https://doi.org/10.4081/jae.2024.1594

Grape detection in natural environment based on improved YOLOv8 network

Early Access
Published: 20 August 2024
Automatic grape picking, disadvantages-enhance, EMA, grape detection, smart agriculture, YOLOv8
Abstract Views: 134
PDF: 76
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Authors

Meng Junjie
Anhui Provincial Engineering Laboratory of Intelligent Agricultural Machinery, School of Engineering, Anhui Agriculture University, Hefei, Anhui, China.
Cao Ziang
Anhui Provincial Engineering Laboratory of Intelligent Agricultural Machinery, School of Engineering, Anhui Agriculture University, Hefei, Anhui, China.
Guo Dandan
Anhui Provincial Engineering Laboratory of Intelligent Agricultural Machinery, School of Engineering, Anhui Agriculture University, Hefei, Anhui, China.
Wang Yuwei
Anhui Provincial Engineering Laboratory of Intelligent Agricultural Machinery, School of Engineering, Anhui Agriculture University, Hefei, Anhui, China.
Zhang Dashan
Anhui Provincial Engineering Laboratory of Intelligent Agricultural Machinery, School of Engineering, Anhui Agriculture University, Hefei, Anhui, China.
Liu Bingyou
Key Laboratory of Electric Drive and Control of Anhui Province, Anhui Polytechnic University, Wuhu, Ahhui, China.
Hou Wenhui
hwh303@ahau.edu.cn
Anhui Provincial Engineering Laboratory of Intelligent Agricultural Machinery, School of Engineering, Anhui Agriculture University, Hefei, Anhui, China.

In the pursuit of intelligent and efficient grape picking, rapid and precise detection of grape locations serves as the fundamental cornerstone. However, amidst the natural environment, grape detection encounters various interference factors, such as fluctuating light intensity, grape leaf obstructions, and grape overlap, all of which can undermine detection accuracy. To address these challenges, this study proposes a grape detection method leveraging an enhanced YOLOv8 network, wherein the conventional CIoU is replaced with Wise-IoU to augment network precision. Additionally, an efficient multi-scale attention module is introduced to heighten the network's focus on grapes. To expedite detection, the original network backbone is substituted with the CloFormer_xxs network. The collected grape images undergo preprocessing to ensure image quality, forming the basis of the dataset. Furthermore, the dataset is augmented using disadvantages-enhance, a novel data enhancement mode, thereby enhancing the robustness and generalization of network. The comprehensive comparison and ablation experiments are conducted to demonstrate the advantageous effects of the proposed modules on the network. Subsequently, the improved network's superiority in grape detection is validated through comparative analyses with other networks, showcasing superior accuracy and faster detection speeds. The network achieves a remarkable accuracy of 92.1%, average accuracy of 94.7%, with preprocessing and post-processing times of 15ms and 0.8ms, respectively. Consequently, the enhanced network presented in this study offers a viable solution for facilitating intelligent and efficient grape picking operations.

