[논문]Weakly Supervised Crop Area Segmentation for an Autonomous Combine Harvester

Kim, Wan-Soo; Lee, Dae-Hyun; Kim, Taehyeong; Kim, Hyunggun; Sim, Taeyong; Kim, Yong-Joo

doi:10.3390/s21144801

[해외논문] Weakly Supervised Crop Area Segmentation for an Autonomous Combine Harvester 원문보기

Sensors, v.21 no.14, 2021년, pp.4801 -

Kim, Wan-Soo (Institute of Agricultural Science, Chungnam National University, Daejeon 34134, Korea) , Lee, Dae-Hyun (wskim0726@gmail.com) , Kim, Taehyeong (Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Korea) , Kim, Hyunggun (babina@cnu.ac.kr) , Sim, Taeyong (Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul 08826, Korea) , Kim, Yong-Joo (taehyeong.kim@lge.com)

Abstract ▼ AI-Helper

Machine vision with deep learning is a promising type of automatic visual perception for detecting and segmenting an object effectively; however, the scarcity of labelled datasets in agricultural fields prevents the application of deep learning to agriculture. For this reason, this study proposes weakly supervised crop area segmentation (WSCAS) to identify the uncut crop area efficiently for path guidance. Weakly supervised learning has advantage for training models because it entails less laborious annotation. The proposed method trains the classification model using area-specific images so that the target area can be segmented from the input image based on implicitly learned localization. This way makes the model implementation easy even with a small data scale. The performance of the proposed method was evaluated using recorded video frames that were then compared with previous deep-learning-based segmentation methods. The results showed that the proposed method can be conducted with the lowest inference time and that the crop area can be localized with an intersection over union of approximately 0.94. Additionally, the uncut crop edge could be detected for practical use based on the segmentation results with post-image processing such as with a Canny edge detector and Hough transformation. The proposed method showed the significant ability of using automatic perception in agricultural navigation to infer the crop area with real-time level speed and have localization comparable to existing semantic segmentation methods. It is expected that our method will be used as essential tool for the automatic path guidance system of a combine harvester.

Keyword

참고문헌 (32)

1. Kneip J. Fleischmann P. Berns K. Crop edge detection based on stereo vision Rob. Auton. Syst. 2020 123 103323 10.1016/j.robot.2019.103323

상세보기
2. Lenaerts B. Missotten B. De Baerdemaeker J. Saeys W. LiDaR sensing to monitor straw output quality of a combine harvester Comput. Electron. Agric. 2012 85 40 44 10.1016/j.compag.2012.03.011

상세보기
3. Coen T. Vanrenterghem A. Saeys W. De Baerdemaeker J. Autopilot for a combine harvester Comput. Electron. Agric. 2008 63 57 64 10.1016/j.compag.2008.01.014

상세보기
4. Zhang Z. Cao R. Peng C. Liu R. Sun Y. Zhang M. Li H. Cut-edge detection method for rice harvesting based on machine vision Agronomy 2020 10 590 10.3390/agronomy10040590

상세보기
5. Benson E.R. Reid J.F. Zhang Q. Machine Vision-based Guidance System for Agricultural Grain Harvesters using Cut-edge Detection Biosyst. Eng. 2003 86 389 398 10.1016/j.biosystemseng.2003.07.002

상세보기
6. Gerrish J.B. Fehr B.W. Van Ee G.R. Welch D.P. Self-steering tractor guided by computer-vision Appl. Eng. Agric. 1997 13 559 563 10.13031/2013.21641

상세보기
7. Zhang T. Xia J.F. Wu G. Zhai J.B. Automatic navigation path detection method for tillage machines working on high crop stubble fields based on machine vision Int. J. Agric. Biol. Eng. 2014 7 29 37
8. Lei Z. Mao W.S. Qi C.B. Xia Z.H. Crop-edge detection based on machine vision N. Z. J. Agric. Res. 2007 50 1367 1374 10.1080/00288230709510424

상세보기
9. Rovira-Mas F. Han S. Wei J. Reid J.F. Autonomous guidance of a corn harvester using stereo vision Agric. Eng. Int. CIGR J. 2007 IX 1 13
10. Ahmad M.Z. Akhtar A. Khan A.Q. Khan A.A. Simplified vision based automatic navigation for wheat harvesting in low income economies arXiv 2015 1501.02376
11. Cho W. Iida M. Suguri M. Masuda R. Kurita H. Using multiple sensors to detect uncut crop edges for autonomous guidance systems of head-feeding combine harvesters Eng. Agric. Environ. Food 2014 7 115 121 10.1016/j.eaef.2014.02.004

상세보기
12. Zhao T. Noguchi N. Yang L.L. Ishii K. Chen J. Development of uncut crop edge detection system based on laser rangefinder for combine harvesters Int. J. Agric. Biol. Eng. 2016 9 21 28
13. Blanquart J.E. Sirignano E. Lenaerts B. Saeys W. Online crop height and density estimation in grain fields using LiDAR Biosyst. Eng. 2020 198 1 14 10.1016/j.biosystemseng.2020.06.014

