Exploiting affine invariant regions and leaf edge shapes for weed detection

Computers and Electronics in Agriculture

Volumn 118, Issue , 2015, Pages 290-299

  Kazmi, Wajahat ^a   Garcia Ruiz, Francisco ^b   Nielsen, Jon ^b   Rasmussen, Jesper ^b   Andersen, Hans Jørgen ^a  

^a AALBORG UNIVERSITY   (Denmark)

^b UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

Affine invariant regions; Computer vision; Local features; Precision agriculture; Weed detection

Indexed keywords

COLOR; COMPUTER VISION; ECONOMIC AND SOCIAL EFFECTS; IMAGE RETRIEVAL; SUGAR BEETS; TREES (MATHEMATICS); VEGETATION; WEED CONTROL;

AFFINE INVARIANT; BAG-OF-VISUAL-WORDS; CLASSIFICATION ACCURACY; FALSE NEGATIVE RATE; HOMOGENEOUS SURFACES; LOCAL FEATURE; PRECISION AGRICULTURE; WEED DETECTION;

FEATURE EXTRACTION;

ACCURACY ASSESSMENT; COLOR MORPH; COMPUTER SIMULATION; DATA SET; DATABASE; DETECTION METHOD; DICOTYLEDON; IMAGE ANALYSIS; MORPHOLOGY; PERFORMANCE ASSESSMENT; PRECISION AGRICULTURE; SUGAR BEET; WEED;

BETA VULGARIS SUBSP. VULGARIS; CIRSIUM ARVENSE;

EID: 84939855053     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2015.08.023     Document Type: Article

References (51)

