https://doi.org/10.4081/jae.2024.1654
AC-YOLO: citrus detection in the natural environment of orchards
Published: 27 December 2024
Abstract Views: 114
PDF: 52
HTML: 0
HTML: 0
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
In the natural environment, the shape and color of fruits can vary greatly due to various factors, and the growth of fruits is irregular, shaded by leaves and branches, and there are phenomena such as overlapping fruits. The complex background causes the difficulty of fruit recognition by the picking robot to increase, which affects the positioning of subsequent picking points, greatly increasing the difficulty of picking, and even causing damage to the end effector. To address such issues, this study adopts panoramic photography to capture images of citrus fruit trees, and proposes an AC-YOLO based citrus recognition method in the natural environment of orchards. Firstly, in the Resblock module of the YOLOv4 backbone feature extraction network, the AC network structure is integrated with different levels of feature mapping to fuse context information as small targets. At the same time, a self-attention mechanism is introduced to suppress the impact of complex backgrounds and underlying noise, improving the detection ability of small target citrus; Finally, the Mish activation function is used to replace Leaky Re LU, improving the generalization ability of the model and improving the accuracy of citrus detection. The panorama image is divided into sub images, and an improved YOLOV4 model is used for recognition. By comparing the orange recognition effects of different network models such as Fast R-CNN, Center Net, YOLOV4 series algorithms, and YOLOV5 series algorithms on the panorama image, the improved YOLOV4 network model has an accuracy rate of 96.19%, a recall rate of 95.47%, and an average accuracy of 97.27%, Compared with the original YOLOv4 model, it has increased by 1.07, 2.59, and 2.02 percentage points respectively. This method has a good recognition effect for citrus in the natural environment of orchards.
Alaaudeen, K.M., Selvarajan, S., Manoharan, H., Jhaveri, R.H. 2024. Intelligent robotics harvesting system process for fruits grasping prediction. Sci. Rep. 14:2820.
DOI: https://doi.org/10.1038/s41598-024-52743-8
Bac, C.W., Roorda, T., Reshef, R., Berman, S., Hemming, J., van Henten E.J. 2016. Analysis of a motion planning problem for sweet-pepper harvesting in a dense obstacle environment. Biosyst. Eng. 146:85-97.
DOI: https://doi.org/10.1016/j.biosystemseng.2015.07.004
Bai, Y., Zhang, B., Xu, N., Zhou, J., Shi, J., Diao, Z. 2023. Vision-based navigation and guidance for agricultural autonomous vehicles and robots: A review. Comput. Electron. Agr. 205:107-124.
DOI: https://doi.org/10.1016/j.compag.2022.107584
Barbashov, N.N., Shanygin, S.V., Barkova, A.A. 2022. Agricultural robots for fruit harvesting in horticulture application. IOP C. Ser. Earth Environ. 981:9-32.
DOI: https://doi.org/10.1088/1755-1315/981/3/032009
Chen, C., Lu, J., Zhou, M., Yi, J., Liao, M. Gao, Z. 2022. A YOLOv3-based computer vision system for identification of tea buds and the picking point. Comput. Electron. Agr. 198:107-116.
DOI: https://doi.org/10.1016/j.compag.2022.107116
Du, X., Han, X., Shen, T., Meng, Z., Chen, K., Yao, X., et al. 2024. Natural frequency identification model based on BP neural network for Camellia oleifera fruit harvesting. Biosyst. Eng. 237:8-49.
DOI: https://doi.org/10.1016/j.biosystemseng.2023.11.012
Fang, Z., Liang, X. 2022. Intelligent obstacle avoidance path planning method for picking manipulator combined with artificial potential field method. Ind. Robot 49:835-850.
DOI: https://doi.org/10.1108/IR-09-2021-0194
Guo, Y., Dong, H., Wang, G., Ke, Y. 2018. A robotic boring system for intersection holes in aircraft assembly. Ind. Robot 4:28-336.
DOI: https://doi.org/10.1108/IR-09-2017-0176
Guo, Z., Shi, Y., Ahmad, I. 2024. Design of smart citrus picking model based on Mask RCNN and adaptive threshold segmentation. PeerJ Comput. Sci. 10:e1865.
DOI: https://doi.org/10.7717/peerj-cs.1865
Hannan, M.W., Burks, T.F., Bulanon, D.M. 2009. A machine vision algorithm combining adaptive segmentation and shape analysis for orange fruit detection. CIGR J. 6:1-17.
Jana, S., Basak, S., Parekh, R. 2017. Automatic fruit recognition from natural images using color and texture features. IEEE Conf. Devices for Integrated Circuit, Kalyani. pp. 620-624.
