Controlled comparison of machine vision algorithms for Rumex and Urtica detection in grassland

Computers and Electronics in Agriculture

Volumn 140, Issue , 2017, Pages 123-138

  Binch, A ^a,b   Fox, C W ^a,b  

^a UNIVERSITY OF LEEDS   (United Kingdom)

^b Ibex Automation Ltd   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

SUPPORT VECTOR MACHINES; WEED CONTROL;

BINARY PATTERNS; ENVIRONMENT CONDITIONS; LINEAR DISCRIMINANTS; MACHINE VISION ALGORITHM; META-CLASSIFIERS; NEAREST NEIGHBOUR; ROBOTIC WEEDING; WEED DETECTION;

COMPUTER VISION;

ALGORITHM; AUTOMATION; COMPARATIVE STUDY; DATA SET; DETECTION METHOD; GRASS; GRASSLAND; HERB; PERFORMANCE ASSESSMENT; ROBOTICS; SHEEP; SUPPORT VECTOR MACHINE; WEED;

OVIS ARIES; RUMEX; URTICA;

EID: 85020289818     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2017.05.018     Document Type: Article

References (39)

1. Agrawal, Motilal, Konolige, Kurt, Blas, Morten Rufus, Censure: center surround extremas for realtime feature detection and matching. European Conference on Computer Vision, 2008, Springer, 102–115.
2. Ahmed, Faisal, Kabir, Md Hasanul, Bhuyan, Shayla, Bari, Hossain, Hossain, Emam, Automated weed classification with local pattern-based texture descriptors. Int. Arab J. Inf. Technol. 11:1 (2014), 87–94.
3. Anken, T., Holpp, M., Venn, W., Kutterer, H.J., et al. Automatic detection of broad-leaved dock in grassland. International Conference on Agricultural Engineering-AgEng 2010: Towards Environmental Technologies, Clermont-Ferrand, France, 6–8 September 2010. CEMAGREF, 2010.
4. Bechar, Avital, Vigneault, Clément, Agricultural robots for field operations: concepts and components. Biosyst. Eng. 149 (2016), 94–111.
5. Bernardo, José M., Smith, Adrian F.M., 2001. Bayesian theory.
6. Blackmore, Simon, Stout, Bill, Wang, Maohua, Runov, Boris, Robotic agriculture – the future of agricultural mechanisation. Proceedings of the 5th European Conference on Precision Agriculture, 2005, 621–628.
7. Cope, James S., Corney, David, Clark, Jonathan Y., Remagnino, Paolo, Wilkin, Paul, Plant species identification using digital morphometrics: a review. Expert Syst. Appl. 39:8 (2012), 7562–7573.
8. Dietterich, Thomas G., Ensemble methods in machine learning. International Workshop on Multiple Classifier Systems, 2000, Springer, 1–15.
9. Domingos, Pedro, Bayesian averaging of classifiers and the overfitting problem. ICML, vol. 2000, 2000, 223–230.
10. Dürr, L., Anken, T., Bollhalder, H., Sauter, J., Burri, K.G., Kuhn, D., Machine vision detection and microwave based elimination of Rumex obtusifolius L. on grassland. 5th European Conference on Precision Agriculture, Uppsala, Sweden, 2005, 5.
11. Friedman, Jerome, Hastie, Trevor, Tibshirani, Robert, 2001. The Elements of Statistical Learning, Springer Series in Statistics, vol. 1, Springer, Berlin.
12. Gebhardt, Steffen, Schellberg, Jürgen, Lock, Reiner, Kühbauch, Walter, Identification of broad-leaved dock (Rumex obtusifolius L.) on grassland by means of digital image processing. Prec. Agric. 7:3 (2006), 165–178.
13. Guo, Lei, Li, Qingshan, LBP and its variations for image classification. Proceedings of the 2012 International Conference on Electronics, Communications and Control, 2012, IEEE Computer Society, 712–715.
14. Hamuda, Esmael, Glavin, Martin, Jones, Edward, A survey of image processing techniques for plant extraction and segmentation in the field. Comput. Electron. Agric. 125 (2016), 184–199.
15. He, Dong-Chen, Wang, Li, Texture unit, texture spectrum, and texture analysis. IEEE Trans. Geosci. Rem. Sens. 28:4 (1990), 509–512.
16. Hinton, Geoffrey E., Osindero, Simon, Teh, Yee-Whye, A fast learning algorithm for deep belief nets. Neural Comput. 18:7 (2006), 1527–1554.
17. Hiremath, S., Van der Heijden, G., Van Evert, F.K., Stein, A., The role of textures to improve the detection accuracy of Rumex obtusifolius in robotic systems. Weed Res. 52:5 (2012), 430–440.
