Computers and Electronics in Agriculture, 190, 12 pages.
    DOI Scopus68 WoS32
    2021 Qiao, Y., Kong, H., Clark, C., Lomax, S., Su, D., Eiffert, S., & Sukkarieh, S. (2021). Intelligent perception for cattle monitoring: A review for cattle identification, body condition score evaluation, and weight estimation. Computers and Electronics in Agriculture, 185, 11 pages.
    DOI Scopus96 WoS45
    2021 Su, D., Qiao, Y., Kong, H., & Sukkarieh, S. (2021). Real time detection of inter-row ryegrass in wheat farms using deep learning. Biosystems Engineering, 204, 198-211.
    DOI Scopus39 WoS23
    2021 Zhang, W., Sun, X., Qiao, Y., Bai, P., Jiang, H., Wang, Y., . . . Zong, H. (2021). Tobacco disease identification based on InceptionV3. Acta Tabacaria Sinica, 27(5), 61-70.
    DOI Scopus9
    2021 Qiao, Y., Kong, H., Clark, C., Lomax, S., Su, D., Eiffert, S., & Sukkarieh, S. (2021). Intelligent perception-based cattle lameness detection and behaviour recognition: A review. Animals, 11(11), 20 pages.
    DOI Scopus28 WoS13 Europe PMC10
    2021 Guo, Y., Qiao, Y., Sukkarieh, S., Chai, L., & He, D. (2021). BIGRU-ATTENTION BASED COW BEHAVIOR CLASSIFICATION USING VIDEO DATA FOR PRECISION LIVESTOCK FARMING. Transactions of the ASABE, 64(6), 1823-1833.
    DOI Scopus16 WoS10
    2021 Qiao, Y., Clark, C., Lomax, S., Kong, H., Su, D., & Sukkarieh, S. (2021). Automated Individual Cattle Identification Using Video Data: A Unified Deep Learning Architecture Approach. FRONTIERS IN ANIMAL SCIENCE, 2, 1-14.
    DOI Scopus11 WoS3
    2020 Kong, H., Shan, M., Su, D., Qiao, Y., Al-Azzawi, A., & Sukkarieh, S. (2020). Filtering for systems subject to unknown inputs without a priori initial information. Automatica, 120, 12 pages.
    DOI Scopus20 WoS14
    2020 Jiang, H., Zhang, C., Qiao, Y., Zhang, Z., Zhang, W., & Song, C. (2020). CNN feature based graph convolutional network for weed and crop recognition in smart farming. Computers and Electronics in Agriculture, 174, 11 pages.
    DOI Scopus163 WoS86
    2019 Qiao, Y., Truman, M., & Sukkarieh, S. (2019). Cattle segmentation and contour extraction based on Mask R-CNN for precision livestock farming. Computers and Electronics in Agriculture, 165, 9 pages.
    DOI Scopus148 WoS94
    2019 Qiao, Y., Cappelle, C., Ruichek, Y., & Yang, T. (2019). ConvNet and LSH-based visual localization using localized sequence matching. Sensors (Switzerland), 19(11), 23 pages.
    DOI Scopus19 WoS13 Europe PMC3
    2017 Qiao, Y. (2017). Visual localization across seasons using sequence matching based on multi-feature combination. Sensors (Switzerland), 17(11), 22 pages.
    DOI Scopus17 WoS8 Europe PMC2
    2017 Qiao, Y., & Zhang, Z. (2017). Visual Localization by Place Recognition Based on Multifeature (D- LBP++HOG). Journal of Sensors, 2017, 18 pages.
    DOI Scopus4 WoS2
    2015 Li, Y., Qiao, Y., & Ruichek, Y. (2015). Multiframe-based high dynamic range monocular vision system for advanced driver assistance systems. IEEE Sensors Journal, 15(10), 5433-5441.
    DOI Scopus25 WoS17
    2014 Qiao, Y., Cappelle, C., & Ruichek, Y. (2014). Image based place recognition and lidar validation for vehicle localization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8856, 304-315.
    DOI Scopus1
    2013 He, D., Qiao, Y., Li, P., Gao, Z., Li, H., & Tang, J. (2013). Weed recognition based on SVM-DS multi-feature fusion. Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery, 44(2), 182-187.
    DOI Scopus42
    2013 Zhao, C., He, D., & Qiao, Y. (2013). Identification method of multi-feature weed based on multi-spectral images and data mining. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 29(2), 192-198.
