Integrating YOLOv8-agri and DeepSORT for Advanced Motion Detection in Agriculture and Fisheries
DOI:
https://doi.org/10.4108/eetinis.v11i1.4618Keywords:
YOLOv8, DeepSort, Motion Detection, Agricultural Datasets, Reproducibility, Open DataAbstract
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.
Downloads
References
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: https://doi.org/10.1109/TEVC.2022.3220747
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: https://doi.org/10.1109/ACCESS.2022.3209825
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: https://doi.org/10.1016/j.aiia.2022.09.007
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: https://doi.org/10.32604/csse.2023.027647
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: https://doi.org/10.1371/journal.pone.0292971
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: https://doi.org/10.1109/JSEN.2022.3151777
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: https://doi.org/10.1111/faf.12666
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: https://doi.org/10.1016/j.atech.2022.100061
Jocher, G., Chaurasia, A. and Qiu, J. (2023), Ultralytics yolov8. URL https://github.com/ultralytics/ultralytics.
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: https://doi.org/10.1109/CVPR52688.2022.00900
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: https://doi.org/10.1609/aaai.v37i12.26682
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: https://doi.org/10.1007/s11042-022-13644-y
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: https://doi.org/10.1016/j.compag.2022.107335
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: https://doi.org/10.1016/j.iswa.2022.200102
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: https://doi.org/10.1007/978-981-13-7403-6_50
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: https://doi.org/10.3389/fpls.2021.753603
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: https://doi.org/10.3389/fmars.2021.823173
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: https://doi.org/10.1140/epje/s10189-023-00290-x
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: https://doi.org/10.3390/info14040218
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: https://doi.org/10.1109/IPEC54454.2022.9777554
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: https://doi.org/10.1109/CVPRW59228.2023.00585
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: https://doi.org/10.3390/s23135843
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: https://doi.org/10.1109/ICIP.2017.8296962
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: https://doi.org/10.1109/ICIP.2016.7533003
Khodarahmi, M. and Maihami, V. (2023) A review on kalman filter models. Archives of Computational Methods in Engineering 30(1): 727–747. DOI: https://doi.org/10.1007/s11831-022-09815-7
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: https://doi.org/10.14569/IJACSA.2019.0100107
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: https://doi.org/10.15439/2021R14
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: https://doi.org/10.1145/3310986.3310997
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
