References
- ALI, M. L. – ZHANG, Z. 2024. The YOLO framework: A comprehensive review of evolution, applications, and benchmarks in object detection. In Computers, vol. 13, no. 12, article no. 336. DOI: https://doi.org/10.3390/computers13120336
- ARAD, B. – BALENDONCK, J. – BARTH, R. – BEN-SHAHAR, O. – EDAN, Y. – HELLSTRÖM, T. – HEMMING, J. – KURTSER, P. – RINGDAHL, O. – TIELEN, T. – VAN TUIJL, B. 2020. Development of a sweet pepper harvesting robot. In Journal of Field Robotics, vol. 37, no. 6, pp. 1027–1039. DOI: https://doi.org/10.1002/rob.21937
- GITHUB.COM. 2023. Brief summary of YOLOv8 model structure. Available at: https://github.com/ultralytics/ultralytics/issues/189
- HE, B. – ZHANG, Y. – GONG, J. – FU, G. – ZHAO, Y. – WU, R. 2022. Fast recognition of tomato fruit in greenhouse at night based on improved YOLO v5. In Transactions of the Chinese Society for Agricultural Machinery, vol. 53, no. 5, pp. 201–208. DOI: https://doi.org/10.6041/j.issn.1000-1298.2022.05.020
- CHARISIS, C. – ARGYROPOULOS, D. 2024. Deep learning-based instance segmentation architectures in agriculture: A review of the scopes and challenges. In Smart Agricultural Technology, vol. 8, article no. 100448. DOI: https://doi.org/10.1016/j.atech.2024.100448
- JOCHER, G. – CHAURASIA, A. – QIU, J. 2023. YOLO by Ultralytics. Available at: https://github.com/ultralytics/ultralytics
- JOCHER, G. – CHAURASIA, A. – STOKEN, A. – BOROVEC, J. – NANOCODE012 – KWON, Y. – MICHAEL, K. – TAOXIE – FANG, J. – IMYHXY – LORNA – ZENG, Y. – WONG, C. – V, A. – MONTES, D. – WANG, Z. – FATI, C. – NADAR, J. – LAUGHING – UNGLVKITDE – SONCK, V. – TKIANAI – YXNONG – SKALSKI, P. – HOGAN, A. – NAIR, D. – STROBEL, M. – JAIN, M. 2020. Ultralytics/YOLOv5: v7.0 – YOLOv5 SOTA Realtime Instance Segmentation. Available at: https://doi.org/10.5281/zenodo.3908559
- JU, R.-Y. – CAI, W. 2023. Fracture detection in pediatric wrist trauma X-ray images using YOLOv8 algorithm. In Scientific Reports, vol. 13, article no. 20077. DOI: https://doi.org/10.1038/s41598-023-47460-7
- LI, R. – JI, Z. – HU, S. – HUANG, X. – YANG, J. – LI, W. 2023. Tomato maturity recognition model based on improved YOLOv5 in greenhouse. In Agronomy, vol. 13, no. 2, article no. 603. DOI: https://doi.org/10.3390/agronomy13020603
- LIN, T.-Y. – DOLLÁR, P. – GIRSHICK, R. – HE, K. – HARIHARAN, B. – BELONGIE, S. 2017 Feature pyramid networks for object detection. arXiv:1612.03144. DOI: https://doi.org/10.48550/arXiv.1612.03144
- LIU, S. – XUE, J. – ZHANG, T. – LV, P. – QIN, H. – ZHAO, T. 2024. Research progress and prospect of key technologies of fruit target recognition for robotic fruit picking. In Frontiers in Plant Science, vol. 15, article no. 1423338. DOI: https://doi.org/10.3389/fpls.2024.1423338
- LI, T. – SUN, M. – DING, X. – LI, Y. – ZHANG, G. – SHI, G. – LI, W. 2021. Tomato recognition method at the ripening stage based on YOLO v4 and HSV. In Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), vol. 37, no. 21, pp. 183–190. DOI: https://doi.org/10.11975/j.issn.1002-6819.2021.21.021
- LI, Y. – LI, J. – LUO, L. – WANG, L. – ZHI, Q. 2025. Tomato ripeness and stem recognition based on improved YOLOX. In Scientific Reports, vol. 15, article no. 1924. DOI: https://doi.org/10.1038/s41598-024-84869-0
- LYU, Z. – ZHANG, F. – WEI, X. – HUANG, Y. – LI, J. – ZHANG, Z. 2023. Synergistic recognition of tomato flowers and fruits in greenhouse using combination enhancement of YOLOX-ViT. In Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), vol. 39, no. 4, pp. 124–134. DOI: https://doi.org/10.11975/j.issn.1002-6819.202211246
- MA, B. – DU, J. – WANG, L. – JIANG, H. – ZHOU, M. 2021. Automatic branch detection of jujube trees based on 3D reconstruction for dormant pruning using the deep learning-based method. In Computers and Electronics in Agriculture, vol. 190, article no. 106484. DOI: https://doi.org/10.1016/j.compag.2021.106484
