A mango picking vision algorithm on instance segmentation and key point detection from RGB images in an open orchard
The vision system of a fruit picking robot must perform two difficult tasks, the accurate pixel-wise instance segmentation of fruit and the correct detection of picking points. Due to the unsatisfactory performance of the vision system on these tasks, the picking performance of the robot is insuffic...
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| Veröffentlicht in: | Biosystems engineering Jg. 206; S. 32 - 54 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier Ltd
01.06.2021
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| ISSN: | 1537-5110, 1537-5129 |
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| Abstract | The vision system of a fruit picking robot must perform two difficult tasks, the accurate pixel-wise instance segmentation of fruit and the correct detection of picking points. Due to the unsatisfactory performance of the vision system on these tasks, the picking performance of the robot is insufficient and its application is not prevalent. In this study, performing fruit instance segmentation and the localization of picking points, a vision algorithm using RGB images was designed in a deep learning framework for a potential visual system of mango picking robots. The algorithm not only performed these two tasks successfully, but also integrated them into an end-to-end network, in which parallel branches performed the two tasks simultaneously. The average precision (at IoU of 0.75) and the average recall of instance segmentation reached 0.947 and 0.929 respectively, and the best precision and recall of picking-point detection reached 0.984 and 0.908 respectively. In addition, the tasks face various illumination and background interference in outdoor orchards, along with complex problems in terms of occlusion, overlap and object scale. In this study, the performance of the vision system was analysed in detail on several datasets and subsets of the various complex conditions, and the major factors that affect the performance were discussed. The results demonstrated that this system was robust against various illuminations and complex backgrounds, and yielded satisfactory segmentation and picking-point detection performances for minor and medium occlusion or overlap, and for medium and large mangoes. The model visualization and the influence analysis of model training demonstrated the training process and modelling effect of the deep learning network.
•Designed an end-to-end vision system for a mango picking robot.•Instance segmentation and pick point detection are performed simultaneously.•It is robust to various illumination and background interference scenarios.•Good performances in minor and medium occlusion, various overlap and object scale. |
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| AbstractList | The vision system of a fruit picking robot must perform two difficult tasks, the accurate pixel-wise instance segmentation of fruit and the correct detection of picking points. Due to the unsatisfactory performance of the vision system on these tasks, the picking performance of the robot is insufficient and its application is not prevalent. In this study, performing fruit instance segmentation and the localization of picking points, a vision algorithm using RGB images was designed in a deep learning framework for a potential visual system of mango picking robots. The algorithm not only performed these two tasks successfully, but also integrated them into an end-to-end network, in which parallel branches performed the two tasks simultaneously. The average precision (at IoU of 0.75) and the average recall of instance segmentation reached 0.947 and 0.929 respectively, and the best precision and recall of picking-point detection reached 0.984 and 0.908 respectively. In addition, the tasks face various illumination and background interference in outdoor orchards, along with complex problems in terms of occlusion, overlap and object scale. In this study, the performance of the vision system was analysed in detail on several datasets and subsets of the various complex conditions, and the major factors that affect the performance were discussed. The results demonstrated that this system was robust against various illuminations and complex backgrounds, and yielded satisfactory segmentation and picking-point detection