Multi-class detection of kiwifruit flower and its distribution identification in orchard based on YOLOv5l and Euclidean distance

Computers and Electronics in Agriculture - Tập 201 - Trang 107342 - 2022
Guo Li1, Longsheng Fu1,2,3,4, Changqing Gao1, Wentai Fang1, Guanao Zhao1, Fuxi Shi1, Jaspreet Dhupia5, Kegang Zhao6, Rui Li1,4, Yongjie Cui1
1College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China
2Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China
3Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China
4Northwest A&F University Shenzhen Research Institute, Shenzhen, Guangdong 518000, China
5Department of Mechanical Engineering, The University of Auckland, Private bag, Auckland 92019, New Zealand
6Xi'an Agriculture Machinery Management and Extension Station, Xi'an 710065, China

Tài liệu tham khảo

Bochkovskiy, A., Wang, C., Liao, H.M., 2020. YOLOv4: Optimal speed and accuracy of object detection. arXiv Prepr. arXiv: 2004.10934.

Cangi, 2018, Effects of different bud loading levels on the yield, leaf and fruit characteristics of Hayward kiwifruit, Hortic. Sci., 33, 23, 10.17221/3736-HORTSCI

Castro, 2021, Pollination as a key management tool in crop production: Kiwifruit orchards as a study case, Sci. Hortic., 290, 110533, 10.1016/j.scienta.2021.110533

Dias, 2018, Apple flower detection using deep convolutional networks, Comput. Ind., 99, 17, 10.1016/j.compind.2018.03.010

Fu, 2021, Fast and accurate detection of kiwifruit in orchard using improved YOLOv3-tiny model, Precis. Agric., 22, 754, 10.1007/s11119-020-09754-y

Gao, 2020, Multi-class fruit-on-plant detection for apple in SNAP system using Faster R-CNN, Comput. Electron. Agric., 176, 105634, 10.1016/j.compag.2020.105634

Gianni, 2018, Artificial pollination in kiwifruit and olive trees, Pollinat. Plants

Gonzalez, 1995, Stigmatic receptivity limits the effective pollination period in kiwifruit, J. Am. Soc. Hortic. Sci., 120, 199, 10.21273/JASHS.120.2.199

Jocher, G., Stoken, A., Borovec, J., NanoCode012, ChristopherSTAN, Changyu, L., Laughing, Hogan, A., lorenzomammana, tkianai, yxNONG, AlexWang1900, Diaconu, L., Marc, wanghaoyang0106, ml5ah, Doug, Hatovix, Poznanski, J., L.Y., changyu98, Rai, P., Ferriday, R., Sullivan, T., Xinyu, W., YuriRibeiro, Claramunt, E.R., hopesala, pritul dave, yzchen, 2020. ultralytics/yolov5: v3.0. https://doi.org/10.5281/ZENODO.3983579.

Kuznetsova, 2020, Using YOLOv3 algorithm with pre- and post-processing for apple detection in fruit-harvesting robot, Agronomy, 10, 1016, 10.3390/agronomy10071016

Li, 2022, Real-time detection of kiwifruit flower and bud simultaneously in orchard using YOLOv4 for robotic pollination, Comput. Electron. Agric., 193, 106641, 10.1016/j.compag.2021.106641

Lim, J.Y., Ahn, H.S., Nejati, M., Bell, J., Williams, H., MacDonald, B.A., 2020. Deep neural network based real-time kiwi fruit flower detection in an orchard environment. arXiv Prepr. arXiv: 2006.04343.

Lin, 2021, Improved YOLO based detection algorithm for floating, Entropy., 23, 1111, 10.3390/e23091111

Lin, 2017, Feature pyramid networks for object detection, 936

Liu, S., Qi, L., Qin, H., Shi, J., Jia, J., 2018. PANet: Path Aggregation Network for Instance Segmentation. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake, pp. 8759–8768. doi: 10.1109/CVPR.2018.00913.

Majeed, 2020, Estimating the trajectories of vine cordons in full foliage canopies for automated green shoot thinning in vineyards, Comput. Electron. Agric., 176, 105671, 10.1016/j.compag.2020.105671

McPherson, 2001, Flower quality and fruit size in kiwifruit (Actinidia deliciosa), New Zeal. J. Crop Hortic. Sci., 29, 93, 10.1080/01140671.2001.9514167

Nepal, 2022, Comparing YOLOv3, YOLOv4 and YOLOv5 for autonomous landing spot detection in faulty UAVs, Sensors, 22, 464, 10.3390/s22020464

Redmon, J., Farhadi, A., 2018. YOLOv3: An incremental improvement. arXiv Prepr. arXiv: 1804.02767.

Salinero, 2009, Phenological growth stages of kiwifruit (Actinidia deliciosa ’Hayward’), Sci. Hortic. (Amsterdam), 121, 27, 10.1016/j.scienta.2009.01.013

Smith, 1994, Spatial analysis of the canopy of kiwifruit vines as it relates to the physical, chemical and postharvest attributes of the fruit, Ann. Bot., 10.1006/anbo.1994.1012

Song, 2021, Canopy segmentation and wire reconstruction for kiwifruit robotic harvesting, Comput. Electron. Agric., 181, 105933, 10.1016/j.compag.2020.105933

Suo, 2021, Improved multi-classes kiwifruit detection in orchard to avoid collisions during robotic picking, Comput. Electron. Agric., 182, 106052, 10.1016/j.compag.2021.106052

Thakur, 2004, Effect of thinning on fruit yield, size and quality of kiwifruit cv. allison, Acta Hortic., 662, 359, 10.17660/ActaHortic.2004.662.53

Tian, 2020, Instance segmentation of apple flowers using the improved mask R-CNN model, Biosyst. Eng., 193, 264, 10.1016/j.biosystemseng.2020.03.008

Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M., 2020a. Scaled-YOLOv4: Scaling cross stage partial network. arXiv Prepr. arXiv: 2011.08036v1.

Wang, C.Y., Mark Liao, H.Y., Wu, Y.H., Chen, P.Y., Hsieh, J.W., Yeh, I.H., 2020b. CSPNet: A new backbone that can enhance learning capability of CNN. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work. 2020-June, 1571–1580. https://doi.org/10.1109/CVPRW50498.2020.00203.

Wang, 2021, DeepPhenology: Estimation of apple flower phenology distributions based on deep learning, Comput. Electron. Agric., 185, 106123, 10.1016/j.compag.2021.106123

Williams, 2020, Autonomous pollination of individual kiwifruit flowers: Toward a robotic kiwifruit pollinator, J. F. Robot., 37, 246, 10.1002/rob.21861

Wu, 2022, Coefficient of restitution of kiwifruit without external interference, J. Food Eng., 327, 111060, 10.1016/j.jfoodeng.2022.111060

Wu, 2021, Segmentation of abnormal leaves of hydroponic lettuce based on DeepLabV3+ for robotic sorting, Comput. Electron. Agric., 190, 106443, 10.1016/j.compag.2021.106443

Xu, 2021, A forest fire detection system based on ensemble learning, Forests, 12, 1, 10.3390/f12020217

Zhang, 2020, Multi-class object detection using Faster R-CNN and estimation of shaking locations for automated shake-and-catch apple harvesting, Comput. Electron. Agric., 173, 105384, 10.1016/j.compag.2020.105384

Zhang, 2021, Computer vision-based tree trunk and branch identification and shaking points detection in Dense-Foliage canopy for automated harvesting of apples, J. F. Robot., 38, 476, 10.1002/rob.21998

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Computers and Electronics in Agriculture

Tập 201

107342

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