Automatic detection of thistle-weeds in cereal crops from aerial RGB images

Communications in Computer and Information Science

Volumn 855, Issue , 2018, Pages 441-452

  Franco, Camilo ^a   Guada, Carely ^b   Rodríguez, J Tinguaro ^b   Nielsen, Jon ^c   Rasmussen, Jesper ^c   Gómez, Daniel ^b   Montero, Javier ^b  

^a UNIVERSIDAD DE LOS ANDES   (Colombia)

^b UNIVERSIDAD COMPLUTENSE DE MADRID   (Spain)

^c UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

Image analysis; k Nearest neighbours; Neural networks; Precision agriculture; Soft measures; Weed detection

Indexed keywords

CROPS; IMAGE ANALYSIS; INFORMATION MANAGEMENT; KNOWLEDGE BASED SYSTEMS; NEAREST NEIGHBOR SEARCH; NETWORK LAYERS; NEURAL NETWORKS; OBJECT DETECTION; PIXELS; PRECISION AGRICULTURE; UNMANNED AERIAL VEHICLES (UAV); WEED CONTROL;

AERIAL IMAGES; AGRICULTURAL SITES; AUTOMATIC DETECTION; K NEAREST NEIGHBOURS (K-NN); K-NEAREST NEIGHBOURS; SITE-SPECIFIC; SOFT MEASURES; WEED DETECTION;

ANTENNAS;

EID: 85048054602     PISSN: 18650929     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-91479-4_37     Document Type: Conference Paper

References (21)

1. Andrew, M.E., Ustin, S.L.: The role of environmental context in mapping invasive plants with hyperspectral image data. Remote Sens. Environ. 112, 4301–4317 (2008)
2. Blaschke, T.: Object based image analysis for remote sensing. ISPRS J. Photogram-metry Remote Sens. 65, 2–16 (2010)
3. Blaschke, T., Burnett, C., Pekkarinen, A.: New contextual approaches using image segmentation for object-based classification. In: De Meer, F., de Jong, S. (eds.) Remote Sensing Image Analysis: Including the Spatial Domain, pp. 211–236. Klu-ver Academic Publishers, Dordrecht (2004)
4. Duro, D.C., Franklin, S.E., Dubé, M.G.: A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sens. Environ. 118, 259–272 (2012)
5. Egorov, A.V., Hansen, M.C., Roya, D.P., Kommareddy, A., Potapov, P.V.: Image interpretation-guided supervised classification using nested segmentation. Remote Sens. Environ. 165, 135–147 (2015)
6. Franco, C., Pedersen, S.M., Papaharalampos, H., Ørum, J.E.: The value of precision for image-based decision support in weed management. Precis. Agric. 18, 366–382 (2017)
7. Gómez, D., Biging, G., Montero, J.: Accuracy statistics for judging soft classification. Int. J. Remote Sens. 29, 693–709 (2008)
8. Gómez, D., Biging, G.S., Montero, J.: Accuracy assessment for soft classification maps. In: Wang, G., Weng, Q. (eds.) Remote Sensing of Natural Resources, pp. 57–86. CRC Press, Boca Raton (2014)
9. Guada, C., Gómez, D., Rodríguez, J.T., Yáñez, J., Montero, J.: Classifying image analysis techniques from their output. Int. J. Comput. Intell. Syst. 9, 43–68 (2016)
10. Guada, C., Zarrazola, E., Yáñez, J., Rodríguez, J.T., Gómez, D., Montero, J.: A novel edge detection algorithm based on a hierarchical graph-partition approach. J. Intell. Fuzzy Syst. 34(3), 1875–1892 (2018, in press)
11. Haykin, S.: Neural Networks. A Comprehensive Foundation. Prentice Hall International, Upper Saddle River (1999)
12. Lamb, D.W., Brown, R.B.: Remote-sensing and mapping of weeds in crops. Agri. Eng. Res. 78, 117–125 (2001)
13. Moran, M.S., Inoue, Y., Barnes, E.M.: Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sens. Environ. 61, 319–346 (1997)
14. Myint, S.W., Gober, P., Brazel, A., Grossman-Clarke, S., Weng, Q.: Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens. Environ. 115, 1145–1161 (2011)
15. Rasmussen, J., Nielsen, J., Garcia-Ruiz, F., Christensen, S., Streibig, J.C.: Potential uses of small unmanned aircraft systems (UAS) in weed research. Weed Res. 53, 242–248 (2013)
16. Robertson, L.D., King, D.J.: Comparison of pixel and object based classification in land cover mapping. Int. J. Remote Sens. 32, 1505–1529 (2011)
17. Seelan, S.K., Laguette, S., Casady, G.M., Seielstad, G.A.: Remote sensing applications for precision agriculture: a learning community approach. Remote Sens. Environ. 88, 157–169 (2003)
18. Slaughter, D.C., Giles, D.K., Downey, D.: Autonomous robotic weed control systems: a review. Comput. Electron. Agric. 61, 63–78 (2008)
19. Sørensen, R., Rasmussen, J., Nielsen, J., Jørgensen, R.N.: Thistle detection using convolutional neural networks. In: Proceedings EFITA-WCCA 2017 Conference, Montpellier, France, paper 75, 2–6 July 2017
20. Tellaeche, A., Pajares, G., Burgos-Artizzu, X.P., Ribeiro, A.: A computer vision approach for weeds identification through support vector machines. Appl. Soft Comput. 11, 908–915 (2011)
21. Whiteside, T.G., Boggs, G.S., Maier, S.W.: Comparing object-based and pixel-based classifications for mapping savannas. Appl. Earth Obs. Geoinf. 13, 884–893 (2011)