performances for minor and medium occlusion or overlap, and for medium and large mangoes. The model visualization and the influence analysis of model training demonstrated the training process and modelling effect of the deep learning network. The vision system of a fruit picking robot must perform two difficult tasks, the accurate pixel-wise instance segmentation of fruit and the correct detection of picking points. Due to the unsatisfactory performance of the vision system on these tasks, the picking performance of the robot is insufficient and its application is not prevalent. In this study, performing fruit instance segmentation and the localization of picking points, a vision algorithm using RGB images was designed in a deep learning framework for a potential visual system of mango picking robots. The algorithm not only performed these two tasks successfully, but also integrated them into an end-to-end network, in which parallel branches performed the two tasks simultaneously. The average precision (at IoU of 0.75) and the average recall of instance segmentation reached 0.947 and 0.929 respectively, and the best precision and recall of picking-point detection reached 0.984 and 0.908 respectively. In addition, the tasks face various illumination and background interference in outdoor orchards, along with complex problems in terms of occlusion, overlap and object scale. In this study, the performance of the vision system was analysed in detail on several datasets and subsets of the various complex conditions, and the major factors that affect the performance were discussed. The results demonstrated that this system was robust against various illuminations and complex backgrounds, and yielded satisfactory segmentation and picking-point detection performances for minor and medium occlusion or overlap, and for medium and large mangoes. The model visualization and the influence analysis of model training demonstrated the training process and modelling effect of the deep learning network. •Designed an end-to-end vision system for a mango picking robot.•Instance segmentation and pick point detection are performed simultaneously.•It is robust to various illumination and background interference scenarios.•Good performances in minor and medium occlusion, various overlap and object scale. |
| Author | Chen, Changxin Zheng, Chan Tu, Shuqin Chen, Pengfei Pang, Jing Yang, Xiaofan Xue, Yueju |
| Author_xml | – sequence: 1 givenname: Chan surname: Zheng fullname: Zheng, Chan organization: College of Mathematics and Informatics, South China Agricultural University, China – sequence: 2 givenname: Pengfei surname: Chen fullname: Chen, Pengfei organization: College of Electronic Engineering, South China Agricultural University, China – sequence: 3 givenname: Jing surname: Pang fullname: Pang, Jing organization: College of Electronic Engineering, South China Agricultural University, China – sequence: 4 givenname: Xiaofan surname: Yang fullname: Yang, Xiaofan organization: College of Electronic Engineering, South China Agricultural University, China – sequence: 5 givenname: Changxin surname: Chen fullname: Chen, Changxin organization: College of Electronic Engineering, South China Agricultural University, China – sequence: 6 givenname: Shuqin surname: Tu fullname: Tu, Shuqin organization: College of Mathematics and Informatics, South China Agricultural University, China – sequence: 7 givenname: Yueju surname: Xue fullname: Xue, Yueju email: xueyueju@163.com organization: College of Electronic Engineering, South China Agricultural University, China |
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| Cites_doi | 10.1002/rob.21525 10.1109/ACCESS.2019.2917620 10.1023/B:VISI.0000029664.99615.94 10.3390/s16081222 10.1002/rob.21715 10.1007/978-3-319-10602-1_48 10.1016/j.compag.2018.06.007 10.1016/j.compag.2018.12.041 10.1016/j.biosystemseng.2017.11.005 10.1002/rob.21876 10.3390/s19204599 10.1016/j.compag.2018.02.016 10.3390/s18040969 10.13031/aea.32.10701 10.1016/j.compag.2016.06.022 10.1016/j.compag.2015.05.021 10.1007/s11119-019-09642-0 10.3390/s17040905 10.1016/j.engappai.2018.09.011 10.1504/IJCVR.2012.046419 10.1016/j.compind.2018.03.017 10.1016/j.compag.2019.01.012 10.1016/j.biosystemseng.2019.03.007 10.1016/j.compag.2019.04.017 10.1002/rob.21902 10.1016/j.compag.2012.11.009 10.1016/j.biosystemseng.2016.08.026 10.1002/rob.21699 10.1016/j.scienta.2019.01.033 10.1002/rob.21889 |
