CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Review on Emerging Trends in Detection of Plant Diseases using Image Processing with Machine Learning

by Punitha Kartikeyan, Gyanesh Shrivastava
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 174 - Number 11
Year of Publication: 2021
Authors: Punitha Kartikeyan, Gyanesh Shrivastava
10.5120/ijca2021920990

Punitha Kartikeyan, Gyanesh Shrivastava . Review on Emerging Trends in Detection of Plant Diseases using Image Processing with Machine Learning. International Journal of Computer Applications. 174, 11 ( Jan 2021), 39-48. DOI=10.5120/ijca2021920990

@article{ 10.5120/ijca2021920990,
author = { Punitha Kartikeyan, Gyanesh Shrivastava },
title = { Review on Emerging Trends in Detection of Plant Diseases using Image Processing with Machine Learning },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2021 },
volume = { 174 },
number = { 11 },
month = { Jan },
year = { 2021 },
issn = { 0975-8887 },
pages = { 39-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume174/number11/31724-2021920990/ },
doi = { 10.5120/ijca2021920990 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:21:50.825675+05:30
%A Punitha Kartikeyan
%A Gyanesh Shrivastava
%T Review on Emerging Trends in Detection of Plant Diseases using Image Processing with Machine Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 174
%N 11
%P 39-48
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In India, about 70% of the population is involved in agriculture and farming. Today plant diseases are significant concern as it reduces the production and quality of agriculture produce. Most plant diseases are caused by bacteria, fungi and virus. Manual detection and identification of leaf disease involve more man power and expensive in large farm. Detection of disease and healthy monitoring of plant are major challenges for sustainable agriculture. Hence there is need to detect plant disease automatically using image processing technique at an early stage with more accuracy. It involves image acquisition, image pre-processing, image segmentation, feature extraction and classification of disease. To increase the quality of produce and yield, a regular monitoring technique for detection of diseases in plants becomes essential. The digital image processing system is one such powerful technique to diagnose the difficult symptoms much earlier than the human eye could recognize. It enables the farmers to take appropriate actions timely in order to safeguard the crop and get the desired quality and yield of agriculture produce. Different techniques used for the classification of plant disease using various classifiers such as Support Vector Machine, Artificial Neural Network , K-Nearest Neighbors and other classifier methods have been discussed. The purpose of this paper is to give an overview of established methods for plant disease detection, classification systems.