    DOI Scopus33

  • Books
    Year Citation
    2022 Qiao, Y., Chai, L., & He, D. (2022). Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming. Mdpi AG.

  • Conference Papers
    Year Citation
    2023 Qiao, Y., Guo, Y., & He, D. (2023). Deep Learning-Based Autonomous Cow Detection for Smart Livestock Farming. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13744 LNCS (pp. 246-258). Online: Springer International Publishing.
    DOI
    2022 Ding, R., Qiao, Y., Yang, X., Jiang, H., Zhang, Y., Huang, Z., . . . Liu, H. (2022). Improved ResNet Based Apple Leaf Diseases Identification. In IFAC-PapersOnLine Vol. 55 (pp. 78-82). Online: ELSEVIER.
    DOI Scopus10 WoS1
    2022 Qiao, Y., Zhang, Z., Guo, Y., & He, D. (2022). Deep learning based grape mildew disease severity classification. In 2022 ASABE Annual International Meeting. St Joseph, Michigan, USA: American Society of Agricultural and Biological Engineers.
    DOI Scopus2
    2022 Qiao, Y., Guo, Y., He, D., & Chai, L. (2022). Deep Learning-based Autonomous cow body detection for smart livestock farming. In American Society of Agricultural and Biological Engineers (ASABE) (pp. 110-115). Houston, Texas, USA: American Society of Agricultural and Biological Engineers.
    DOI
    2020 Qiao, Y., Su, D., Kong, H., Sukkarieh, S., Lomax, S., & Clark, C. (2020). BiLSTM-based Individual Cattle Identification for Automated Precision Livestock Farming. In 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Vol. 2020-August (pp. 967-972). New York, NY, USA: IEEE.
    DOI Scopus27 WoS15
    2020 Qiao, Y., Su, D., Kong, H., Sukkarieh, S., Lomax, S., & Clark, C. (2020). Data Augmentation for Deep Learning based Cattle Segmentation in Precision Livestock Farming. In 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Vol. 2020-August (pp. 979-984). New York, NY, USA: IEEE.
    DOI Scopus20 WoS15
    2019 Qiao, Y., Su, D., Kong, H., Sukkarieh, S., Lomax, S., & Clark, C. (2019). Individual Cattle Identification Using a Deep Learning Based Framework. In R. Fitch, J. Katupitiya, & M. Whitty (Eds.), IFAC-PapersOnLine Vol. 52 (pp. 318-323). Amsterdam, Netherlands: ELSEVIER.
    DOI Scopus66 WoS36
    2017 Qiao, Y., Cappelle, C., Yang, T., & Ruichek, Y. (2017). Visual localization based on place recognition using multi-feature combination (D- λ LBP++HOG). In J. BlancTalon, R. Penne, W. Philips, D. Popescu, & P. Scheunders (Eds.), Advanced Concepts for Intelligent Vision Systems. ACIVS 2017 Vol. 10617 LNCS (pp. 275-287). Antwerp, BELGIUM: SPRINGER INTERNATIONAL PUBLISHING AG.
    DOI Scopus1
    2016 Qiao, Y., Cappelle, C., Ruichek, Y., & Dornaika, F. (2016). Visual localization based on sequence matching using ConvNet features. In IECON Proceedings (Industrial Electronics Conference) (pp. 1067-1074). ITALY, Florence: IEEE.
    DOI Scopus3 WoS1
    2016 Qiao, Y., Cappelle, C., & Ruichek, Y. (2016). Visual localization using sequence matching based on multi-feature combination. In J. BlancTalon, C. Distante, W. Philips, D. Popescu, & P. Scheunders (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10016 LNCS (pp. 324-335). Lecce, ITALY: SPRINGER INTERNATIONAL PUBLISHING AG.
    DOI Scopus1
    2015 Qiao, Y., Cappelle, C., & Ruichek, Y. (2015). Place recognition based visual localization using lbp feature and SVM. In O. P. Lagunas, O. H. Alcantara, & G. A. Figueroa (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9414 (pp. 393-404). Polytechn Univ Morelos, Cuernavaca, MEXICO: SPRINGER-VERLAG BERLIN.
    DOI Scopus6 WoS5
    2013 Li, P., He, D., Qiao, Y., & Yang, C. (2013). An application of soft sets in weed identification. In American Society of Agricultural and Biological Engineers Annual International Meeting 2013, ASABE 2013 Vol. 5 (pp. 4279-4288). American Society of Agricultural and Biological Engineers.
    DOI Scopus4
  • Position: Research Fellow
  • Phone: 83132117
  • Email: yongliang.qiao@adelaide.edu.au
  • Campus: Lot 14
  • Building: Australian Institute for Machine Learning Building, floor Lower Ground
  • Room: LG.23
  • Org Unit: Australian Institute for Machine Learning - Projects
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