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Google Scholar
Europe PMC
Abdullahi H. S., Sheriff R., & Mahieddine F. 2017, August. Convolution neural network in precision agriculture for plant image recognition and classification. In 2017 Seventh International Conference on Innovative Computing Technology (INTECH) (Vol. 10, pp. 256-272). New York: Ieee.
Bochkovskiy A., Wang C. Y., & Liao H. Y. M. 2020. Yolov4: Optimal speed and accuracy of object detection. arxiv preprint arxiv: 2004. 10934.
Castro W., Oblitas J., De-La-Torre M., Cotrina C., Bazán K., & Avila-George, H. 2019. Classification of cape gooseberry fruit according to its level of ripeness using machine learning techniques and different color spaces. IEEE access, 7, 27389-27400.
Fu L., Duan J., Zou X., Lin G., Song S., Ji B., & Yang, Z. 2019. Banana detection based on color and texture features in the natural environment. Computers and Electronics in Agriculture, 167, 105057.
Fan Q., Huang H., Guan J., & He R. 2023. Rethinking local perception in lightweight vision transformer. arxiv preprint arxiv:2303.17803.
Gao F., Fu L., Zhang X., Majeed Y., Li R., Karkee M., & Zhang Q. 2020. Multi-class fruit-on-plant detection for apple in SNAP system using Faster R-CNN. Computers and Electronics in Agriculture, 176, 105634.
Girshick R., Donahue J., Darrell T., & Malik, J. 2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580-587).
Girshick R. 2015. Fast r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 1440-1448).
Hubel D. H., & Wiesel T. N. 1962. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. The Journal of physiology, 160(1), 106.
He K., Gkioxari G., Dollár P., & Girshick R. 2017. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 2961-2969).
Ji X., Dong Z., Han Y., Lai C. S., & Qi, D. 2023. A brain-inspired hierarchical interactive in-memory computing system and its application in video sentiment analysis. IEEE transactions on circuits and systems for video technology.
Jocher G. yolov5. Git code. 2020. [accessed 19 Sep 2022] https://github.com/ultralytics/yolov5
Lu J., & Sang N. 2015. Detecting citrus fruits and occlusion recovery under natural illumination conditions. Computers and Electronics in Agriculture, 110, 121-130.
Liu W., Anguelov D., Erhan D., Szegedy C., Reed S., Fu C. Y., & Berg A. C. 2016. Ssd: Single shot multibox detector. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14 (pp. 21-37). Springer International Publishing.
Le T. T., & Lin C. Y. 2019. Deep learning for noninvasive classification of clustered horticultural crops–A case for banana fruit tiers. Postharvest Biology and Technology, 156, 110922.
Ouyang D., He S., Zhang G., Luo M., Guo H., Zhan, J., & Huang Z. 2023, June. Efficient multi-scale attention module with cross-spatial learning. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
Pothen Z. S., & Nuske S. 2016, May. Texture-based fruit detection via images using the smooth patterns on the fruit. In 2016 IEEE international conference on robotics and automation (ICRA) (pp. 5171-5176). IEEE.
Patel H N , Jain R K , Joshi M V , et al. Fruit Detection using Improved Multiple Features based Algorithm[J]. International Journal of Computer Applications, 2011, 13(2): 1-5.
Rabby M. K. M., Chowdhury B., & Kim J. H. 2018, December. A modified canny edge detection algorithm for fruit detection & classification. In 2018 10th international conference on electrical and computer engineering (ICECE) (pp. 237-240). IEEE.
Ren S., He K., Girshick R., & Sun J. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28.
Redmon J., Divvala S., Girshick R., & Farhadi A. 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).
Redmon J., & Farhadi A. 2017. YOLO9000: better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7263-7271).
Redmon J., & Farhadi A. 2018. Yolov3: An incremental improvement. arxiv preprint arxiv: 1804.02767.
Sheridan T. B. 2016. Human–robot interaction: status and challenges. Human factors, 58(4), 525-532. DOI: 10.1177/0018720816644364
Tian Y., Chen G., Li J., **ang X., Liu Y., & Li H. Y. 2018. Present development of grape industry in the world. Chin. J. Trop. Agric, 38, 96-105.
Tian Y., Yang G., Wang Z., Wang H., Li E., & Liang Z. 2019. Apple detection during different growth stages in orchards using the improved YOLO-V3 model. Computers and electronics in agriculture, 157, 417-426.
Tong Z., Chen Y., Xu Z, et al. Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism[J]. arXiv2023: 2301. 10051.
Van Henten E. J., Schenk E. J., Van Willigenburg L. G., Meuleman J., & Barreiro P. 2010. Collision-free inverse kinematics of the redundant seven-link manipulator used in a cucumber picking robot. Biosystems engineering, 106(2), 112-124.
Wouter Bac C., Van Henten E. J., Hemming J., & Edan Y. 2014. Harvesting robots for high-value crops: state-of-the-art review and challenges ahead. J. Field Robot, 31, 888-911.
Wang J., Chen Y., Ji, X., Dong Z., Gao M., & Lai, C. S. 2023. Vehicle-mounted adaptive traffic sign detector for small-sized signs in multiple working conditions. IEEE Transactions on Intelligent Transportation Systems.
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 (pp. 7464-7475).
Yan B., Fan, P., Lei, X., Liu, Z., & Yang, F. 2021. A real-time apple targets detection method for picking robot based on improved YOLOv5. Remote Sensing, 13(9), 1619.
Zhuang J. J., Luo S. M., Hou C. J., Tang Y., He Y., & Xue X. Y. 2018. Detection of orchard citrus fruits using a monocular machine vision-based method for automatic fruit picking applications. Computers and Electronics in Agriculture, 152, 64-73.
Zeeshan M., Prabhu A., Arun C., & Rani, N. S. 2020, July. Fruit classification system using multiclass support vector machine classifier. In 2020 international conference on electronics and sustainable communication systems (ICESC) (pp. 289-294). IEEE.

How to Cite

Junjie, M. (2024) “Grape detection in natural environment based on improved YOLOv8 network”, Journal of Agricultural Engineering. doi: 10.4081/jae.2024.1594.
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