Integrating YOLOv8-agri and DeepSORT for Advanced Motion Detection in Agriculture and Fisheries


  • Hieu Duong-Trung Can Tho University
  • Nghia Duong-Trung German Research Centre for Artificial Intelligence image/svg+xml



YOLOv8, DeepSort, Motion Detection, Agricultural Datasets, Reproducibility, Open Data


This paper integrates the YOLOv8-agri models with the DeepSORT algorithm to advance object detection and tracking in the agricultural and fisheries sectors. We address the current limitations in object classification by adapting YOLOv8 to the unique demands of these environments, where misclassification can hinder operational efficiency. Through the strategic use of transfer learning on specialized datasets, our study refines the YOLOv8-agri models for precise recognition and categorization of diverse biological entities. Coupling these models with DeepSORT significantly enhances motion tracking, leading to more accurate and reliable monitoring systems. The research outcomes identify the YOLOv8l-agri model as the optimal solution for balancing detection accuracy with training time, making it highly suitable for precision agriculture and fisheries applications. We have publicly made our experimental datasets and trained models publicly available to foster reproducibility and further research. This initiative marks a step forward in applying sophisticated computer vision techniques to real-world agricultural and fisheries management.


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Bi, Y., Xue, B., Mesejo, P., Cagnoni, S. and Zhang, M. (2022) A survey on evolutionary computation for computer vision and image analysis: Past, present, and future trends. IEEE Transactions on Evolutionary Computation 27(1): 5–25. DOI:

Mahadevkar, S.V., Khemani, B., Patil, S., Kotecha, K., Vora, D., Abraham, A. and Gabralla, L.A. (2022) A review on machine learning styles in computer visiontechniques and future directions. IEEE Access . DOI:

Dhanya, V., Subeesh, A., Kushwaha, N., Vishwakarma, D.K., Kumar, T.N., Ritika, G. and Singh, A. (2022) Deep learning based computer vision approaches for smart agricultural applications. Artificial Intelligence in Agriculture . DOI:

Punithavathi, R., Rani, A.D.C., Sughashini, K., Kurangi, C., Nirmala, M., Ahmed, H.F.T. and Balamurugan, S. (2023) Computer vision and deep learningenabled weed detection model for precision agriculture. Comput. Syst. Sci. Eng 44(3): 2759–2774. DOI:

Huynh, H.X., Tran, L.N. and Duong-Trung, N. (2023) Smart greenhouse construction and irrigation control system for optimal brassica juncea development. Plos one 18(10): e0292971. DOI:

Hu, W.C., Chen, L.B., Huang, B.K. and Lin, H.M. (2022) A computer vision-based intelligent fish feeding system using deep learning techniques for aquaculture. IEEE Sensors Journal 22(7): 7185–7194. DOI:

Saleh, A., Sheaves, M. and Rahimi Azghadi, M. (2022) Computer vision and deep learning for fish classification in underwater habitats: A survey. Fish and Fisheries 23(4): 977–999. DOI:

Gladju, J., Kamalam, B.S. and Kanagaraj, A. (2022) Applications of data mining and machine learning framework in aquaculture and fisheries: A review. Smart Agricultural Technology 2: 100061. DOI:

Jocher, G., Chaurasia, A. and Qiu, J. (2023), Ultralytics yolov8. URL

Yamada, Y. and Otani, M. (2022) Does robustness on imagenet transfer to downstream tasks? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition: 9215–9224. DOI:

Luccioni, A.S. and Rolnick, D. (2023) Bugs in the data: How imagenet misrepresents biodiversity. In Proceedings of the AAAI Conference on Artificial Intelligence, 37: 14382–14390. DOI:

Terven, J. and Cordova-Esparza, D. (2023) A comprehensive review of yolo: From yolov1 to yolov8 and beyond. arXiv preprint arXiv:2304.00501 .

