YOLOv8s-CFB: a lightweight method for real-time detection of apple fruits in complex environments
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11554-024-01543-4.pdf
Reference31 articles.
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2. Nesterov, D.A., Shurygin, B.M., Solovchenko, A.E., Krylov, A.S., Sorokin, D.V.: A CNN-based method for fruit detection in apple tree images. Comput. Math. Model. 33, 354–364 (2022). https://doi.org/10.1007/s10598-023-09577-2
3. Mao, Z., Wang, W., Yang, H.: Apple object detection in natural environment based on YOLO v5. In: 2023 2nd International Conference on Applied Statistics, Computational Mathematics and Software Engineering (ASCMSE 2023), vol. 12784. SPIE (2023). https://doi.org/10.1117/12.2691833
4. Jia, W., et al.: Apple harvesting robot under information technology: a review. Int. J. Adv. Robot. Syst. 17, 1729881420925310 (2020). https://doi.org/10.1177/1729881420925310
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