Details
Title
IoT based Automated Plant Disease Classification using Support Vector MachineJournal title
International Journal of Electronics and TelecommunicationsYearbook
2021Volume
vol. 67Issue
No 3Affiliation
Mewada, Hiren : Faculty of Electrical Engineering, Prince Mohammad Bin Fahd University, Al Kobhar, Kingdom of Saudi Arabai ; Patoliaya, Jignesh : Charotar University of Science and Technology, Changa, IndiaAuthors
Keywords
Plant Disease classification ; Support vector machine (SVM) ; Graph Cut ; Gray-level Co-occurance MatrixDivisions of PAS
Nauki TechniczneCoverage
517-522Publisher
Polish Academy of Sciences Committee of Electronics and TelecommunicationsBibliography
[1] A. Akhtar, A. Khanum, S. A. Khan, and A. Shaukat, “Automated plant disease analysis (apda): performance comparison of machine learning techniques,” in 2013 11th International Conference on Frontiers of Information Technology. IEEE, 2013, pp. 60–65.[2] M. H. Saleem, J. Potgieter, and K. M. Arif, “Plant disease detection and classification by deep learning,” Plants, vol. 8, no. 11, p. 468, 2019.
[3] R. M. Haralick, K. Shanmugam, and I. H. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man, and Cybernetics, no. 6, pp. 610–621, 1973.
[4] J. Isleib, Signs and symptoms of plant disease, 2019 (accessed February 3, 2019). [Online]. Available: https://www.canr.msu.edu/news/signs and symptoms of plant disease is it fungal viral or bacterial
[5] R. Borges, M. Rossato, M. Santos, M. Ferreira, M. Fonseca, A. Reis, and L. Boiteux, “First report of a leaf spot caused by paramyrothecium roridum on tectona grandis in brazil,” Plant Disease, vol. 102, no. 8, pp. 1661–1661, 2018.
[6] H. K. Mewada, A. V. Patel, and K. K. Mahant, “Concurrent design of active contour for image segmentation using zynq zc702,” Computers & Electrical Engineering, vol. 72, pp. 631–643, 2018.
[7] M. Rzanny, M. Seeland, J. W¨aldchen, and P. M¨ader, “Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain,” Plant methods, vol. 13, no. 1, pp. 1–11, 2017.
[8] D. Vukadinovic and G. Polder, “Watershed and supervised classification based fully automated method for separate leaf segmentation,” in The Netherland Congress on Computer Vision, 2015, pp. 1–2.
[9] C. Niu, H. Li, Y. Niu, Z. Zhou, Y. Bu, and W. Zheng, “Segmentation of cotton leaves based on improved watershed algorithm,” in International Conference on Computer and Computing Technologies in Agriculture. Springer, 2015, pp. 425–436.
[10] L. S. Pinto, A. Ray, M. U. Reddy, P. Perumal, and P. Aishwarya, “Crop disease classification using texture analysis,” in 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE, 2016, pp. 825–828.
[11] A. Abraham, R. Falcon, and M. Koeppen, Computational Intelligence in Wireless Sensor Networks: Recent Advances and Future Challenges. Springer, 2017, vol. 676.
[12] S. Hu, H. Wang, C. She, and J. Wang, “Agont: ontology for agriculture internet of things,” in International Conference on Computer and Computing Technologies in Agriculture. Springer, 2010, pp. 131–137.
[13] Y. Shi, Z. Wang, X. Wang, and S. Zhang, “Internet of things application to monitoring plant disease and insect pests,” in 2015 International conference on Applied Science and Engineering Innovation. Atlantis Press, 2015.
[14] A. Kapoor, S. I. Bhat, S. Shidnal, and A. Mehra, “Implementation of iot (internet of things) and image processing in smart agriculture,” in 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS). IEEE, 2016, pp. 21–26.
[15] N. Anantrasirichai, S. Hannuna, and N. Canagarajah, “Automatic leaf extraction from outdoor images,” arXiv preprint arXiv:1709.06437, 2017.
[16] V. Singh and A. K. Misra, “Detection of plant leaf diseases using image segmentation and soft computing techniques,” Information processing in Agriculture, vol. 4, no. 1, pp. 41–49, 2017.
[17] N. P. Singh, R. Kumar, and R. Srivastava, “Local entropy thresholding based fast retinal vessels segmentation by modifying matched filter,” in International Conference on Computing, Communication & Automation. IEEE, 2015, pp. 1166–1170.
[18] E. Hamuda, M. Glavin, and E. Jones, “A survey of image processing techniques for plant extraction and segmentation in the field,” Computers and Electronics in Agriculture, vol. 125, pp. 184–199, 2016.
[19] B.-y. Sun and M.-c. Lee, “Support vector machine for multiple feature classifcation,” in 2006 IEEE International Conference on Multimedia and Expo. IEEE, 2006, pp. 501–504.
[20] S. Arivazhagan, R. N. Shebiah, S. Ananthi, and S. V. Varthini, “Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features,” Agricultural Engineering International: CIGR Journal, vol. 15, no. 1, pp. 211–217, 2013.
[21] S. Arivazhagan, R. Shebiah, S. Ananthi, and S. Varthini, “Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features,” Agricultural Engineering International: CIGR Journal, vol. 15, no. 1, pp. 211–217, 2013.