상세보기
14. Li Y. Iida M. Suyama T. Suguri M. Masuda R. Implementation of deep-learning algorithm for obstacle detection and collision avoidance for robotic harvester Comput. Electron. Agric. 2020 174 105499 10.1016/j.compag.2020.105499

상세보기
15. Jiang W. Yang Z. Wang P. Cao Q. Navigation Path Points Extraction Method Based on Color Space and Depth Information for Combine Harvester Proceedings of the 2020 5th International Conference on Advanced Robotics and Mechatronics (ICARM) Shenzhen, China 18？21 December 2020 622 627
16. Long J. Shelhamer E. Darrell T. Fully Convolutional Networks for Semantic Segmentation Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Boston, MA, USA 7？12 June 2015 3431 3440
17. Zhao H. Qi X. Shen X. Shi J. Jia J. Icnet for Real-Time Semantic Segmentation on High-Resolution Images Proceedings of the European Conference on Computer Vision (ECCV) Munich, Germany 8？14 September 2018 405 420
18. Wang J. Sun K. Cheng T. Jiang B. Deng C. Zhao Y. Liu D. Mu Y. Tan M. Wang X. Deep high-resolution representation learning for visual recognition IEEE Trans. Pattern Anal. Mach. Intell. 2020 10.1109/TPAMI.2020.2983686 32248092

상세보기
19. He K. Gkioxari G. Dollar P. Girshick R. Mask r-cnn Proceedings of the IEEE International Conference on Computer Vision Venice, Italy 22？29 October 2017 2961 2969
20. Kim W.-S. Lee D.-H. Kim Y.-J. Machine vision-based automatic disease symptom detection of onion downy mildew Comput. Electron. Agric. 2020 168 105099 10.1016/j.compag.2019.105099

상세보기
21. Ni X. Li C. Jiang H. Takeda F. Deep learning image segmentation and extraction of blueberry fruit traits associated with harvestability and yield Hortic. Res. 2020 7 1 14 10.1038/s41438-020-0323-3 31908804

상세보기
22. Brahimi M. Arsenovic M. Laraba S. Sladojevic S. Boukhalfa K. Moussaoui A. Deep learning for plant diseases: Detection and saliency map visualisation Human and Machine Learning Springer Berlin/Heidelberg, Germany 2018 93 117
23. Christiansen P. Nielsen L.N. Steen K.A. Jørgensen R.N. Karstoft H. DeepAnomaly: Combining background subtraction and deep learning for detecting obstacles and anomalies in an agricultural field Sensors 2016 16 1904 10.3390/s16111904

상세보기
24. Kim W.S. Lee D.H. Kim Y.J. Kim T. Hwang R.Y. Lee H.J. Path detection for autonomous traveling in orchards using patch-based CNN Comput. Electron. Agric. 2020 175 105620 10.1016/j.compag.2020.105620

상세보기
25. Choi J. Yin X. Yang L. Noguchi N. Development of a laser scanner-based navigation system for a combine harvester Eng. Agric. Environ. Food 2014 7 7 13 10.1016/j.eaef.2013.12.002

상세보기
26. Wu Y. Xu L. Crop organ segmentation and disease identification based on weakly supervised deep neural network Agronomy 2019 9 737 10.3390/agronomy9110737

상세보기
27. Zhou B. Khosla A. Lapedriza A. Oliva A. Torralba A. Learning Deep Features for Discriminative Localization Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Las Vegas, NV, USA 26 June？1 July 2016 2921 2929
28. Kim W.S. Lee D.H. Kim Y.J. Kim T. Lee W.S. Choi C.H. Stereo-vision-based crop height estimation for agricultural robots Comput. Electron. Agric. 2021 181 105937 10.1016/j.compag.2020.105937

상세보기
29. Ji R. Qi L. Crop-row detection algorithm based on Random Hough Transformation Math. Comput. Model. 2011 54 1016 1020 10.1016/j.mcm.2010.11.030

상세보기
30. Ding L. Goshtasby A. On the canny edge detector Pattern Recognit. 2001 34 721 725 10.1016/S0031-3203(00)00023-6

상세보기
31. Kim G. Seo D. Kim K.-C. Hong Y. Lee M. Lee S. Kim H. Ryu H.-S. Kim Y.-J. Chung S.-O. Tillage boundary detection based on RGB imagery classification for an autonomous tractor Korean J. Agric. Sci. 2020 47 205 217

원문보기 상세보기
32. Han X.Z. Kim H.J. Kim J.Y. Yi S.Y. Moon H.C. Kim J.H. Kim Y.J. Path-tracking simulation and field tests for an auto-guidance tillage tractor for a paddy field Comput. Electron. Agric. 2015 112 161 171 10.1016/j.compag.2014.12.025

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