Smart Agriculture ›› 2023, Vol. 5 ›› Issue (2): 1-12.doi: 10.12133/j.smartag.SA202305004
• Topic--Machine Vision and Agricultural Intelligent Perception • Next Articles
XIA Xue1(), CHAI Xiujuan1, ZHANG Ning1(), ZHOU Shuo1, SUN Qixin1, SUN Tan2()
Received:
2023-05-11
Online:
2023-06-30
corresponding author:
1. ZHANG Ning, E-mail:zhangning@caas.cn;
2. SUN Tan, E-mail:suntan@caas.cn
About author:
XIA Xue, E-mail:xiaxue@caas.cn
Supported by:
CLC Number:
XIA Xue, CHAI Xiujuan, ZHANG Ning, ZHOU Shuo, SUN Qixin, SUN Tan. A Lightweight Fruit Load Estimation Model for Edge Computing Equipment[J]. Smart Agriculture, 2023, 5(2): 1-12.
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URL: https://www.smartag.net.cn/EN/10.12133/j.smartag.SA202305004
1 | FENG A J, ZHOU J F, VORIES E D, et al. Yield estimation in cotton using UAV-based multi-sensor imagery[J]. Biosystems engineering, 2020, 193: 101-114. |
2 | KURTULMUS F, LEE W S, VARDAR A. Green citrus detection using 'eigenfruit', color and circular Gabor texture features under natural outdoor conditions[J]. Computers and electronics in agriculture, 2011, 78(2): 140-149. |
3 | QURESHI W S, PAYNE A, WALSH K B, et al. Machine vision for counting fruit on mango tree canopies[J]. Precision agriculture, 2017, 18(2): 224-244. |
4 | ZHOU R, DAMEROW L, SUN Y R, et al. Using colour features of cv. 'Gala' apple fruits in an orchard in image processing to predict yield[J]. Precision agriculture, 2012, 13(5): 568-580. |
5 | ANNAMALAI P, LEE W S. Citrus yield mapping system using machine vision[C]// 2003, Las Vegas, NV July 27-30, 2003. St. Joseph, MI, USA: American Society of Agricultural and Biological Engineers, 2003: 1. |
6 | STAJNKO D, RAKUN J, BLANKE M. Modelling apple fruit yield using image analysis for fruit colour, shape and texture[J]. European journal of horticultural science, 2009, 74(6): 260-267. |
7 | DORJ U O, LEE M, YUN S S. An yield estimation in citrus orchards via fruit detection and counting using image processing[J]. Computers and electronics in agriculture, 2017, 140: 103-112. |
8 | SA I, GE Z Y, DAYOUB F, et al. DeepFruits: A fruit detection system using deep neural networks[J]. Sensors, 2016, 16(8): ID 1222. |
9 | CHEN S W, SHIVAKUMAR S S, DCUNHA S, et al. Counting apples and oranges with deep learning: A data-driven approach[J]. IEEE robotics and automation letters, 2017, 2(2): 781-788. |
10 | BARGOTI S, UNDERWOOD J. Deep fruit detection in orchards[C]// 2017 IEEE International Conference on Robotics and Automation (ICRA). Piscataway, NJ, USA: IEEE, 2017: 3626-3633. |
11 | HÄNI N, ROY P, ISLER V. A comparative study of fruit detection and counting methods for yield mapping in apple orchards[J]. Journal of field robotics, 2020, 37(2): 263-282. |
12 | 李志军, 杨圣慧, 史德帅, 等. 基于轻量化改进YOLOv5的苹果树产量测定方法[J]. 智慧农业(中英文), 2021, 3(2): 100-114. |
LI Z J, YANG S H, SHI D S, et al. Yield estimation method of apple tree based on improved lightweight YOLOv5[J]. Smart agriculture, 2021, 3(2): 100-114. | |
13 | KESTUR R, MEDURI A, NARASIPURA O. MangoNet: A deep semantic segmentation architecture for a method to detect and count mangoes in an open orchard[J]. Engineering applications of artificial intelligence, 2019, 77: 59-69. |
14 | 高芳芳, 武振超, 索睿, 等. 基于深度学习与目标跟踪的苹果检测与视频计数方法[J]. 农业工程学报, 2021, 37(21): 217-224. |