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| Keywords | Instance segmentation Fruit picking robot Occlusion and overlap End-to-end framework Picking-point detection Complex scene |
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| References | Häni, Roy, Isler (bib11) 2018 Koirala, Walsh, Wang, McCarthy (bib20) 2019; 162 Bac, van Henten, Hemming, Edan (bib1) 2014; 31 He, Zhang, Ren, Sun (bib14) 2016 Liu, Anguelov, Erhan, Szegedy, Reed (bib26) 2015 Litz (bib25) 2009 Payne, Walsh, Subedi, Jarvis (bib34) 2013; 91 Luo, Tang, Zou, Ye, Feng, Li (bib31) 2016; 151 Bargoti, Underwood (bib2) 2017 Liu, Chen, Aditya, Sivakumar, Dcunha, Qu, Kumar (bib27) 2018 Zhao, Gong, Huang, Liu (bib52) 2016; 127 Rahnemoonfar, Sheppard (bib35) 2017; 17 Kang, Chen (bib17) 2019; 19 Ren, He, Girshick, Sun (bib38) 2015 Chen, Skandan, Dcunha, Das, Okon, Qu, Kumar (bib5) 2017 Gong, Zhong, Hu (bib54) 2019; 7 He, Gkioxari, Dollár, Girshick (bib13) 2017 Gutiérrez, Wendel, Underwood (bib10) 2019; 157 Dalal, Triggs (bib6) 2005 Kestur, Meduri, Narasipura (bib19) 2019; 77 Roy, Isler (bib40) 2017 Xiong, Ge, Grimstad, From (bib46) 2020; 37 Williams, Jones, Nejati, Seabright, Bell, Penhall, MacDonald (bib45) 2019; 181 Xiong, Lin, Liu, He, Tang, Yang (bib48) 2018; 166 Redmon, Farhadi (bib37) 2018 Bargoti, Underwood (bib3) 2017; 34 Koirala, Walsh, Wang, McCarthy (bib21) 2019 Edan, Han, Kondo (bib8) 2009 Zou, Ye, Luo, Xiong, Luo, Wang (bib53) 2016; 32 Roy, Dong, Isler (bib39) 2018 Sa, Ge, Dayoub, Upcroft, Perez, McCool (bib41) 2016; 16 Ntsoane, Zude-Sasse, Mahajan, Sivakumar (bib33) 2019; 249 Redmon, Divvala, Girshick, Farhadi (bib36) 2015 Bay, Tuytelaars, Van Gool (bib4) 2006 Gongal, Amatya, Karkee, Zhang, Lewis (bib9) 2015; 116 Lin, Dollár, Girshick, He, Hariharan, Belongie (bib22) 2017 Xiong, He, Lin, Liu, Bu, Yang, Zou (bib47) 2018; 151 Kamilaris, Prenafeta-Boldú (bib16) 2018; 147 Häni, Roy, Isler (bib12) 2020; 37 Li, Qi, Dai, Ji, Wei (bib24) 2017 Newell, Yang, Deng (bib32) 2016 Long, Shelhamer, Darrell (bib28) 2015 Lin, Maire, Belongie, Hays, Perona, Ramanan, Zitnick (bib23) 2014; 8693 Kapach, Barnea, Mairon, Edan, Ben-Shahar (bib18) 2012; 3 Szegedy, Ioffe, Vanhoucke, Alemi (bib43) 2017 Lowe (bib29) 2004; 60 Tian, Yang, Wang, Wang, Li, Liang (bib44) 2019; 157 Silwal, Davidson, Karkee, Mo, Zhang, Lewis (bib42) 2017; 34 Xiong, Liu, Lin, Bu, He, Yang (bib49) 2018; 18 Luo, Tang, Lu, Chen, Zhang, Zou (bib30) 2018; 99 Zhang, Kjellstrom, Mandt (bib51) 2017 Yan, Xiong, Lin (bib50) 2018 Dong, Roy, Isler (bib7) 2020; 37 Huang, Wane, Parsons (bib15) 2017 Häni (10.1016/j.biosystemseng.2021.03.012_bib12) 2020; 37 Ntsoane (10.1016/j.biosystemseng.2021.03.012_bib33) 2019; 249 Kapach (10.1016/j.biosystemseng.2021.03.012_bib18) 2012; 3 Bay (10.1016/j.biosystemseng.2021.03.012_bib4) 2006 Williams (10.1016/j.biosystemseng.2021.03.012_bib45) 2019; 181 Roy (10.1016/j.biosystemseng.2021.03.012_bib39) 2018 Lin (10.1016/j.biosystemseng.2021.03.012_bib22) 2017 Liu (10.1016/j.biosystemseng.2021.03.012_bib27) 2018 Ren (10.1016/j.biosystemseng.2021.03.012_bib38) 2015 Gutiérrez (10.1016/j.biosystemseng.2021.03.012_bib10) 2019; 157 Newell (10.1016/j.biosystemseng.2021.03.012_bib32) 2016 Gong (10.1016/j.biosystemseng.2021.03.012_bib54) 2019; 7 Chen (10.1016/j.biosystemseng.2021.03.012_bib5) 2017 Zou (10.1016/j.biosystemseng.2021.03.012_bib53) 2016; 32 Dalal (10.1016/j.biosystemseng.2021.03.012_bib6) 2005 Xiong (10.1016/j.biosystemseng.2021.03.012_bib49) 2018; 18 Li (10.1016/j.biosystemseng.2021.03.012_bib24) 2017 Bargoti (10.1016/j.biosystemseng.2021.03.012_bib3) 2017; 34 Gongal (10.1016/j.biosystemseng.2021.03.012_bib9) 2015; 116 Luo (10.1016/j.biosystemseng.2021.03.012_bib31) 2016; 151 Xiong (10.1016/j.biosystemseng.2021.03.012_bib47) 2018; 151 Koirala (10.1016/j.biosystemseng.2021.03.012_bib21) 2019 Kamilaris (10.1016/j.biosystemseng.2021.03.012_bib16) 2018; 147 Lin (10.1016/j.biosystemseng.2021.03.012_bib23) 2014; 8693 Koirala (10.1016/j.biosystemseng.2021.03.012_bib20) 2019; 162 Silwal (10.1016/j.biosystemseng.2021.03.012_bib42) 2017; 34 Redmon (10.1016/j.biosystemseng.2021.03.012_bib37) 2018 Roy (10.1016/j.biosystemseng.2021.03.012_bib40) 2017 He (10.1016/j.biosystemseng.2021.03.012_bib14) 2016 Long (10.1016/j.biosystemseng.2021.03.012_bib28) 2015 Kestur (10.1016/j.biosystemseng.2021.03.012_bib19) 2019; 77 Dong (10.1016/j.biosystemseng.2021.03.012_bib7) 2020; 37 Edan (10.1016/j.biosystemseng.2021.03.012_bib8) 2009 Luo (10.1016/j.biosystemseng.2021.03.012_bib30) 2018; 99 Zhao (10.1016/j.biosystemseng.2021.03.012_bib52) 2016; 127 Redmon (10.1016/j.biosystemseng.2021.03.012_bib36) 2015 Liu (10.1016/j.biosystemseng.2021.03.012_bib26) 2015 Bac (10.1016/j.biosystemseng.2021.03.012_bib1) 2014; 31 Kang (10.1016/j.biosystemseng.2021.03.012_bib17) 2019; 19 Sa (10.1016/j.biosystemseng.2021.03.012_bib41) 2016; 16 Szegedy (10.1016/j.biosystemseng.2021.03.012_bib43) 2017 Xiong (10.1016/j.biosystemseng.2021.03.012_bib48) 2018; 166 Tian (10.1016/j.biosystemseng.2021.03.012_bib44) 2019; 157 Litz (10.1016/j.biosystemseng.2021.03.012_bib25) 2009 Lowe (10.1016/j.biosystemseng.2021.03.012_bib29) 2004; 60 Bargoti (10.1016/j.biosystemseng.2021.03.012_bib2) 2017 Huang (10.1016/j.biosystemseng.2021.03.012_bib15) 2017 Rahnemoonfar (10.1016/j.biosystemseng.2021.03.012_bib35) 2017; 17 He (10.1016/j.biosystemseng.2021.03.012_bib13) 2017 Yan (10.1016/j.biosystemseng.2021.03.012_bib50) 2018 Zhang (10.1016/j.biosystemseng.2021.03.012_bib51) 2017 Xiong (10.1016/j.biosystemseng.2021.03.012_bib46) 2020; 37 Payne (10.1016/j.biosystemseng.2021.03.012_bib34) 2013; 91 Häni (10.1016/j.biosystemseng.2021.03.012_bib11) 2018 |
| References_xml | – year: 2015 ident: bib38 article-title: Faster R-CNN: Towards real-time object detection with region proposal networks publication-title: Paper presented at the advances in neural information processing systems (NIPS) – volume: 37 start-page: 263 year: 2020 end-page: 282 ident: bib12 article-title: A comparative study of fruit detection and counting methods for yield mapping in apple orchards publication-title: Journal of Field Robotics – volume: 162 start-page: 219 year: 2019 end-page: 234 ident: bib20 article-title: Deep learning – method overview and review of use for fruit detection and yield estimation publication-title: Computers and Electronics in Agriculture – volume: 99 start-page: 130 year: 2018 end-page: 139 ident: bib30 article-title: A vision methodology for harvesting robot to detect cutting points on peduncles of double overlapping grape clusters in a vineyard publication-title: Computers in Industry – year: 2018 ident: bib50 article-title: Spatial temporal graph convolutional networks for skeleton-based action recognition publication-title: Paper presented at the thirty-second AAAI conference on artificial intelligence – year: 2006 ident: bib4 article-title: Surf: Speeded up robust features – volume: 116 start-page: 8 year: 2015 end-page: 19 ident: bib9 article-title: Sensors and systems for fruit detection and localization: A review publication-title: Computers and Electronics in Agriculture – year: 2009 ident: bib8 article-title: Automation in agriculture – volume: 16 start-page: 1222 year: 2016 ident: bib41 article-title: DeepFruits: A fruit detection system using deep neural networks publication-title: Sensors – volume: 18 start-page: 969 year: 2018 ident: bib49 article-title: Green grape detection and picking-point calculation in a night-time natural environment using a charge-coupled device (CCD) vision sensor with artificial illumination publication-title: Sensors – year: 2019 ident: bib21 article-title: Deep learning for real-time fruit detection and orchard fruit load estimation: Benchmarking of ‘MangoYOLO’ publication-title: Precision Agriculture – year: 2017 ident: bib13 article-title: Mask r-cnn – volume: 17 start-page: 905 year: 2017 ident: bib35 article-title: Deep count: Fruit counting based on deep simulated learning publication-title: Sensors – year: 2017 ident: bib51 article-title: Determinantal point processes for mini-batch diversification publication-title: Paper presented at the Uncertainty in Artificial Intelligence – year: 2017 ident: bib22 article-title: Feature pyramid networks for object detection publication-title: Paper presented at the proceedings of the IEEE conference on computer vision and pattern recognition – volume: 60 start-page: 91 year: 2004 end-page: 110 ident: bib29 article-title: Distinctive image features from scale-invariant keypoints publication-title: International Journal of Computer Vision – volume: 31 start-page: 888 year: 2014 end-page: 911 ident: bib1 article-title: Harvesting robots for high-value crops: State-of-the-art review and challenges ahead publication-title: Journal of Field Robotics – volume: 34 start-page: 1039 year: 2017 end-page: 1060 ident: bib3 article-title: Image segmentation for fruit detection and