| Deep learning for smart fish farming: applications, opportunities and challenges X Yang, S Zhang, J Liu, Q Gao, S Dong, C Zhou Reviews in Aquaculture 13 (1), 66-90, 2021 | 306 | 2021 |
| Application of machine learning in intelligent fish aquaculture: A review S Zhao, S Zhang, J Liu, H Wang, J Zhu, D Li, R Zhao Aquaculture 540, 736724, 2021 | 216 | 2021 |
| Evaluation of fish feeding intensity in aquaculture using a convolutional neural network and machine vision C Zhou, D Xu, L Chen, S Zhang, C Sun, X Yang, Y Wang Aquaculture 507, 457-465, 2019 | 191 | 2019 |
| Real-time detection and tracking of fish abnormal behavior based on improved YOLOV5 and SiamRPN++ H Wang, S Zhang, S Zhao, Q Wang, D Li, R Zhao Computers and Electronics in Agriculture 192, 106512, 2022 | 141 | 2022 |
| Automatic fish population counting by machine vision and a hybrid deep neural network model S Zhang, X Yang, Y Wang, Z Zhao, J Liu, Y Liu, C Sun, C Zhou Animals 10 (2), 364, 2020 | 110 | 2020 |
| A lightweight dead fish detection method based on deformable convolution and YOLOV4 S Zhao, S Zhang, J Lu, H Wang, Y Feng, C Shi, D Li, R Zhao Computers and Electronics in Agriculture 198, 107098, 2022 | 67 | 2022 |
| Visual SLAM for underwater vehicles: A survey S Zhang, S Zhao, D An, J Liu, H Wang, Y Feng, D Li, R Zhao Computer Science Review 46, 100510, 2022 | 63 | 2022 |
| Fast detection of cannibalism behavior of juvenile fish based on deep learning H Wang, S Zhang, S Zhao, J Lu, Y Wang, D Li, R Zhao Computers and Electronics in Agriculture 198, 107033, 2022 | 36 | 2022 |
| LiteEnhanceNet: A lightweight network for real-time single underwater image enhancement S Zhang, S Zhao, D An, D Li, R Zhao Expert Systems with Applications 240, 122546, 2024 | 21 | 2024 |
| Image super-resolution reconstruction using generative adversarial networks based on wide-channel activation X Sun, Z Zhao, S Zhang, J Liu, X Yang, C Zhou Ieee Access 8, 33838-33854, 2020 | 12 | 2020 |
| Evaluation of fish feeding intensity in aquaculture based on near-infrared machine vision Z Chao, X Daming, L Kai, C Lan, Z Song, S Chuanheng, Y Xinting Smart agriculture 1 (1), 76, 2019 | 11 | 2019 |
| Computer vision and feeding behavior based intelligent feeding controller for fish in aquaculture C Zhou, K Lin, D Xu, C Sun, L Chen, S Zhang, Q Guo Computer and Computing Technologies in Agriculture XI: 11th IFIP WG 5.14 …, 2019 | 9 | 2019 |
| Method for segmentation of overlapping fish images in aquaculture C Zhou, K Lin, D Xu, J Liu, S Zhang, C Sun, X Yang International Journal of Agricultural and Biological Engineering 12 (6), 135-142, 2019 | 8 | 2019 |
| A Metric-Based Few-Shot Learning Method for Fish Species Identification with Limited Samples J Lu, S Zhang, S Zhao, D Li, R Zhao Animals 14 (5), 755, 2024 | 4 | 2024 |
| MDNet: A Fusion Generative Adversarial Network for Underwater Image Enhancement S Zhang, S Zhao, D An, D Li, R Zhao Journal of Marine Science and Engineering 11 (6), 1183, 2023 | 4 | 2023 |
| Mamba-UIE: Enhancing Underwater Images with Physical Model Constraint S Zhang, Y Duan, D Li, R Zhao arXiv preprint arXiv:2407.19248, 2024 | 1 | 2024 |
| Unsupervised underwater image restoration via Koschmieder model disentanglement S Zhang, D An, D Li, R Zhao Expert Systems with Applications 266, 126075, 2025 | | 2025 |
| PDCFNet: Enhancing Underwater Images through Pixel Difference Convolution S Zhang, D Li, R Zhao arXiv preprint arXiv:2409.19269, 2024 | | 2024 |
Chao Zhou（周超）China National Engineering Research Center for Information Technology in AgricultureDirección de correo verificada de nercita.org.cn