GAO F F, WU Z C, SUO R, et al. Apple detection and counting using real-time video based on deep learning and object tracking[J]. Transactions of the Chinese society of agricultural engineering, 2021, 37(21): 217-224. | |
15 | WANG Z L, WALSH K, KOIRALA A. Mango fruit load estimation using a video based MangoYOLO-kalman filter-hungarian algorithm method[J]. Sensors, 2019, 19(12): ID 2742. |
16 | LUO W H, XING J L, MILAN A, et al. Multiple object tracking: A literature review[J]. Artificial intelligence, 2021, 293: ID 103448. |
17 | RAKAI L, SONG H S, SUN S J, et al. Data association in multiple object tracking: A survey of recent techniques[J]. Expert systems with applications, 2022, 192: ID 116300. |
18 | 涂淑琴, 汤寅杰, 李承桀, 等. 基于改进ByteTrack算法的群养生猪行为识别与跟踪技术[J]. 农业机械学报, 2022, 53(12): 264-272. |
TU S Q, TANG Y J, LI C J, et al. Behavior recognition and tracking of group-housed pigs based on improved ByteTrack algorithm[J]. Transactions of the Chinese society for agricultural machinery, 2022, 53(12): 264-272. | |
19 | ZHANG Y F, WANG C Y, WANG X G, et al. FairMOT: On the fairness of detection and re-identification in multiple object tracking[J]. International journal of computer vision, 2021, 129(11): 3069-3087. |
20 | ZHANG Y F, SUN P Z, JIANG Y, et al. ByteTrack: Multi-object tracking by associating every detection box[C]// European conference on computer vision. Berlin, German: Springer, 2022: 1-21. |
21 | 吴昊. 基于YOLOX和重识别的行人多目标跟踪方法[J]. 自动化与仪表, 2023, 38(3): 59-62, 67. |
WU H. Pedestrian multi-target tracking method based on YOLOX and person re-identification[J]. Automation & instrumentation, 2023, 38(3): 59-62, 67. | |
22 | OUYANG W L, WANG X G, ZENG X Y, et al. DeepID-Net: Deformable deep convolutional neural networks for object detection[C]// 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ, USA: IEEE, 2015: 2403-2412. |
23 | REDMON J, FARHADI A. YOLOv3: An incremental improvement[EB/OL]. arXiv: , 2018. |
24 | WANG C Y, MARK LIAO H Y, WU Y H, et al. CSPNet: A new backbone that can enhance learning capability of CNN[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Piscataway, NJ, USA: IEEE, 2020: 1571-1580. |
25 | 韦锦, 李正强, 许恩永, 等. 基于DA2-YOLOv4算法绿篱识别研究[J]. 中国农机化学报, 2022, 43(9): 122-130. |
WEI J, LI Z Q, XU E Y, et al. Research on hedge recognition based on DA2-YOLOv4 algorithm[J]. Journal of Chinese agricultural mechanization, 2022, 43(9): 122-130. | |
26 | WANG C Y, BOCHKOVSKIY A, LIAO H Y M. Scaled-YOLOv4: Scaling cross stage partial network[C]// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ, USA: IEEE, 2021: 13024-13033. |
27 | GÜNEY E, BAYILMIŞ C, ÇAKAN B. An implementation of real-time traffic signs and road objects detection based on mobile GPU platforms[J]. IEEE access, 2022, 10: 86191-86203. |
28 | LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ, USA: IEEE, 2017: 936-944. |
29 | LIU S, QI L, QIN H F, et al. Path aggregation network for instance segmentation[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2018: 8759-8768. |
30 | 孙泽强, 陈炳才, 崔晓博, 等. 融合频域注意力机制和解耦头的YOLOv5带钢表面缺陷检测[J]. 计算机应用, 2023, 43(1): 242-249. |
SUN Z Q, CHEN B C, CUI X B, et al. Strip steel surface defect detection by YOLOv5 algorithm fusing frequency domain attention mechanism and decoupled head[J]. Journal of computer applications, 2023, 43(1): 242-249. |
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