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China Agricultural University Researchers have exceeded the planed publications of the project, with many important research outputs generated.
They have published or submitted 11 papers and articles associated to research developed in the AquaDetector project, some of which are available online.
We welcome you to explore our research!
1. Haixia Li, Yu Guo, Huajian Zhao, Yang Wang, David Chow, (2021) Towards automated greenhouse: A state of the art review on greenhouse monitoring methods and technologies based on internet of things, Computers and Electronics in Agriculture, 191, Elsevier B.V. https://doi.org/10.1016/j.compag.2021.106558
2. Wang, G.; Muhammad, A.; Liu, C.; Du, L.; Li, D. Automatic Recognition of Fish Behavior with a Fusion of RGB and Optical Flow Data Based on Deep Learning. Animals. 2021, 11, 2774. https://doi.org/10.3390/ani11102774
3. He Wang, Song Zhang, Shili Zhao, Qi Wang, Daoliang Li, Ran Zhao (2022), Real-time detection and tracking of fish abnormal behavior based on improved YOLOV5 and SiamRPN++. https://doi.org/10.1016/j.compag.2021.106512
4. Shanhong Zhang et al., 2022. Numerical investigations on temperature and flow field performance of octagonal culture tank under different physical parameters for fish growth based on computational fluid dynamics. Computers and Electronics in Agriculture https://doi.org/10.1016/j.compag.2022.106821
5. Shili Zhao et al., 2022. A lightweight dead fish detection method based on deformable convolution and YOLOV4. Computers and Electronics in Agriculture https://doi.org/10.1016/j.compag.2022.107098
6. Yu Guo et al., 2022. Dual memory scale network for multi-step time series forecasting in thermal environment of aquaculture facility: A case study of recirculating aquaculture water temperature. Expert Systems with Applications (SCI)
7. Qi Wang et al., 2022. Recent advances of machine vision technology in fish classification. ICES Journal of marine science https://doi.org/10.1093/icesjms/fsab264
8. Mulan Mu et al., 2022. Phase change materials applied in agricultural greenhouses. Journal of Energy Storage https://doi.org/10.1016/j.est.2022.104100
9. He Wang et al., 2022. Fast detection of cannibalism behavior of juvenile fish based on deep learning. Computers and Electronics in Agriculture https://doi.org/10.1016/j.compag.2022.107033
10. Shili Zhao, et. al., 2021, Application of machine learning in intelligent fish aquaculture: A review, Aquaculture, Volume 540, https://doi.org/10.1016/j.aquaculture.2021.736724.
11. Ran Zhao et al., 2022. Formation control of multiple underwater robots based on ADMM distributed model predictive control. Ocean Engineering https://doi.org/10.1109/TSMC.2018.2855444
AquaDetector project will have the opportunity to share our research with stakeholders in Singapore, with partner AquaBioTech Group submitting posters to the poster session in areas of water quality and biomedia optimisation. AquaBioTech Group will be represented at the conference by CEO Mr Shane A. Hunter and Business Development Executive Ms Paola Reale.
WORLD AQUACULTURE Singapore 2022 will be held in Singapore from November 29th to December 2nd with involvement from countries throughout the Asian-Pacific region and around the world. Aquaculture is rapidly growing in the Asian-Pacific region and increasingly being integrated into the Singapore food systems; therefore 2022 is the perfect time for the world aquaculture community to focus on Singapore.
A major international trade show at WORLD AQUACULTURE Singapore 2022 is the place to learn about the latest aquaculture technologies presented by exhibitors from around the world.
Registration for this event is still open, and interested participants can register to attend
We look forward to see you there! You can visit the AquaBioTech Booth number 723 to meet our team and find more information on the AquaDetector Project.