DOI: https://doi.org/10.1109/DEVIC.2017.8074025
Ju, C., Kim, J., Seol, J., Son, H.I., 2022. A review on multirobot systems in agriculture. Comput. Electron. Agr. 202:107-136.
DOI: https://doi.org/10.1016/j.compag.2022.107336
Krizhevsky, A., Sutskever, I., Hinton, G.E. 2012. ImageNet classification with deep convolutional neural networks. Proc. Advances in Neural Information Processing Systems 25:1097-1105.
Liu, J., Liu, Z. 2024. The vision-based target recognition, localization, and control for harvesting robots: a review. Int. J. Precis. Eng. Manuf. 25:409-428.
DOI: https://doi.org/10.1007/s12541-023-00911-7
Liu, M., Bian, Y., Liu, Q., Wang, X., Wang, Y. 2024a. Weakly supervised tracklet association learning with video labels for person re-identification. IEEE Trans. Pattern. Anal. Mach. Intell. 46:3595-3607.
DOI: https://doi.org/10.1109/TPAMI.2023.3346168
Liu, M., Wang, F., Wang, X., Wang, Y., Roy-Chowdhury, A.K. 2024b. A two-stage noise-tolerant paradigm for label corrupted person re-identification. IEEE Trans. Pattern. Anal. Mach. Intell. 46:4944-4956.
DOI: https://doi.org/10.1109/TPAMI.2024.3361491
Luo, K., Zhang, X., Cao, C., Wu, Z., Qin, K., et al. 2023. Continuous identification of the tea shoot tip and accurate positioning of picking points for a harvesting from standard plantations. Front. Plant Sci. 14:1211279.
DOI: https://doi.org/10.3389/fpls.2023.1211279
Lv, J., Xu, H., Xu, L., Zou, L., Rong, H., Yang, B., et al. 2022. Recognition of fruits and vegetables with similar‐color background in natural environment: A survey. J. Field Robot. 39:888-904.
DOI: https://doi.org/10.1002/rob.22074
Mai, X., Zhang, H., Jia, X., Meng, M.Q-H., 2020. Faster R-CNN with classifier fusion for automatic detection of small fruits. IEEE T. Autom. Sci. Eng. 17:1555-1569.
DOI: https://doi.org/10.1109/TASE.2020.2964289
Nehme, H., Aubry, C., Solatges, T., Savatier, X., Rossi, R., Boutteau, R. 2021. Lidar-based structure tracking for agricultural robots: Application to autonomous navigation in vineyards. J. Intell. Robot. Syst. 103:61.
DOI: https://doi.org/10.1007/s10846-021-01519-7
Vasconez, J.P, Kantor, G.A, Cheein, F.A.A. 2019. Human-robot interaction in agriculture: A survey and current challenges. Biosyst. Eng. 179:35-48.
DOI: https://doi.org/10.1016/j.biosystemseng.2018.12.005
Wan, S., Goudos, S. 2020. Faster R-CNN for multi-class fruit detection using a robotic vision system. Comput. Netw. 168:107-126.
DOI: https://doi.org/10.1016/j.comnet.2019.107036
Wang, L., Wang, Z., Liu, M., Ying, Z., Xu, N., Mdeng, Q. 2022. Full coverage path planning methods of harvesting robot with multi-objective constraints. J. Intell. Robot. Syst. 106:17.
DOI: https://doi.org/10.1007/s10846-022-01722-0
Wang, Y., He, Z., Cao, D., Ma, L., Li, K., Jia, L., Cui, Y. 2023a. Coverage path planning for kiwifruit picking robots based on deep reinforcement learning. Comput. Electron. Agr. 205:107593.
DOI: https://doi.org/10.1016/j.compag.2022.107593
Wang, Y., Wu, H., Zhu, Z., Ye, Y., Qian, M. 2023b. Continuous picking of yellow peaches with recognition and collision-free path. Comput. Electron. Agr. 214:108273.
DOI: https://doi.org/10.1016/j.compag.2023.108273
Xiao, X., Huang, J., Li, M., Xu, Y., Zhang, H., Wen, C., Dai, S. 2022. Fast recognition method for citrus under complex environments based on improved YOLOv3. J. Eng. 2022:148-159.
DOI: https://doi.org/10.1049/tje2.12097
Xiao, X., Jiang, Y., Wang, Y. 2024. A method of robot picking citrus based on 3D detection. IEEE Instru. Meas. Mag. 27:50-58.