yield estimation in apple orchards publication-title: Journal of Field Robotics – volume: 37 start-page: 97 year: 2020 end-page: 121 ident: bib7 article-title: Semantic mapping for orchard environments by merging two-sides reconstructions of tree rows publication-title: Journal of Field Robotics – volume: 77 start-page: 59 year: 2019 end-page: 69 ident: bib19 article-title: MangoNet: A deep semantic segmentation architecture for a method to detect and count mangoes in an open orchard publication-title: Engineering Applications of Artificial Intelligence – volume: 37 start-page: 202 year: 2020 end-page: 224 ident: bib46 article-title: An autonomous strawberry-harvesting robot: Design, development, integration, and field evaluation publication-title: Journal of Field Robotics – volume: 151 start-page: 226 year: 2018 end-page: 237 ident: bib47 article-title: Visual positioning technology of picking robots for dynamic litchi clusters with disturbance publication-title: Computers and Electronics in Agriculture – year: 2009 ident: bib25 article-title: The mango: Botany, production and uses – year: 2017 ident: bib40 article-title: Vision-based apple counting and yield estimation publication-title: Paper presented at the 2016 international symposium on experimental robotics, cham – year: 2005 ident: bib6 article-title: Histograms of oriented gradients for human detection. Paper presented at the Computer Vision and Pattern Recognition, 2005. CVPR 2005 publication-title: IEEE Computer Society Conference on – volume: 157 start-page: 417 year: 2019 end-page: 426 ident: bib44 article-title: Apple detection during different growth stages in orchards using the improved YOLO-V3 model publication-title: Computers and Electronics in Agriculture – volume: 32 start-page: 5 year: 2016 end-page: 18 ident: bib53 article-title: Fault-tolerant design of a limited universal fruit-picking end-effector based on vision-positioning error publication-title: Applied Engineering in Agriculture – volume: 3 start-page: 4 year: 2012 end-page: 34 ident: bib18 article-title: Computer vision for fruit harvesting robots – state of the art and challenges ahead publication-title: International Journal of Computational Vision and Robotics – year: 2015 ident: bib28 article-title: Fully convolutional networks for semantic segmentation publication-title: Paper presented at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – volume: 181 start-page: 140 year: 2019 end-page: 156 ident: bib45 article-title: Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms publication-title: Biosystems Engineering – year: 2017 ident: bib43 article-title: Inception-v4, inception-resnet and the impact of residual connections on learning publication-title: Paper presented at the thirty-first AAAI conference on artificial intelligence – volume: 34 start-page: 1140 year: 2017 end-page: 1159 ident: bib42 article-title: Design, integration, and field evaluation of a robotic apple harvester publication-title: Journal of Field Robotics – year: 2016 ident: bib32 article-title: Stacked hourglass networks for human pose estimation – volume: 166 start-page: 44 year: 2018 end-page: 57 ident: bib48 article-title: The recognition of litchi clusters and the calculation of picking point in a nocturnal natural environment publication-title: Biosystems Engineering – year: 2018 ident: bib11 article-title: Apple counting using convolutional neural networks publication-title: Paper presented at the 2018 IEEE/RSJ international conference on intelligent robots and systems (IROS) – year: 2017 ident: bib5 article-title: Counting apples and oranges with deep learning: A data driven approach – year: 2015 ident: bib36 article-title: You only look once: Unified, real-time object detection – volume: 151 start-page: 90 year: 2016 end-page: 104 ident: bib31 article-title: Vision-based extraction of spatial information in grape clusters for harvesting robots publication-title: Biosystems Engineering – year: 2017 ident: bib2 article-title: Deep fruit detection in orchards publication-title: Paper presented at the 2017 IEEE International Conference on robotics and automation (ICRA) – year: 2016 ident: bib14 article-title: Deep residual learning for image recognition publication-title: Paper presented at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) – volume: 157 start-page: 126 year: 2019 end-page: 135 ident: bib10 article-title: Ground based hyperspectral