- McALLISTER, W. – OSIPYCHEV, D. – DAVIS, A. – CHOWDHARY, G. 2019. Agbots: Weeding a field with a team of autonomous robots. In Computers and Electronics in Agriculture, vol. 163, article no. 104827. DOI: https://doi.org/10.1016/j.compag.2019.05.036
- NAZARKAR, A. – KUCHULAKANTI, H. – PAIDIMARRY, C. S. – KULKARNI, S. 2023. Impact of various data splitting ratios on the performance of machine learning models in the classification of lung cancer. In Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023), Advances in Engineering Research, vol. 223, pp. 96–104. DOI: https://doi.org/10.2991/978-94-6463-252-1_12
- SOLIMANI, F. – CARDELLICCHIO, A. – DIMAURO, G. – PETROZZA, A. – SUMMERER, S. – CELLINI, F. – RENÒ, V. 2024. Optimizing tomato plant phenotyping detection: Boosting YOLOv8 architecture to tackle data complexity. In Computers and Electronics in Agriculture, vol. 218, article no. 108728. DOI: https://doi.org/10.1016/j.compag.2024.108728
- SONG, G. – WANG, J. – MA, R. – SHI, Y. – WANG, Y. 2024. Study on the fusion of improved YOLOv8 and depth camera for bunch tomato stem picking point recognition and localization. In Frontiers in Plant Science, vol. 15, article no. 1447855. DOI: https://doi.org/10.3389/fpls.2024.1447855
- SUN, Q. – CHAI, X. – ZENG, Z. – ZHOU, G. – SUN, T. 2021. Multilevel feature fusion for fruit bearing branch keypoint detection. In Computers and Electronics in Agriculture, vol. 191, article no. 106479. DOI: https://doi.org/10.1016/j.compag.2021.106479
- ULTRALYTICS. 2024. YOLOv8. Explore Ultralytics YOLOv8. Supported tasks and modes. Available at: https://docs.ultralytics.com/models/yolov8/#supported-tasks-and-modes
- VIJAYAKUMAR, A. – VAIRAVASUNDARAM, S. 2024. YOLO-based object detection models: A review and its applications. In Multimedia Tools and Applications, vol. 83, pp. 83535–83574. DOI: https://doi.org/10.1007/s11042-024-18872-y
- WANG, C.-Y. – BOCHKOVSKIY, A. – LIAO, H.-Y. M. 2023. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7464–7475. DOI: https://doi.org/10.1109/CVPR52729.2023.00721
- WANG, R. – ZHENG, X. – CHEN, Y. 2025. A vision model for automated frozen tuna processing. In Scientific Reports, vol. 15, article no. 3216. DOI: https://doi.org/10.1038/s41598-025-87339-3
- WANG, Y. – LIU, Q. – YANG, J. – REN, G. – WANG, W. – ZHANG, W. – LI, F. 2024. A method for tomato plant stem and leaf segmentation and phenotypic extraction based on skeleton extraction and supervoxel clustering. In Agronomy, vol. 14, no. 1, article no. 198. DOI: https://doi.org/10.3390/agronomy14010198
- WANG, X. – HAN, X. – MAO, H. 2012. Vision-based detection of tomato main stem in greenhouse with red rope. In Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), vol. 28, no. 21, pp. 135–141.
- YANG, Y. – CHEN, J. – SUN, L. – ZHOU, Z. – HUANG, Z. – WU, B. 2024. Unsupervised domain-adaptive SAR ship detection based on cross-domain feature interaction and data contribution balance. In Remote Sensing, vol. 16, no. 2, article no. 420. DOI: https://doi.org/10.3390/rs16020420
- YUE, X. – QI, K. – NA, X. – ZHANG, Y. – LIU, Y. – LIU, C. 2023. Improved YOLOv8-Seg network for instance segmentation of healthy and diseased tomato plants in the growth stage. In Agriculture, vol. 13, no. 8, article no. 1643. DOI: https://doi.org/10.3390/agriculture13081643
- XIAO, F. – WANG, H. – LI, Y. – CAO, Y. – LV, X. – XU, G. 2023. Object detection and recognition techniques based on digital image processing and traditional machine learning for fruit and vegetable harvesting robots: An overview and review. In Agronomy, vol. 13, no. 3, article no. 639. DOI: https://doi.org/10.3390/agronomy13030639
- XIANG, R. – ZHANG, M. – ZHANG, J. 2022. Recognition for stems of tomato plants at night based on a hybrid joint neural network. In Agriculture, vol. 12, no. 6, article no. 743. DOI: https://doi.org/10.3390/agriculture12060743
- XU, M. – YOON, S. – FUENTES, A. – PARK, D. S. 2023. A comprehensive survey of image augmentation techniques for deep learning. In Pattern Recognition, vol. 137, article no. 109347. DOI: https://doi.org/10.1016/j.patcog.2023.109347