ARCHIVES
Research Article
Crop Disease Detection Using Neural Network and Machine Learning Algorithms
Pranav Bansal1
Ishaan Gupta2
Rajas Paunikar3
123 Computer Science, Maharaja Agrasen Institute of Technology, Delhi, India.
Published Online: January-February 2023
Pages: 36-41
Cite this article
No DOIReferences
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leaf image classification,” Computational Intelligence and Neuroscience, p. 11, 2016.
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16. J. Feng, N.-F. Liao, B. Zhao, Y.-D. Luo, and B.-J. Li, “Cucumber diseases diagnosis using multispectral imaging technique,” Guang pu
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computer vision and fluorescence imaging spectroscopy for detection of huanglongbing citrus disease in the usa and brazil,” Journal of
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19. L. Bo, Y. Yue-Min, L. Ru, S. Wen-Jing, and W. Ke-Lin, “Plant leaf chlorophyll content retrieval based on a field imaging spectroscopy
system,” Sensors, vol. 14, no. 10, 2014.
20. Y. Yang, R. Chai, and Y. He, “Early detection of rice blast (pyricularia) at seedling stage in nipponbare rice variety using near-infrared
hyper-spectral image,” African Journal of Biotechnology, vol. 11, pp. 6809–6817, 2012.
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23. E. Arias-Castro and D. L. Donoho, “Does median filtering truly preserve edges better than linear filtering?” The Annals of Statistics,
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Perspectives in Science, vol. 8, pp. 510 – 512, 2016.
26. I. Jolliffe, “Principal component analysis,” Springer Verlag, 2002.
27. K. Korjus, “Machine learning-principal component analysis,” University of Tartu-Institute of Computer Science courses - Spring
Technical Report, 2016.
28. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J.
Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal
of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
29. T. Kohonen, “Learning vector quantization for pattern recognition,” Technical Report TKKF-A601, Helsinki Univeristy of Technology,
Espoo, Finland., 1986.
30. A. Sato and K. Yamada, “Generalized learning vector quantization,” Hasselmo (Eds.), NIPS, pp. 423–429, 1995.
31. P. Schneider, M. Biehl, and B. Hammer, “Relevance matrices in lvq,” Proc. European Symposium on Artificial Neural Networks, pp.
37–42, 2007.
32. B. Hammer, S. Marc, and V. Thomas, “On the generalization ability of grlvq networks,” Neural Process. Lett., pp. 109–120, 2005.
33. M. Biehl, “A no-nonsense gmlvq toolbox,” University of Groningen, The Netherlands, 2016. [Online]. Available:
34. http://matlabserver.cs.rug.nl/gmlvqweb/web/
35. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J.
Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal
of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
International Journal of Plant Physiology and Biochemistry, vol. 7(2), pp. 12–22, 2015.
2. J. R. Aduwo, E. Mwebaze, and J. A. Quinn, “Automated vision-based diagnosis of cassava mosaic disease,” Industrial Conference on
Data Mining - Workshops, pp. 114–122, 2010.
3. E. Mwebaze and M. Biehl, “Prototype-based classification for image analysis and its application to crop disease diagnosis,” Advances
in Self-Organizing Maps and Learning Vector Quantization - Proceedings of the 11th International Workshop WSOM 2016, pp. 329–
339, January 2016.
4. G. Owomugisha and E. Mwebaze, “Machine learning for plant disease incidence and severity measurements from leaf images,” 15th
IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 158–163, 2016.
5. A. Ramcharan, K. Baranowski, P. McCloskey, B. Ahmed, J. Legg, and D. P. Hughes, “Deep learning for image-based cassava disease
detection,” Frontiers in Plant Science, vol. 8, p. 1852, 2017.
6. E. Mwebaze, P. Schneider, F.-M. Schleif, J. Aduwo, J. Quinn, S. Haase, T. Villmann, and M. Biehl, “Divergence-based classification in
learning vector quantization,” Neurocomputing, vol. 74, no. 9, pp. 1429 – 1435, 2011.
7. E. Mwebaze, M. Biehl, G. Bearda, and D. Zuehlke, “Combining dissimilarity measures for prototypebased classification,” European
Symposium on Artificial Neural Networks (ESANN), vol. 23, pp. 31– 36, 2015.
8. S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, and D. Stefanovic, “Deep neural networks based recognition of plant diseases by
leaf image classification,” Computational Intelligence and Neuroscience, p. 11, 2016.