Diwan, T., Anirudh, G. and Tembhurne, J.V. (2023) Object detection using yolo: Challenges, architectural successors, datasets and applications. multimedia Tools and Applications 82(6): 9243–9275. DOI:

Mei, Y., Sun, B., Li, D., Yu, H., Qin, H., Liu, H., Yan, N. et al. (2022) Recent advances of target tracking applications in aquaculture with emphasis on fish. Computers and Electronics in Agriculture 201: 107335. DOI:

Coulibaly, S., Kamsu-Foguem, B., Kamissoko, D. and Traore, D. (2022) Deep learning for precision agriculture: A bibliometric analysis. Intelligent Systems with Applications 16: 200102. DOI:

Paul, A., Ghosh, S., Das, A.K., Goswami, S., Das Choudhury, S. and Sen, S. (2020) A review on agricultural advancement based on computer vision and machine learning. Emerging Technology in Modelling and Graphics: Proceedings of IEM Graph 2018 : 567–581. DOI:

Qin, Z., Wang, W., Dammer, K.H., Guo, L. and Cao, Z. (2021) Ag-yolo: A real-time low-cost detector for precise spraying with case study of palms. Frontiers in Plant Science 12: 753603. DOI:

Kandimalla, V., Richard, M., Smith, F., Quirion, J., Torgo, L. and Whidden, C. (2022) Automated detection, classification and counting of fish in fish passages with deep learning. Frontiers in Marine Science 8: 2049. DOI:

Durve, M., Orsini, S., Tiribocchi, A., Montessori, A., Tucny, J.M., Lauricella, M., Camposeo, A. et al. (2023) Benchmarking yolov5 and yolov7 models with deepsort for droplet tracking applications. The European Physical Journal E 46(5): 32. DOI:

Paik, C. and Kim, H.J. (2022) Improving object detection, multi-object tracking, and re-identification for disaster response drones. arXiv preprint arXiv:2201.01494 .

Razzok, M., Badri, A., El Mourabit, I., Ruichek, Y. and Sahel, A. (2023) Pedestrian detection and tracking system based on deep-sort, yolov5, and new data association metrics. Information 14(4): 218. DOI:

Wang, Y. and Yang, H. (2022) Multi-target pedestrian tracking based on yolov5 and deepsort. In 2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) (IEEE): 508–514. DOI:

Vats, A. and Anastasiu, D.C. (2023) Enhancing retail checkout through video inpainting, yolov8 detection, and deepsort tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition: 5529–5536. DOI:

Sharma, N., Baral, S., Paing, M.P. and Chawuthai, R. (2023) Parking time violation tracking using yolov8 and tracking algorithms. Sensors 23(13): 5843. DOI:

Wojke, N., Bewley, A. and Paulus, D. (2017) Simple online and realtime tracking with a deep association metric. In 2017 IEEE international conference on image processing (ICIP) (IEEE): 3645–3649. DOI:

Bewley, A., Ge, Z., Ott, L., Ramos, F. and Upcroft, B. (2016) Simple online and realtime tracking. In 2016 IEEE international conference on image processing (ICIP) (IEEE): 3464–3468. DOI:

Khodarahmi, M. and Maihami, V. (2023) A review on kalman filter models. Archives of Computational Methods in Engineering 30(1): 727–747. DOI:

Duong-Trung, N., Quach, L.D. andNguyen, C.N. (2019) Learning deep transferability for several agricultural classification problems. International Journal of Advanced Computer Science and Applications 10(1). DOI:

Duong-Trung, N., Le Ha, D.N. and Huynh, H.X. (2021) Classification-segmentation pipeline for mri via transfer learning and residual networks. In RICE: 39–43. DOI:

Duong-Trung, N., Quach, L.D., Nguyen, M.H. and Nguyen, C.N. (2019) Classification of grain discoloration via transfer learning and convolutional neural networks. In Proceedings of the 3rd International Conference on Machine Learning and Soft Computing: 27–32. DOI:




How to Cite

Duong-Trung, H., & Duong-Trung, N. (2024). Integrating YOLOv8-agri and DeepSORT for Advanced Motion Detection in Agriculture and Fisheries. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 11(1), e4.