References
  1. V. Sharma, A. Verma and N. Goel, “Classification Techniques for Plant Disease Detection”, International Journal of Recent Technology and Engineering (IJRTE) ,2020,ISSN: 2277-3878, vol-8 , Issue-6
  2. T. Youwen , L . Tianlai and N. Yan , “The recognition of cucumber disease based on image processing and support vector machine”, In proceeding of 2008 Congress on image and signal processing, IEEE, Sanya, 262-267, DOI: 10.1109/CISP.2008.29.
  3. A. Meunkaewjinda, P. Kumsawat and K. Attakitmongcol , Sri kaew, “Grape leaf disease detection from color imagery using hybrid intelligent system”, proceedings of ECTI-CON 2008, IEEE, Krabi, pp 513–516, DOI:10.1109/ecticon.2008.4600483
  4. Y. Tian, C. Zhao, S. Lu and X. Guo, “SVM- based Multiple Classifier System for Recognition of Wheat Leaf Diseases”, Proceedings of 2010 Conference on Dependable Computing (CDC’2010) , 2010, Yichang, China.
  5. J. Zhang and W. Zhang , “Support Vector Machine for Recognition of Cucumber Leaf Diseases”, In Proceeding of 2nd International Conference on Advanced Computer Control (ICACC), IEEE, Shenyang, 2010, 590-594. DOI: 10.1109/ICACC.2010.5487242.
  6. S. Prasad, P. Kumar, R Hazra and A. Kumar, “Plant Leaf Disease Detection Using Gabor Wavelet Transform”, Proceedings of 3rd International Conference on Swarm, Evolutionary and Memetic Computing, Springer-Verlag, Berlin, Heidelberg, 2012.
  7. M. M. Pawar, S. Bhusari and A. Gungewar , “Identification of infected pomegranates using color texture feature analysis”, International journal of computer application, (0975-8887), 2012.
  8. S. Arivazhagan, S. Newlin, 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, 2013, vol.15, no.1,pp.211-217.
  9. R. Kiran Gavhale, U. Gawande , Kamal and O. Hajari , “Unhealthy Region of Citrus Leaf Detection Using Image Processing Techniques” , IEEE International Conference for Convergence of Technology, 2014, pp. 978-1-4799-3759-2/14.
  10. V. Singh and A.K. Mishra, “Detection of plant leaf diseases using image segmentation and soft computing techniques”, Information Processing in Agriculture 4 (1) ,  2016
  11. S. Jayamoorthy and N. Palanivel , “Identification of Leaf Disease Using Fuzzy C-MEAN and Kernal Fuzzy C-MEAN and Suggesting the Pesticides”, International Journal of Advanced Research in Science, Engineering and Technology, May 2017, Vol. 4, Issue 5.
  12. Akhtar, Asma, A. Khanum, Shoab A. Khan and A. Shaukat, "Automated Plant Disease Analysis (APDA): Performance Comparison of Machine Learning Techniques" , IEEE International Conference on Frontiers of Information Technology (FIT), pp. 60-65, 2013.
  13. S. R. Dubey and A.S Jalal, “Detection and Classification of Apple Fruit Diseases Using Complete Local Binary Patterns” , IEEE Third International Conference on Computer and Communication Technology, pp. 978-0-7695-4872, 2012.
  14. U. Mokhtar, A.S. Mona, Alit, A. E. Hassenian and H. Hefny, “Tomato leaves diseases detection approach based on support vector machines”, IEEE pp. 978-1-5090-0275-7/15, 2015.
  15. Q. Yao , Z. Guan, Y. Zhou, J. Tang; Y. Hu and B. Yang, “Application of support vector machine for detecting rice diseases using shape and color texture features”, International Conference on Engineering Computation, 2009, pp.79-83.
  16. E. Omrani, B. Khoshnevisan, S. Shamshirb and, H. Saboohi, N. B. Anuar and M. H. N. Nasir “Potential of radial basis function-based support vector regression for apple disease detection”, Measurement, vol.55, pp.512–519, Sept-2014
  17. S. Phadikar and J. Sil, “Rice Disease Identification using Pattern Recognition Techniques”, Proceedings of 11th International Conference on Computer and Information Technology (ICCIT 2008), 25-27 December, 2008, IEEE, Khulna, Bangladesh, pp. 420-423.
  18. D. Al Bashish , M. Braik and S. Bani-Ahmad , “A framework for detection and classification of plant leaf and stem diseases”, In proceeding of 2010 International conference on signal and image processing. IEEE, Chennai, 113-118.
  19. H. Al-Hiary, S. Bani-Ahmad, M. Reyalat, M. Braik and Z. AlRahamneh, “Fast and accurate detection and classification of plant diseases”, International journal of computer applications (0975-8887), 2011, vol.17- no.1, pp-31-38.
  20. K. Song , Z. Liu , H. Su and C. Guo, “A Research of maize disease image recognition of Corn Based on BP Networks”, Third international conference on Measuring Technology and Mechatronics Automation,IEEE, 2011:246-249, DOI:10.1109/ICMTMA.2011.66
  21. P. Revathi and M. Hemlatha, “Homogeneous Segmentation Based Edge Detection Techniques for Proficient Identification of the Cotton Leaf Spot Diseases”, International Journal of Computer Applications, 2012, vol 47,no 2,pp 0975-888.
  22. A. Asfarian , Y. Herdiyeni , A. Rauf and KH. Mutaqin , “Paddy Diseases Identification with Texture Analysis using Fractal Descriptors Based on Fourier Spectrum”, In proceeding of International Conference on Computer Control Informatics and Its Applications, IEEE, Jakarta, 77-81, 2013,
  23. T. Bindu and V.Toran, “Identification and classification of normal and infected apples using neural network”, International Journal of Science and Research, 2013,vol.2, no.6, pp.160-163.
  24. F. Hong and L. Huijie, “Plant leaves recognition and classification model based on image features and neural network”, IJCSI International Journal of Computer Science, 2014, vol.11, no.1, pp.100-104.
  25. C.S. Sumathi and A.V. Senthil Kumar, “Enhancing accuracy of plant leaf classification techniques”, International Journal of Engineering Research and Applications, 2014, vol.4, no.3, pp.40-46.
  26. R Kiran Gavhale and U. Gawande, “An Overview of the Research on Plant Leaves Disease detection using Image Processing Techniques”, IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727, vol 16, Issue 1, 2014, pp 10-16.