imaging for extensive mango yield estimation publication-title: Computers and Electronics in Agriculture – start-page: 4438 year: 2017 end-page: 4446 ident: bib24 article-title: Fully convolutional instance-aware semantic segmentation publication-title: 30th IEEE Conference on Computer Vision and Pattern Recognition (Cvpr 2017) – year: 2018 ident: bib39 article-title: Registering reconstructions of the two sides of fruit tree rows publication-title: Paper presented at the 2018 IEEE/RSJ international conference on intelligent robots and systems (IROS) – volume: 7 start-page: 64323 year: 2019 end-page: 64350 ident: bib54 article-title: Diversity in machine learning publication-title: IEEE Access – volume: 249 start-page: 77 year: 2019 end-page: 85 ident: bib33 article-title: Quality assesment and postharvest technology of mango: A review of its current status and future perspectives publication-title: Scientia Horticulturae – year: 2018 ident: bib37 article-title: Yolov3: An incremental improvement – volume: 19 start-page: 4599 year: 2019 ident: bib17 article-title: Fruit detection and segmentation for apple harvesting using visual sensor in orchards publication-title: Sensors – volume: 8693 start-page: 740 year: 2014 end-page: 755 ident: bib23 article-title: Microsoft COCO: Common objects in Context publication-title: Computer Vision - Eccv 2014, Pt V – volume: 91 start-page: 57 year: 2013 end-page: 64 ident: bib34 article-title: Estimation of mango crop yield using image analysis – segmentation method publication-title: Computers and Electronics in Agriculture – year: 2017 ident: bib15 article-title: Towards automated strawberry harvesting: Identifying the picking point – year: 2018 ident: bib27 article-title: Robust fruit counting: Combining deep learning, tracking, and structure from motion publication-title: Paper presented at the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) – volume: 127 start-page: 311 year: 2016 end-page: 323 ident: bib52 article-title: A review of key techniques of vision-based control for harvesting robot publication-title: Computers and Electronics in Agriculture – volume: 147 start-page: 70 year: 2018 end-page: 90 ident: bib16 article-title: Deep learning in agriculture: A survey publication-title: Computers and Electronics in Agriculture – year: 2015 ident: bib26 article-title: SSD: Single Shot MultiBox detector – volume: 31 start-page: 888 issue: 6 year: 2014 ident: 10.1016/j.biosystemseng.2021.03.012_bib1 article-title: Harvesting robots for high-value crops: State-of-the-art review and challenges ahead publication-title: Journal of Field Robotics doi: 10.1002/rob.21525 – volume: 7 start-page: 64323 year: 2019 ident: 10.1016/j.biosystemseng.2021.03.012_bib54 article-title: Diversity in machine learning publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2917620 – volume: 60 start-page: 91 issue: 2 year: 2004 ident: 10.1016/j.biosystemseng.2021.03.012_bib29 article-title: Distinctive image features from scale-invariant keypoints publication-title: International Journal of Computer Vision doi: 10.1023/B:VISI.0000029664.99615.94 – volume: 16 start-page: 1222 issue: 8 year: 2016 ident: 10.1016/j.biosystemseng.2021.03.012_bib41 article-title: DeepFruits: A fruit detection system using deep neural networks publication-title: Sensors doi: 10.3390/s16081222 – volume: 34 start-page: 1140 issue: 6 year: 2017 ident: 10.1016/j.biosystemseng.2021.03.012_bib42 article-title: Design, integration, and field evaluation of a robotic apple harvester publication-title: Journal of Field Robotics doi: 10.1002/rob.21715 – volume: 8693 start-page: 740 year: 2014 ident: 10.1016/j.biosystemseng.2021.03.012_bib23 article-title: Microsoft COCO: Common objects in Context publication-title: Computer Vision - Eccv 2014, Pt V doi: 10.1007/978-3-319-10602-1_48 – volume: 151 start-page: 226 year: 2018 ident: 10.1016/j.biosystemseng.2021.03.012_bib47 article-title: Visual positioning technology of picking robots for dynamic litchi clusters with disturbance publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2018.06.007 – year: 2017 ident: 10.1016/j.biosystemseng.2021.03.012_bib51 article-title: Determinantal point processes for mini-batch diversification – year: 2016 ident: 10.1016/j.biosystemseng.2021.03.012_bib14 article-title: Deep residual learning for image recognition – volume: 157 start-page: 126 year: 2019 ident: 10.1016/j.biosystemseng.2021.03.012_bib10 