Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case

Computers and Electronics in Agriculture

Volumn 138, Issue , 2017, Pages 200-209

  Johannes, Alexander ^a   Picon, Artzai ^b   Alvarez Gila, Aitor ^b   Echazarra, Jone ^b   Rodriguez Vaamonde, Sergio ^b   Navajas, Ana Díez ^c   Ortiz Barredo, Amaia ^c  

^a BASF SE   (Germany)

^b TECNALIA RESEARCH AND INNOVATION   (Spain)

^c NEIKER and a Vaccine producing Commercial Company CZVSL   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

DIAGNOSIS; INFERENCE ENGINES; PEST CONTROL;

AUTOMATED IDENTIFICATION SYSTEMS; CORRECTIVE ACTIONS; HOT SPOT DETECTION; IMAGE PROCESSING ALGORITHM; INTEGRATED PEST MANAGEMENT STRATEGIES; PLANT DISEASE DIAGNOSIS; RECEIVER OPERATING CHARACTERISTICS; STATISTICAL INFERENCE;

IMAGE PROCESSING;

ALGORITHM; DISEASE INCIDENCE; IMAGE PROCESSING; INTEGRATED PEST MANAGEMENT; MOBILE PHONE; SYMPTOM; WHEAT;

GERMANY; SPAIN;

SEPTORIA; TRITICUM AESTIVUM;

EID: 85018415591     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2017.04.013     Document Type: Article

References (32)

1. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S., SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34:11 (2012), 2274–2282.
2. Agrios, G.N., 2006. Plant Pathology, fifth ed. Academic Press, p. 952. ISBN: 9780120445653.
3. Bradley, A.P., The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn. 30:7 (1997), 1145–1159.
4. Buchsbaum, G., A spatial processor model for object colour perception. J. Franklin Inst. 310:1 (1980), 1–2.
5. Chai, Y., Pardey, P., Beddow, J., Hurley, T., Kriticos, D., Joachim-Braun, H., The global occurrence and economic consequences of stripe rust in wheat. Advancing Pest and Disease Modeling Workshop, 2015, University of Minnesota, CSIRO, and CIMMYT (accessed 14th October 2016).
6. Chan, T.F., Vese, L.A., Active contours without edges. IEEE Trans. Image Process. 10:2 (2001), 266–277.
7. Clark, B., Bryson, R., Tonguc, L., Kelly, C., Jellis, G., 2010. The encyclopaedia of cereal diseases. HGCA and BASF plc, Crop Protection.
8. Coakley, S.M., Scherm, H., Chakraborti, S., Climate change and plant disease management. Annu. Rev. Phytopathol. 37 (1999), 399–426.
9. “Computer Vision - A Modern Approach”, by Forsyth, Ponce, Pearson Education ISBN 0-13-085198-1, pp. 301–307.
10. Fawcett, T., An introduction to ROC analysis. Pattern Recogn. Lett. 27:8 (2006), 861–874.
11. Felzenszwalb, P.F., Huttenlocher, D.P., Efficient graph-based image segmentation. Int. J. Comput. Vision, 59, 2004, 167, 10.1023/B:VISI.0000022288.19776.77.
12. Finlayson, G.D., Trezzi, E., 2004. Shades of gray and colour constancy. In: Color and Imaging Conference Jan (1), pp. 37–41.
13. Finlayson, G.D., Mackiewicz, M., Hurlbert, A., Color correction using root-polynomial regression. IEEE Trans. Image Process. 24:5 (2015), 1460–1470.
14. Garrett, K.A., Dendy, S.P., Frank, E.E., Rouse, M.N., Travers, S.E., Climate change effects on plant disease: genomes to ecosystems. Annu. Rev. Phytopathol. 44 (2007), 489–509.
15. Herrera-Foessel, S.A., Singh, R.P., Huerta-Espino, J., Crossa, J., Yuen, J., Djurle, A., Effect of leaf rust on grain yield and yield traits of durum wheats with race-specific and slow-rusting resistance to leaf rust. Plant Dis. 90:8 (2006), 1065–1072.
16. Huang, W., Lamb, D.W., Niu, Z., Zhang, Y., Liu, L., Wang, J., Identification of yellow rust in wheat using in situ spectral reflectance measurements and airborne hyperspectral imaging. Precision Agric. 8:4–5 (2007), 187–197.
17. Hughes, D.P., Salathé, M., 2015. An open access repository of images on plant health to enable the development of mobile disease diagnostics. Computers and Society. In Computer Science. Cornell University Library. arXiv:1511.08060v2 [cs.CY].
18. Land, E.H., McCann, J.J., Lightness and retinex theory. JOSA 61:1 (1977), 1–11.
19. Lewis, D.D., Naive (Bayes) at forty: the independence assumption in information retrieval. European Conference on Machine Learning, 1998, Springer, Berlin Heidelberg, 4–15 April.
20. Mahlein, A.K., Rumpf, T., Welke, P., Dehne, H.W., Plümer, L., Steiner, U., Oerke, E.C., Development of spectral indices for detecting and identifying plant diseases. Remote Sens. Environ. 128 (2013), 21–30.
21. Mohanty, S.P., Hughes, D.P., Salathé, M., Using deep learning for image-based plant disease detection. Front. Plant Sci., 7, 2016.
22. Moshou, D., Bravo, C., Oberti, R., West, J., Bodria, L., McCartney, A., Ramón, H., Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps. Real Time Imaging 11:2 (2005), 75–83.
23. Oerke, E.C., Crop losses to pests. J. Agric. Sci. 144 (2006), 31–43.
24. Ojala, T., Pietikainen, M., Maenpaa, T., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24:7 (2002), 971–987.
25. Sankaran, S., Mishra, A., Ehsani, R., Davis, C., A review of advanced techniques for detecting plant diseases. Comput. Electr. Agric. 72:1 (2010), 1–13.
26. Sannakki, Sanjeev S., Rajpurohit, Vijay S., Nargund, V.B., Kumar, Arun, Yallur, Prema S., Leaf disease grading by machine vision and fuzzy logic. Int. J. Comput. Technol. Appl. 2:5 (2011), 1709–1716.
27. Savary, S., Ficke, A., Aubertot, J.N., Hollier, C., Crop losses due to diseases and their implications for global food production losses and food security. Food Secur., 2012, 10.1007/s12571-012-0200-5.
28. Siricharoen, P., Scotney, B., Morrow, P., Parr, G., A lightweight mobile system for crop disease diagnosis. Campilho, A., Karray, F., (eds.) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science, vol. 9730, 2016, Springer, Cham.
29. Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., Stefanovic, D., Deep neural networks based recognition of plant diseases by leaf image classification. Comput. Intelligence Neurosci., 2016, 10.1155/2016/328980.
30. van de Weijer, J., Gevers, T., Gijsenij, A., Edge-based color constancy. IEEE Trans. Image Process. 16:9 (2007), 2207–2214.
31. Vedaldi, A., Soatto, S., 2008. Quick shift and kernel methods for mode seeking. In: European Conference on Computer Vision, Marseille, France.
32. Xie, X., Zhang, X., He, B., Liang, D., Zhang, D., Huang, L., A system for diagnosis of wheat leaf diseases based on Android smartphone. Opt. Measur. Technol. Instr., 2016, 10.1117/12.2246919.