  27. P.R. Rothe and R. V. Kshirsagar, “Cotton Leaf Disease Identification using Pattern Recognition Techniques”, International Conference on Pervasive Computing (ICPC), IEEE, 2015.
  28. A. Rastogi , R. Arora and S. Sharma , “Leaf Disease Detection and Grading using Computer Vision Technology and Fuzzy Logic” , IEEE 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN), Noida, 2015, pp 500-505, 2015.
  29. M. Yadav and T. Verma , “ Hybrid Approach of Neural Network and Genetic Algorithm to Recognize Black Mold Disease in Tomato”, International Journal for Research in Applied Science & Engineering Technology (IJRASET). 2016,Vol 4, Issue 5.
  30. S. D. Khirade and A.B. Patil, “Plant Disease Detection Using Image Processing”, In International Conference on Computing Communication Control and Automation (ICCUBEA), 2015 (pp. 768- 771). IEEE.
  31. H. Wang, G. Li, Z. Ma, and X. Li, "Image recognition of plant diseases based on backpropagation networks" , In 5th International Congress on Image and Signal Processing (CISP), 2012 pp. 894-900, IEEE.
  32. Bhong , B. V. Pawar and S. Vijay, "Study and Analysis of Cotton Leaf Disease Detection Using Image Processing." International Journal of Advanced Research in Science, Engineering and Technology, 2016, vol 3, Issue 2
  33. S.B Kutty, N.E Abdullah, H. Hashim, A. Afhzan Ab Rahim, A. S. Kusim , T.N. Tuan Yaakub, Puteri N. A. M. Yunus and M. F. A. Rahman, “Classification of Watermelon Leaf Diseases Using Neural Network Analysis”, IEEE, Business Engineering and Industrial Applications Colloquium (BEIAC), Langkawi, pp 459 – 464, 2013.
  34. S. Sanjeev Sannaki, S. Vijay , Rajpurohit, V and B. N. P. Kulkarni “Diagnosis and Classification of Grape Leaf Diseases using Neural Network”,IEEE,Tiruchengode,pp1 – 5, 2013.
  35. H. Wang, G. Li, Z. Ma and X. Li, “ImageRecognition of Plant Diseases Based on Principal Component Analysis and Neural Networks”, 8th International Conference on Natural Computation ,2012, pp.246-251
  36. G. Xu , F. Zhang , SG. Shah , Y. Ye and H. Mao, “Use of leaf color images to identify nitrogen and potassium deficient tomatoes”, 2011, Pattern Recognition Lett vol.32,1584-1590.
  37. M. Suresha, K. N. Shreekanth and B. V. Thirumalesh, "Recognition of diseases in paddy leaves using knn classifier," 2nd International Conference for Convergence in Technology (I2CT), Mumbai, 2017, pp. 663-666. DOI: 10.1109/I2CT.2017.8226213.
  38. E. Hossain, M. F. Hossain and M. A. Rahaman, "A Color and Texture Based Approach for the Detection and Classification of Plant Leaf Disease Using KNN Classifier," International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox'sBazar, Bangladesh, 2019, pp. 1-6.
  39. J. Abdulridha, R. Ehsani, A. Abd-Elrahman and Y. Ampatzidis, “A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses”, Computers and electronics in agriculture, 2019,156,549-557
  40. Y.C Zhang, H.P Mao, B. Hu and M.X Li, “ Features selection of cotton disease leaves image based on fuzzy feature selection techniques”, IEEE Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition, China, 2-4 Nov 2007.
  41. J. Pang, Bai Zy, Lai Jc and Li Sk, “Automatic segmentation of crop leaf spot disease images by integrating local threshold and seeded region growing” , International conference on image analysis and signal processing, IEEE, Hubei, 2010, pp 590–594.
  42. Z. Zhuo ,Y. Zang , Y. Li , Y. Zhang , P. Wang and X. Luo , “Rice plant-hopper infestation detection and classification algorithms based on fractal dimension values and fuzzy C-means”, Mathematical and Computer Modelling 58(2011), 701–709.
  43. P.K. Nitin and B.D Sanjay,. “Crop Disease Detection Using CBIR”, International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS), 2013.
  44. H. Qinghai, M. Benxue, Q. Duanyang, Z. Qiang, H. Xinmin and Z. Jing, “Cotton pests and diseases detection based on image processing”, Telkomnika Indonesian Journal of Electrical Engineering, 2013, vol.11, no.6, pp.3445-3450.
  45. D. Zhihua and W. Huan, “Image segmentation method for cotton mite disease based on color features and area thresholding”, Journal of Theoretical and Applied Information Technology, 2013,vol. 48, no.1, pp.527-533.
  46. T. Deshpande, S. Sengupta and K.S. Raghuvanshi, “Grading & Identification of Disease in Pomegranate Leaf and Fruit ” , International Journal of Computer Science and Information Technologies, IJCSIT, 2014,vol. 5 (3), pp 4638-4645.
  47. L. Wang , F. Dong , Q. Guo , C. Nie and S. Sun ,“Improved Rotational Kernel Transformation Directional Feature for Recognition of Wheat Stripe Rust and Powdery Mildew”, In proceeding of Seventh International Conference on Image and Signal Processing, IEEE, Dalian, 286-291, DOI: 10.1109/CISP.2014.7003793
  48. P. Narvekar and S. N. Patil, “Novel Algorithm for Grape Leaf Diseases Detection,” International Journal of Engineering Research and General Science, 2015, vol.3, pp 1240-1244.
  49. J. Bhavini Samajpati and D. Sheshang Degadwala, “Hybrid Approach for Apple Fruit Diseases Detection and Classification Using Random Forest Classifier”, IEEE International Conference on Communication and Signal Processing,2016, pp. 978-5090-0396, DOI:10.1109/ICCSP.2016.7754302.
  50. J. Lu, J. Hu, G. Zhao, F. Mei and C. Zhang, “An in-field automatic wheat disease diagnosis system”, Computers and Electronics in Agriculture 142 ,2017, 369–379.
  51. M. Kumar, T. Hazra and S. S. Tripathy , “Wheat Leaf Disease Detection Using Image Processing”, International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), Apr 2017,vol 6, Issue 4.
Index Terms

Computer Science
Information Sciences

Keywords

Image Processing Disease Detection Segmentation Feature Extraction Classification

Powered by PhDFocusTM