article-title: Ground based hyperspectral imaging for extensive mango yield estimation publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2018.12.041 – volume: 166 start-page: 44 year: 2018 ident: 10.1016/j.biosystemseng.2021.03.012_bib48 article-title: The recognition of litchi clusters and the calculation of picking point in a nocturnal natural environment publication-title: Biosystems Engineering doi: 10.1016/j.biosystemseng.2017.11.005 – volume: 37 start-page: 97 year: 2020 ident: 10.1016/j.biosystemseng.2021.03.012_bib7 article-title: Semantic mapping for orchard environments by merging two-sides reconstructions of tree rows publication-title: Journal of Field Robotics doi: 10.1002/rob.21876 – volume: 19 start-page: 4599 issue: 20 year: 2019 ident: 10.1016/j.biosystemseng.2021.03.012_bib17 article-title: Fruit detection and segmentation for apple harvesting using visual sensor in orchards publication-title: Sensors doi: 10.3390/s19204599 – volume: 147 start-page: 70 year: 2018 ident: 10.1016/j.biosystemseng.2021.03.012_bib16 article-title: Deep learning in agriculture: A survey publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2018.02.016 – year: 2018 ident: 10.1016/j.biosystemseng.2021.03.012_bib39 article-title: Registering reconstructions of the two sides of fruit tree rows – year: 2015 ident: 10.1016/j.biosystemseng.2021.03.012_bib38 article-title: Faster R-CNN: Towards real-time object detection with region proposal networks – volume: 18 start-page: 969 issue: 4 year: 2018 ident: 10.1016/j.biosystemseng.2021.03.012_bib49 article-title: Green grape detection and picking-point calculation in a night-time natural environment using a charge-coupled device (CCD) vision sensor with artificial illumination publication-title: Sensors doi: 10.3390/s18040969 – volume: 32 start-page: 5 issue: 1 year: 2016 ident: 10.1016/j.biosystemseng.2021.03.012_bib53 article-title: Fault-tolerant design of a limited universal fruit-picking end-effector based on vision-positioning error publication-title: Applied Engineering in Agriculture doi: 10.13031/aea.32.10701 – year: 2018 ident: 10.1016/j.biosystemseng.2021.03.012_bib50 article-title: Spatial temporal graph convolutional networks for skeleton-based action recognition – year: 2017 ident: 10.1016/j.biosystemseng.2021.03.012_bib40 article-title: Vision-based apple counting and yield estimation – volume: 127 start-page: 311 year: 2016 ident: 10.1016/j.biosystemseng.2021.03.012_bib52 article-title: A review of key techniques of vision-based control for harvesting robot publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2016.06.022 – volume: 116 start-page: 8 year: 2015 ident: 10.1016/j.biosystemseng.2021.03.012_bib9 article-title: Sensors and systems for fruit detection and localization: A review publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2015.05.021 – year: 2019 ident: 10.1016/j.biosystemseng.2021.03.012_bib21 article-title: Deep learning for real-time fruit detection and orchard fruit load estimation: Benchmarking of ‘MangoYOLO’ publication-title: Precision Agriculture doi: 10.1007/s11119-019-09642-0 – year: 2017 ident: 10.1016/j.biosystemseng.2021.03.012_bib13 – start-page: 4438 year: 2017 ident: 10.1016/j.biosystemseng.2021.03.012_bib24 article-title: Fully convolutional instance-aware semantic segmentation – year: 2017 ident: 10.1016/j.biosystemseng.2021.03.012_bib22 article-title: Feature pyramid networks for object detection – year: 2016 ident: 10.1016/j.biosystemseng.2021.03.012_bib32 – year: 2017 ident: 10.1016/j.biosystemseng.2021.03.012_bib5 – year: 2005 ident: 10.1016/j.biosystemseng.2021.03.012_bib6 article-title: Histograms of oriented gradients for human detection. Paper presented at the Computer Vision and Pattern Recognition, 2005. CVPR 2005 – year: 2017 ident: 10.1016/j.biosystemseng.2021.03.012_bib43 article-title: Inception-v4, inception-resnet and the impact of residual connections on learning – volume: 17 start-page: 905 issue: 4 year: 2017 ident: 10.1016/j.biosystemseng.2021.03.012_bib35 article-title: Deep count: Fruit counting based on deep simulated learning publication-title: Sensors doi: 10.3390/s17040905 – year: 2009 ident: 10.1016/j.biosystemseng.2021.03.012_bib25 – year: 2015 ident: 10.1016/j.biosystemseng.2021.03.012_bib36 – volume: 77 start-page: 59 year: 2019 ident: 10.1016/j.biosystemseng.2021.03.012_bib19 article-title: MangoNet: A deep semantic