1. Åstrand B., Baerveldt A. An agricultural mobile robot with vision-based perception for mechanical weed control. Auton. Robots 2002, 13(1):21-35.
2. Agarwal G., Belhumeur P., Feiner S., Jacobs D., Kress W.J., Ramamoorthi R., Bourg N.A., Dixit N., Ling D., Mahajan Haibin, Russell R., Shirdhonkar S., Sunkavalli K., White S. First steps toward an electronic field guide for plants. Taxon 2006, 55:597-610.
3. Alenya G., Dellen B., Foix S., Torras C. Robotized plant probing: leaf segmentation utilizing time-of-flight data. Robot. Autom. Magaz., IEEE 2013, 20(3):50-59. ISSN 1070-9932. 10.1109/MRA.2012.2230118.
4. Andreasen C., Stryhn H. Increasing weed flora in Danish beet, pea and winter barley fields. Crop Protect. 2012, 36:11-17. ISSN 02612194, 10.1016/j.cropro.2012.01.012.
5. Avrithis, Y., Rapantzikos, K., 2011. The medial feature detector: stable regions from image boundaries. In: 2011 IEEE International Conference on Computer Vision (ICCV), vol. 1(c), pp. 1724-1731, <>. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6126436http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6126436.
6. Bay H., Ess A., Tuytelaars T., Van Gool L. Speeded-up robust features (SURF). Comp. Vis. Image Understand. 2008, 110(3):346-359. .
7. Beaudet, P.R., 1978. Rotationally invariant image operators. In: Proceedings of the 4th International Joint Conference on Pattern Recognition. Kyoto, Japan, pp. 579-583.
8. Belhumeur, P.N., Chen, D., Feiner, S., Jacobs, D.W., Kress, W.J., Ling, H., Lopez, I., Ramamoorthi, R., 2008. Searching the world's herbaria: a system for visual identification of plant species. In: European Conference on Computer Vision (ECCV ) IV, pp. 116-129 <>. http://link.springer.com/chapter/10.1007/978-3-540-88693-8_9.
9. Belongie S., Malik J., Puzicha J. Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24(4):509-522. ISSN 01628828 10.1109/34.993558.
10. Campbell R., Flynn P.J. A survey of free-form object representation and recognition techniques. Comp. Vis. Image Understand. 2001, 81(2):166-210. ISSN 10773142 10.1006/cviu.2000.0889.
11. Chatfield, K., Lempitsky, V.S., Vedaldi, A., Zisserman, A., 2011. The devil is in the details: an evaluation of recent feature encoding methods. In: British Machine Vision Conference, BMVA. Dundee, UK, pp. 1-12, http://dx.doi.org/10.5244/C.25.76, 2011.
12. Christensen S., Søgaard H.T., Kudsk P., Nørremark M., Lund I., Nadimi E.S., Jørgensen R. Site-specific weed control technologies. Weed Res. 2009, 49(3):233-241. ISSN 00431737 10.1111/j.1365-3180.2009.00696.x.
13. Cope J.S., Corney D., Clark J.Y., Remagnino P., Wilkin P. Plant species identification using digital morphometrics: a review. Expert Syst. Appl. 2012, 39(8):7562-7573. ISSN 09574174, 10.1016/j.eswa.2012.01.073.
14. Cormack G.V., Lynam T.R. Statistical precision of information retrieval evaluation. Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '06 2006, 533-540. ACM, New York, NY, USA, ISBN 1-59593-369-7, http://dx.doi.org/10.1145/1148170.1148262, .
15. Dellen, B., Alenya, G., Foix, S., Torras, C., 2011. Segmenting color images into surface patches by exploiting sparse depth data. In: IEEE Workshop on Applications of Computer Vision (WACV). Kona, Hawai, pp. 591-598 <>. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5711558.
16. FAOSTAT, 2011. Food and Agricultural Organization of the United Nations. <> (retrieved 26.02.14). http://faostat3.fao.org/.
17. Freeman W.T., Adelson E.H. The design and use of steerable filters. IEEE Trans. Pattern Anal. Mach. Intell. 1991, 13(9):891-906. ISSN 0162-8828. 10.1109/34.93808.
18. Golzarian M.R., Frick R.a. Classification of images of wheat, ryegrass and brome grass species at early growth stages using principal component analysis. Plant Meth. 2011, 7(1):28. ISSN 1746-4811 10.1186/1746-4811-7-28.
19. Jeon H.Y., Tian L.F., Zhu H., crop Robust Robust crop and weed segmentation under uncontrolled outdoor illumination. Sensors (Basel, Switzerland) 2011, 11(6):6270-6283. ISSN 1424-8220 10.3390/s110606270.
20. Kataoka, T., Kaneko, T., Okamoto, H., Hata, S., 2003. Crop growth estimation system using machine vision. In: Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), vol. 2, pp. 1079-1083, <>. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1225492.
21. Kazmi W., Foix S., Alenyà G., Andersen H.J. Indoor and outdoor depth imaging of leaves with time-of-flight and stereo vision sensors: Analysis and comparison. ISPRS J. Photogram. Rem. Sens. 2014, 88:128-146. ISSN 09242716, 10.1016/j.isprsjprs.2013.11.012.
22. Kazmi W., Garcia-Ruiz F.J., Nielsen J., Rasmussen J., Andersen H.J. Detecting creeping thistle in sugar beet fields using vegetation indices. Comp. Electron. Agricult. 2015, 112(0):10-19. ISSN 0168-1699, , precision Agriculture. 10.1016/j.compag.2015.01.008.
23. Kazmi W., Andersen H.J. A comparison of interest point and region detectors on structured, range and texture images. J. Vis. Commun. Image R. 2015, 32:156-169. ISSN 1047-3203. 10.1016/j.jvcir.2015.08.004.
24. Kumar, N., Belhumeur, P.N., Biswas, A., Jacobs, D.W., Kress, W.J., Lopez, I., Soares, J.V.B., 2012. Leafsnap: a computer vision system for automatic plant species identification. In: 12th European Conference on Computer Vision (ECCV). Florence, Italy, pp. 502-516.
