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Reseach Article

A Survey on Detecting Plant Diseases Detection and Proposal of a Solution using Recommendation System

by Hanif Khan Pathan, Dhanraj Verma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 38
Year of Publication: 2021
Authors: Hanif Khan Pathan, Dhanraj Verma
10.5120/ijca2021921784

Hanif Khan Pathan, Dhanraj Verma . A Survey on Detecting Plant Diseases Detection and Proposal of a Solution using Recommendation System. International Journal of Computer Applications. 183, 38 ( Nov 2021), 39-44. DOI=10.5120/ijca2021921784

@article{ 10.5120/ijca2021921784,
author = { Hanif Khan Pathan, Dhanraj Verma },
title = { A Survey on Detecting Plant Diseases Detection and Proposal of a Solution using Recommendation System },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2021 },
volume = { 183 },
number = { 38 },
month = { Nov },
year = { 2021 },
issn = { 0975-8887 },
pages = { 39-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number38/32182-2021921784/ },
doi = { 10.5120/ijca2021921784 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:19:02.898819+05:30
%A Hanif Khan Pathan
%A Dhanraj Verma
%T A Survey on Detecting Plant Diseases Detection and Proposal of a Solution using Recommendation System
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 38
%P 39-44
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Agriculture is the backbone of the Indian economy. A significant amount of people in India depends on agricultural income. But due to traditional methods of farming and dependency on nature, infection, and other different diseases, the production, and profit of crops are affected, results in poor quality and productivity. This paper is motivated to investigate the recent advancement in the agriculture based on computational technology. Therefore, recent technique of efficient and accurate plant disease detection using Machine Learning (ML) and image processing techniques has proposed to study. A survey has been carried out and the summary of conducted review has been reported. The reviewed literature is focused on categorizing the methods based on ML algorithms used. Additionally, the trends of utilization of ML techniques are also described. Using the concluded reviews and available facts we proposed a ML model for early disease prediction, and also proposed a recommendation system which provides relevant solution to deal with the disease is described. The different components of both the models and required functional aspects are also discussed. Finally, the paper provides the conclusion of the work carried out and future guidelines.