segmentation architecture for a method to detect and count mangoes in an open orchard publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2018.09.011 – year: 2018 ident: 10.1016/j.biosystemseng.2021.03.012_bib27 article-title: Robust fruit counting: Combining deep learning, tracking, and structure from motion – volume: 3 start-page: 4 issue: 1/2 year: 2012 ident: 10.1016/j.biosystemseng.2021.03.012_bib18 article-title: Computer vision for fruit harvesting robots – state of the art and challenges ahead publication-title: International Journal of Computational Vision and Robotics doi: 10.1504/IJCVR.2012.046419 – year: 2018 ident: 10.1016/j.biosystemseng.2021.03.012_bib11 article-title: Apple counting using convolutional neural networks – volume: 99 start-page: 130 year: 2018 ident: 10.1016/j.biosystemseng.2021.03.012_bib30 article-title: A vision methodology for harvesting robot to detect cutting points on peduncles of double overlapping grape clusters in a vineyard publication-title: Computers in Industry doi: 10.1016/j.compind.2018.03.017 – volume: 157 start-page: 417 year: 2019 ident: 10.1016/j.biosystemseng.2021.03.012_bib44 article-title: Apple detection during different growth stages in orchards using the improved YOLO-V3 model publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2019.01.012 – year: 2018 ident: 10.1016/j.biosystemseng.2021.03.012_bib37 – volume: 181 start-page: 140 year: 2019 ident: 10.1016/j.biosystemseng.2021.03.012_bib45 article-title: Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms publication-title: Biosystems Engineering doi: 10.1016/j.biosystemseng.2019.03.007 – volume: 162 start-page: 219 year: 2019 ident: 10.1016/j.biosystemseng.2021.03.012_bib20 article-title: Deep learning – method overview and review of use for fruit detection and yield estimation publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2019.04.017 – year: 2017 ident: 10.1016/j.biosystemseng.2021.03.012_bib2 article-title: Deep fruit detection in orchards – year: 2006 ident: 10.1016/j.biosystemseng.2021.03.012_bib4 – volume: 37 start-page: 263 issue: 2 year: 2020 ident: 10.1016/j.biosystemseng.2021.03.012_bib12 article-title: A comparative study of fruit detection and counting methods for yield mapping in apple orchards publication-title: Journal of Field Robotics doi: 10.1002/rob.21902 – volume: 91 start-page: 57 year: 2013 ident: 10.1016/j.biosystemseng.2021.03.012_bib34 article-title: Estimation of mango crop yield using image analysis – segmentation method publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2012.11.009 – volume: 151 start-page: 90 year: 2016 ident: 10.1016/j.biosystemseng.2021.03.012_bib31 article-title: Vision-based extraction of spatial information in grape clusters for harvesting robots publication-title: Biosystems Engineering doi: 10.1016/j.biosystemseng.2016.08.026 – volume: 34 start-page: 1039 issue: 6 year: 2017 ident: 10.1016/j.biosystemseng.2021.03.012_bib3 article-title: Image segmentation for fruit detection and yield estimation in apple orchards publication-title: Journal of Field Robotics doi: 10.1002/rob.21699 – year: 2009 ident: 10.1016/j.biosystemseng.2021.03.012_bib8 – year: 2015 ident: 10.1016/j.biosystemseng.2021.03.012_bib28 article-title: Fully convolutional networks for semantic segmentation – volume: 249 start-page: 77 year: 2019 ident: 10.1016/j.biosystemseng.2021.03.012_bib33 article-title: Quality assesment and postharvest technology of mango: A review of its current status and future perspectives publication-title: Scientia Horticulturae doi: 10.1016/j.scienta.2019.01.033 – volume: 37 start-page: 202 issue: 2 year: 2020 ident: 10.1016/j.biosystemseng.2021.03.012_bib46 article-title: An autonomous strawberry-harvesting robot: Design, development, integration, and field evaluation publication-title: Journal of Field Robotics doi: 10.1002/rob.21889 – year: 2017 ident: 10.1016/j.biosystemseng.2021.03.012_bib15 – year: 2015 ident: 10.1016/j.biosystemseng.2021.03.012_bib26 |
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| SubjectTerms | algorithms Complex scene data collection End-to-end framework Fruit picking robot fruits Instance segmentation lighting mangoes Occlusion and overlap orchards Picking-point detection vision |
| Title | A mango picking vision algorithm on instance segmentation and key point detection from RGB images in an open orchard |
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