9. K. K. R. Gokulakrishnan, “Detecting the plant diseases and issues by image processing technique and broadcasting,” International
Journal of Science and Research, vol. 3, 2014.
10. S. D. Khirade and A. B. Patil, “Plant disease detection using image processing,” Proceedings of the 2015 International Conference on
Computing Communication Control and Automation, pp. 768–771, 2015.
11. M. Nixon and A. S. Aguado, “Feature extraction & image processing for computer vision, third edition,” Academic Press, 2012.
12. W. Johanna, B. Stenberg, and R. A. V. Rossel, “Soil analysis using visible and near infrared spectroscopy,” Plant Mineral Nutrients:
Methods and Protocols, 2013.
13. L. Raphael, “Application of ftir spectroscopy to agricultural soils analysis,” Chapter from the Book Fourier Transforms, 2011.
14. S. Sindhuja, M. Ashish, E. Reza, and D. Cristina, “A review of advanced techniques for detecting plant diseases,” Computers and
Electronics in Agriculture, vol. 72, pp. 1 – 13, 2010.
15. J. Feng, N.-F. Liao, M.-Y. Liang, B. Zhao, and Z.-F. Dai, “Multispectral imaging system for the plant diseases and insect pests
diagnosis,” Guang Pu Xue Yu Guang Pu Fen Xi, 2009.
16. J. Feng, N.-F. Liao, B. Zhao, Y.-D. Luo, and B.-J. Li, “Cucumber diseases diagnosis using multispectral imaging technique,” Guang pu
xue yu guang pu fen xi = Guang pu, 2009.
17. J. J. Belasque, M. C. G. Gasparoto, and L. G. Marcassa, “Detection of mechanical and disease stresses in citrus plants by fluorescence
spectroscopy,” Applied Optics, vol. 47, no. 11, pp. 1922–1926, 2008.
18. C. B. Wetterich, R. Kumar, S. Sankaran, J. J. Belasque, R. Ehsani, and L. G. Marcassa, “A comparative study on application of
computer vision and fluorescence imaging spectroscopy for detection of huanglongbing citrus disease in the usa and brazil,” Journal of
Spectroscopy, 2013.
19. L. Bo, Y. Yue-Min, L. Ru, S. Wen-Jing, and W. Ke-Lin, “Plant leaf chlorophyll content retrieval based on a field imaging spectroscopy
system,” Sensors, vol. 14, no. 10, 2014.
20. Y. Yang, R. Chai, and Y. He, “Early detection of rice blast (pyricularia) at seedling stage in nipponbare rice variety using near-infrared
hyper-spectral image,” African Journal of Biotechnology, vol. 11, pp. 6809–6817, 2012.
21. C. B.-S. Inc, “Ci-710 miniature leaf spectrometer,” 2010. [Online]. Available: http://www.cid-inc.com
22. G. Bradski, “The OpenCV Library,” Dr. Dobb’s Journal of Software Tools, 2000.
23. E. Arias-Castro and D. L. Donoho, “Does median filtering truly preserve edges better than linear filtering?” The Annals of Statistics,
2009.
24. S. W. Smith, “Moving average filters,” The Scientist and Engineer’s Guide to Digital Signal Processing, pp. 277–284, 1999.
25. K. K. Vasan and B. Surendiran, “Dimensionality reduction using principal component analysis for network intrusion detection,”
Perspectives in Science, vol. 8, pp. 510 – 512, 2016.
26. I. Jolliffe, “Principal component analysis,” Springer Verlag, 2002.
27. K. Korjus, “Machine learning-principal component analysis,” University of Tartu-Institute of Computer Science courses - Spring
Technical Report, 2016.
28. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J.
Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal
of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
29. T. Kohonen, “Learning vector quantization for pattern recognition,” Technical Report TKKF-A601, Helsinki Univeristy of Technology,
Espoo, Finland., 1986.
30. A. Sato and K. Yamada, “Generalized learning vector quantization,” Hasselmo (Eds.), NIPS, pp. 423–429, 1995.
31. P. Schneider, M. Biehl, and B. Hammer, “Relevance matrices in lvq,” Proc. European Symposium on Artificial Neural Networks, pp.
37–42, 2007.
32. B. Hammer, S. Marc, and V. Thomas, “On the generalization ability of grlvq networks,” Neural Process. Lett., pp. 109–120, 2005.
33. M. Biehl, “A no-nonsense gmlvq toolbox,” University of Groningen, The Netherlands, 2016. [Online]. Available:
34. http://matlabserver.cs.rug.nl/gmlvqweb/web/
35. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J.
Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal
of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
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