References
  1. “Agricultural Education”, http://www.hillagric.ac.in/aboutus /vc/vc_addresses/pdf/2017/08.12.2017-Agri.Edu.Day-03.12. 2017.pdf
  2. P. Shrivastava, R. Kumar, “Soil salinity: A serious environmental issue and plant growth promoting bacteria as one of the tools for its alleviation”, Sau. Jour. of Biol. Sci., 2015, 22, 123-131
  3. K. G. Liakos, P. Busato, D. Moshou, S. Pearson, D. Bochtis, “Machine Learning in Agriculture: A Review”, Sensors 2018, 18, 2674; doi:10.3390/s18082674
  4. K. Kang, H. Li, J. Yan, X. Zeng, B. Yang, T. Xiao, C. Zhang, Z. Wang, R. Wang, X. Wang, W. Ouyang, “T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos”, Copyright 2017 IEEE
  5. M. Wang, Y. Wan, Z. Ye, X. Lai, “Remote sensing image classification based on the optimal support vector machine and modified binary coded ant colony optimization algorithm”, Infor. Scie., 402, 2017, 50-68
  6. G. Wang, W. Li , M. A. Zuluaga, R. Pratt, P. A. Patel, M. Aertsen, T. Doel, A. L. David, J. Deprest, S. Ourselin, T. Vercauteren, “Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning”, IEEE Trans. on Med. Ima., Vol. 37, No. 7, July 2018
  7. J. E. van Engelen, H. H. Hoos, “A survey on semi-supervised learning”, Mac. Lea., https://doi.org/10.1007/s10994-019-05855-6
  8. Mrs. U. Shruthi, Dr. V. Nagaveni, Dr. B. K. Raghavendra, “A Review on Machine Learning Classification Techniques for Plant Disease Detection”, 5th Inte. Conf. on Adv. Comp. & Comm. Sys., 978-1-5386-9533-3/19/$31.00 ©2019 IEEE
  9. M. Islam, A. Dinh, K. Wahid, P. Bhowmik, “Detection of Potato Diseases Using Image Segmentation and Multiclass Support Vector Machine”, 30th Cana. Conf. on Elec. & Comp. Engg., 978-1-5090-5538-8/17/$31.00 ©2017 IEEE
  10. S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, D. Stefanovic, “Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification”, Hind. Publ. Corp. Comp. Intel. & Neuro. Vol. 2016, Art ID 3289801, 11 pages
  11. S. Ramesh, Mr. R. Hebbar, M. Niveditha, R. Pooja, N. P. Bhat, N. Shashank, Mr. P V Vinod, “Plant Disease Detection Using Machine Learning”, 2018 Inter. Conf. on Des. Inn. for 3Cs Comp. Comm. Cont., 978-1-5386-7523-6/18/$31.00 ©2018 IEEE
  12. K. P. Ferentinos, “Deep learning models for plant disease detection and diagnosis”, Comp. & Elec. in Agri., 145, 2018, 311-318
  13. V. Singh, A. K. Misra, “Detection of plant leaf diseases using image segmentation and soft computing techniques”, Info. Proce. in Agr. 4 (2017) 41–49
  14. K. Golhani, S. K. Balasundram, G. Vadamalai, B. Pradhan, “A review of neural networks in plant disease detection using hyper-spectral data”, Info. Proc. in Agri. 5 (2018) 354–371
  15. A. K. Rangarajan, R. Purushothaman, A. Ramesh, “Tomato crop disease classification using pre-trained deep learning algorithm”, Proc. Comp. Sci. 133 (2018) 1040–1047
  16. M. T. Kuska, A. K. Mahlein, “Aiming at decision making in plant disease protection and phenotyping by the use of optical sensors”, Eur J Plant Pathol, https://doi.org/10.1007/s10658-018-1464-1
  17. E. Fujita, Y. Kawasaki, H. Uga, S. Kagiwada, H. Iyatomi, “Basic investigation on a robust and practical plant diagnostic system”, 15th IEEE Inter. Conf. on Mach. Lear. & Appl., 978-1-5090-6167-9/16 $31.00 © 2016 IEEE
  18. F. Martinelli, R. Scalenghe, S. Davino, S. Panno, G. Scuderi, P. Ruisi, P. Villa, D. Stroppiana, M. Boschetti, L. R. Goulart, C. E. Davis, A. M. Dandekar, “Advanced methods of plant disease detection. A review”, Agron. Sustain. Dev. (2015) 35:1–25, Springer-Verlag France 2014
  19. J. P. Shah, H. B. Prajapati, V. K. Dabhi, “A Survey on Detection and Classification of Rice Plant Diseases”, IEEE inte. Conf. on 2016, 10, 1-8
  20. S. Iniyan, R. Jebakumar, P. Mangalraj, M. Mohit, A. Nanda, “Plant Disease Identification and Detection Using Support Vector Machines and Artificial Neural Networks”, © Sprin. Natu. Sing. Pte Ltd. 2020
  21. A. Sehgal, S. Mathur, “Plant Disease Classification Using Soft Computing Supervised Machine Learning”, IEEE Conf. Rec. # 45616; ISBN: 978-1-7281-0167-5
  22. A. Lowe, N. Harrison, A. P. French, “Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress”, Pla. Meth. (2017) 13:80
  23. X. Yang, T. Guo, “Machine learning in plant disease research”, Euro. Jour. of BioMe.l Res., 2017 | Vol. 3 | Iss. 1
  24. K. Nagasubramanian, S. Jones, A. K. Singh, S. Sarkar, A. Singh, B. Ganapathysubramanian, “Plant disease identification using explainable 3D deep learning on hyper spectral images”, Pla. Meth. (2019) 15:98
  25. A. M. Abdu, M. M. Mokji, U. U. Sheikh, “An Automatic Plant Disease Symptom Segmentation Concept Based on Pathological Analogy”, 10th Con. & Sys. Grad. Res. Coll., 2 - 3 August 2019, Shah Alam, Malaysia 978-1-7281-0754-7 IEEE
  26. G. Wang, Y. Sun, J. Wang, “Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning”, Hind. Comp. Intel. & Neur. Vol. 2017, Article ID 2917536, 8 pages
  27. P. Sharma, Y. P. S. Berwal, W. Ghai, “Performance analysis of deep learning CNN models for disease detection in plants using image segmentation”, Infor. Proc. in Agri.
  28. X. Q. Yue, Z. Y. Shang, J. Y. Yang, L. Huang, Y. Q. Wang, “A smart data-driven rapid method to recognize the strawberry maturity”, Info. Proce. in Agri.
  29. M. A. Hussein, A. H. Abbas, “Plant Leaf Disease Detection Using Support Vector Machine”, Al-Mustansiriyah Jour. of Sci., ISSN: 1814-635X, ISSN:2521-3520, Vol. 30, Iss. 1, 2019
  30. P. K. Sethy, B. Negi, N. K. Barpanda, S. K. Behera, A. K. Rath, “Measurement of Disease Severity of Rice Crop Using Machine Learning and Computational Intelligence”, Cog. Scie. & Arti. Inte., Spri. Bri. in Fore. & Medi. Bioi., https://doi.org/10.1007/978-981-10-6698-6_1
  31. P. Thakur, P. Aggarwal, M. Juneja, “Plant Disease Detection and Classification using Image Processing: A Review”, Inter. Jour. of Rec. Rese. Asp. ISSN: 2349-7688, Vol. 4, Issue 3, Sept 2017, pp. 22-27
  32. S. S. Kumar, B. K. Raghavendra, “Diseases Detection of Various Plant Leaf Using Image Processing Techniques: A Review”, 5th Inter. Confe. on Adv. Comp. & Comm. Sys. 978-1-5386-9533-3/19/$31.00 ©2019 IEEE
  33. C. K. Reddy, D. Hiranmayi, K. Eswaran, G. G. Likhita, B. Saritha, “Classification of Diseased Plants using Separation of Points by Planes”, Inter. Jou. of Engg Tech. Sci. & Rese., ISSN 2394 – 3386, Volume 4, Issue 9, Sept. 2017.
Index Terms

Computer Science
Information Sciences

Keywords

Review survey plant disease detection and recommendation machine learning algorithms feature selection.