25. Lazebnik, S., Schmid, C., Ponce, J., 2003. A sparse texture representation using affine-invariant regions. In: Computer Vision and Pattern Recognition (CVPR), pp. 319-324 <>. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1211486.
26. Lazebnik S., Schmid C., Ponce J. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. Conference on Computer Vision and Pattern Recognition 2006, vol. 2:2169-2178. IEEE Computer Society, ISBN 0-7695-2597-0, http://dx.doi.org/10.1109/CVPR.2006.68, .
27. Ling, H., Jacobs, D.W., 2009. Shape matching for foliage database retrieval, 100.
28. Lopez-Granadoz F. Weed detection for site-specific weed management: mapping and real-time approaches. Weed Res. 2011, 51(1):1-11. ISSN 1365-3180, 10.1111/j.1365-3180.2010.00829.x.
29. Lowe, D., 1999. Object recognition from local scale-invariant features. In: The Proceedings of the Computer Vision, vol. 2, 1999, pp. 1150-1157, <>. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=790410http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=790410.
30. Lowe D.G. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 2004, 60(2):91-110. ISSN 0920-5691 10.1023/B:VISI.0000029664.99615.94.
31. Manning C.D., Raghavan P., Schütze H. Evaluation in Information Retrieval 2008, Cambridge University Press, New York, NY, USA, ISBN 0521865719, 9780521865715.
32. Matas J., Chum O., Urban M., Pajdla T. Robust wide-baseline stereo from maximally stable extremal regions. British Machine Vision Conference 2002, vol. 22:384-393. Elsevier, Cardiff, Wales, UK, .
33. Meyer G.E., Neto J.a.C. Verification of color vegetation indices for automated crop imaging applications. Comp. Electron. Agricult. 2008, 63(2):282-293. ISSN 01681699, 10.1016/j.compag.2008.03.009.
34. Meyer G.E., Mehta T., Kocher M.F., Mortensen D.A., Samal A. Textural imaging and discriminant analysis for distinguishing weeds for spot spraying. Trans. ASAE 1998, 41(4):1189-1197. ISSN 0001-2351, http://dx.doi.org/10.13031/2013.17244,
35. Mikolajczyk K., Schmid C. Indexing based on scale invariant interest points. 8th International Conference on Computer Vision 2001, vol. 1:525-531. IEEE Computer Society, http://dx.doi.org/10.1109/ICCV.2001.937561.
36. Mikolajczyk K., Schmid C. Scale and affine invariant interest point detectors. Int. J. Comp. Vis. 2004, 60(1):63-86.
37. Mikolajczyk, K., Zisserman, A., Schmid, C., 2003. Shape recognition with edge-based features. In: Proceedings of the British machine vision conference, vol. 2003, pp. 79.1-79.10, <>. http://www.bmva.org/bmvc/2003/papers/paper-155.html.
38. Mikolajczyk K., Tuytelaars T., Schmid C., Zisserman A., Matas J., Schaffalitzky F., Kadir T., Gool L.V. A comparison of affine region detectors. Int. J. Comp. Vis. 2005, 65(1-2):43-72. ISSN 0920-5691, 10.1007/s11263-005-3848-x.
39. Mikolajczyk K., Leibe B., Schiele B. Local features for object class recognition. Tenth IEEE International Conference on Computer Vision, 2005, ICCV 2005 2005, vol. 2:1792-1799. IEEE, .
40. Miller S., Fornstrom K., Mesbah A. Canada thistle control and competition in sugarbeets. J. Sugar Beet Res. 1994, 31(3-4):87-96. ISSN 0899-1502
41. Mouine S., Yahiaoui I., Verroust-Blondet A. A shape-based approach for leaf classification using multiscale triangular representation. ICMR-3rd ACM International Conference on Multimedia Retrieval 2013, ACM, Dallas, United States,
42. Otsu N. A threshold selection method from gray-level histograms. IEEE Trans. Syst., Man Cybernet. 1979, 9(1):62-66. ISSN 0018-947. 10.1109/TSMC.1979.4310076.
43. Royer D., Wilf P. Correlations of climate and plant ecology to leaf size and shape: potential proxies for the fossil record. Am. J. Bot. 2005, 92(7):1141-1151.
44. Šeatović D. A segmentation approach in novel real time 3D plant recognition system. Lecture Notes in Computer Science 2008, vol. 5008:363-372. Springer, Berlin, Heidelberg, ISBN 978-3-540-79546-9, http://dx.doi.org/10.1007/978-3-540-79547-6_35, A. Gasteratos, M. Vincze, J. Tsotsos (Eds.).
45. Söderkvist, O.J.O., 2001. Computer Vision Classification of Leaves from Swedish Trees, Master's thesis. Linköping University, SE-581 83 Linköping, Sweden.
46. Tuytelaars T., Mikolajczyk K. Local invariant feature detectors: a survey. Found. Trends Comp. Graph. Vis. 2007, 3(3):177-280. ISSN 1572-2740, 10.1561/0600000017.
47. Varytimidis, C., Rapantzikos, K., Avrithis, Y., 2012. WαSH: weighted α-shapes for local feature detection. In: European Conference on Computer Vision (ECCV 2012) <>. http://link.springer.com/chapter/10.1007/978-3-642-33709-3_56.
48. Vedaldi, A., Fulkerson, B., 2008. VLFeat: an open and portable library of computer vision algorithms. In: Ver (0.9.16) <>. http://www.vlfeat.org/.
49. Woebbecke, D.M., Meyer, G.E., Von Bargen, K., Mortensen, D.A., 1992. Plant species identification, size, and enumeration using machine vision techniques on near-binary images. In: Optics in Agriculture and Forestry, SPIE 1836, vol. 1836, pp. 208-219, <>. http://dx.doi.org/10.1117/12.144030.
50. Woebbecke D., Meyer G.E., Von Bargen K., Mortensen D.A. Color indices for weed identification under various soil, residue, and lighting conditions. Trans. ASAE 1995, 38(1):259-269.
51. Zitnick, C.L., Ramnath, K., 2011 Edge foci interest points. In: 2011 International Conference on Computer Vision, pp. 359-366, <>. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6126263.