DOI: https://doi.org/10.1109/MIM.2024.10505191
Xu, R., Li, C. 2022. A modular agricultural robotic system (MARS) for precision farming: concept and implementation. J. Field Robot. 39:387-409.
DOI: https://doi.org/10.1002/rob.22056
Zhang, Q, Su, H-W. 2023. Real-time recognition and localization of apples for robotic picking based on structural light and deep learning. Smart Cities (Basel) 6:3393-3410.
DOI: https://doi.org/10.3390/smartcities6060150
Zheng, X., Rong, J., Zhang, Z., Yang, Y., Li, W., Yuan, T. 2024. Fruit growing direction recognition and nesting grasping strategies for tomato harvesting robots. J. Field Robot. 41:300-313.
DOI: https://doi.org/10.1002/rob.22263
Zhuang, J.J, Luo, S.M., Hou, C.J., Tang, Y., He, Y., Zue, X.Y. 2018. Detection of orchard citrus fruits using a monocular machine vision-based method for automatic fruit picking applications. Comput. Electron. Agr. 152:64-73.
DOI: https://doi.org/10.1016/j.compag.2018.07.004
Supporting Agencies
National Key Research and Development Program of China, National Natural Science Foundation of China, National Natural Science Foundation of Hunan Province, China Postdoctoral Science Foundation, China Scholarship CouncilHow to Cite
Xiao, X. (2024) “AC-YOLO: citrus detection in the natural environment of orchards”, Journal of Agricultural Engineering, 55(4). doi: 10.4081/jae.2024.1654.
Copyright (c) 2024 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Ileana Blanco, Evelia Schettini, Giacomo Scarascia Mugnozza, Giuliano Vox, Thermal behaviour of green façades in summer , Journal of Agricultural Engineering: Vol. 49 No. 3 (2018)
- Daniele Sarri, Luisa Martelloni, Marco Rimediotti, Riccardo Lisci, Stefania Lombardo, Marco Vieri, Testing a multi-rotor unmanned aerial vehicle for spray application in high slope terraced vineyard , Journal of Agricultural Engineering: Vol. 50 No. 1 (2019)
- Salvatore Faugno, Stefania Pindozzi, Roberta Infascelli, Collins Okello, Maria Nicolina Ripa, Lorenzo Boccia, Assessment of nitrogen content in buffalo manure and land application costs , Journal of Agricultural Engineering: Vol. 43 No. 2 (2012)
- Michele Carrara, Pietro Catania, Giuseppe Lo Re, Marco Ortolani, Mariangela Vallone, AN INNOVATIVE SYSTEM FOR VINEYARD MANAGEMENT IN SICILY , Journal of Agricultural Engineering: Vol. 41 No. 1 (2010)
- Marco Manzone, Bruno Ruffinengo, Performance of a snow blower prototype mounted on different vehicles type , Journal of Agricultural Engineering: Vol. 50 No. 1 (2019)
- Vincenzo Bagarello, Giuseppe Basile, Gaetano Caltabellotta, Giuseppe Giordano, Massimo Iovino, Testing soil water repellency in a Sicilian area two years after a fire , Journal of Agricultural Engineering: Vol. 51 No. 2 (2020)
- Costanza Di Stefano, Vito Ferro, Stefano Mirabile, TESTING THE GRAIN-SIZE DISTRIBUTION DETERMINED BY LASER DIFFRACTOMETRY FOR SICILIAN SOILS , Journal of Agricultural Engineering: Vol. 42 No. 3 (2011)
- Andrea Petroselli, Rodolfo Piscopia, Salvatore Grimaldi, Design discharge estimation in small and ungauged basins: EBA4SUB framework sensitivity analysis , Journal of Agricultural Engineering: Vol. 51 No. 2 (2020)
- Vincenzo Bagarello, Costanza Di Stefano, Vito Ferro, Giordano Giuseppe, Massimo Iovino, A PEDOTRANSFER FUNCTION FOR ESTIMATING THE SOIL ERODIBILITY FACTOR IN SICILY , Journal of Agricultural Engineering: Vol. 40 No. 3 (2009)
- Yevgeny Beiderman, Mark Kunin, Eli Kolberg, Ilan Halachmi, Binyamin Abramov, Rafael Amsalem, Zeev Zalevsky, Automatic solution for detection, identification and biomedical monitoring of a cow using remote sensing for optimised treatment of cattle , Journal of Agricultural Engineering: Vol. 45 No. 4 (2014)
<< < 30 31 32 33 34 35 36 37 38 39 > >>
You may also start an advanced similarity search for this article.