18. Hiremath, Santosh, Tolpekin, Valentyn A, Heijden, Gerie van der, Stein, Alfred, Segmentation of Rumex obtusifolius using gaussian markov random fields. Mach. Vis. Appl. 24:4 (2013), 845–854.
19. Hsu, Chih-Wei, Chang, Chih-Chung, Lin, Chih-Jen, et al. 2003. A Practical Guide to Support Vector Classification. Tech. Report. < https://www.csie.ntu.edu.tw/cjlin/>.
20. Leutenegger, Stefan, Chli, Margarita, Siegwart, Roland Y., Brisk: binary robust invariant scalable keypoints. 2011 International Conference on Computer Vision, 2011, IEEE, 2548–2555.
21. McCarthy, Cheryl, Rees, Steven, Baillie, Craig, Machine vision-based weed spot spraying: a review and where next for sugarcane?. Proceedings of the 32nd Annual Conference of the Australian Society of Sugar Cane Technologists (ASSCT 2010), vol. 32, 2010, Australian Society of Sugar Cane Technologists, 424–432.
22. McLachlan, Geoffrey, Discriminant Analysis and Statistical Pattern Recognition, vol. 544, 2004, John Wiley & Sons.
23. Moshou, D., Kateris, D., Pantazi, X.E., Gravalos, I., Crop and weed species recognition based on hyperspectral sensing and active learning. Precision Agriculture, 2013, Springer, 555–561.
24. Pantazi, Xanthoula-Eirini, Moshou, Dimitrios, Bravo, Cedric, Active learning system for weed species recognition based on hyperspectral sensing. Biosyst. Eng. 146 (2016), 193–202.
25. Patel, Akash, Kasat, D.R., Jain, Sanjeev, Thakare, V.M., Performance analysis of various feature detector and descriptor for real-time video based face tracking. Int. J. Comput. Appl., 93(1), 2014.
26. Peteinatos, Gerassimos G., Weis, Martin, Andújar, Dionisio, Ayala, Victor Rueda, Gerhards, Roland, Potential use of ground-based sensor technologies for weed detection. Pest Manage. Sci. 70:2 (2014), 190–199.
27. Polder, Gerrit, van Evert, Frits K., Lamaker, Arjan, De Jong, Arjan, Van der Heijden, G.W.A.M., Lotz, L.A.P., Van der Zalm, T., Kampenaar, C., 2007. Weed Detection using Textural Image Analysis. Plant Research International, PO Box, 16:6700.
28. Rosenthal, Robert, The file drawer problem and tolerance for null results. Psychol. Bull., 86(3), 1979, 638.
29. ŠEATOVIĆ, Dejan, 3d-object recognition, localization and treatment of Rumex obtusifolius in its natural environment. 1 st International Conference on Machine Control & Guidance, 2008, 169.
30. Šeatović, Dejan, Kutterer, Hansjörg, Anken, Thomas, Automatic weed detection and treatment in grasslands. ELMAR, 2010 Proceedings, 2010, IEEE, 65–68.
31. Siddiqi, Muhammad Hameed, Ahmad, Irshad, Sulaiman, Suziah Bt, Weed recognition based on erosion and dilation segmentation algorithm. 2009 International Conference on Education Technology and Computer, 2009, IEEE, 224–228.
32. Tillett, N.D., Marchant, John A., Hague, A., Autonomous plant scale crop protection. AgEng96, Eur. Soc. Agric. Eng. A 96 (1996), 23–26.
33. Van Evert, F.K., Polder, G., Van Der Heijden, G.W.A.M., Kempenaar, C., Lotz, L.A.P., Real-time vision-based detection of Rumex obtusifolius in grassland. Weed Res. 49:2 (2009), 164–174.
34. van Evert, Frits K., Samsom, Joost, Polder, Gerrit, Vijn, Marcel, Dooren, Hendrik-Jan van, Lamaker, Arjan, van der Heijden, Gerie W.A.M., Kempenaar, Corné, Zalm, Ton van der, Lotz, Lambertus A.P., A robot to detect and control broad-leaved dock (Rumex obtusifolius L.) in grassland. J. Field Robot. 28:2 (2011), 264–277.
35. Vapnik, Vladimir, The Nature of Statistical Learning Theory. 2013, Springer Science & Business Media.
36. Vibhute, Anup, Bodhe, S.K., Applications of image processing in agriculture: a survey. Int. J. Comput. Appl., 52(2), 2012.
37. Vincent, Luc, Soille, Pierre, Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 13:6 (1991), 583–598.
38. Vrindts, Els, Baerdemaeker, Josse De, Optical weed detection and evaluation using reflection measurements. Photonics East (ISAM, VVDC, IEMB), 1999, International Society for Optics and Photonics, 279–289.
39. Weisstein, Eric W., 2015. Fast Fourier Transform. Wolfram World of Mathematics (Online Resource).