A Review on Leaf Parameter Analysis and Disease Identification using Machine Learning

Authors

  • Chandru Jathar  Research Scholar, Srinivas University College of Engineering and Technology, Mangalore, Karnataka, India
  • Suresh Kumar Rudrahithlu  Dean and Principal, Srinivas University, Bangalore Campus, Bangalore, Karnataka, India

Keywords:

Disease identification, plant, leaf, fruit, machine learning, deep learning.

Abstract

The leaf diseases need to be identified at the earliest as it indicates the disease well in advance which affects the yield of the fruit and crop. The black rot, black measles, leaf blight and mites are the well known disease causing agents for the leafs. Diseases on leaf and fruits will cause the economic losses and in agricultural based industries. In this paper the survey is carried on apple leaf and fruit diseases detection using the machine learning approaches. The algorithm designing should not be restricted to one plant hence specific algorithms are essential. The predict the disease for the fruit and plants the machine learning approaches are helpful. The extensive feature extraction such as leaf area, width, length of the leaf and so by considering these the machine learning approaches provides the greater accuracy. We have formulated the gaps and a proposed system, which will be helpful for new researchers in the field of leaf detection.

References

  1. Dutot, M.L.; Nelson, M.; Tyson, R.C. Predicting the spread of postharvest disease in stored fruit, with application to apples. Postharvest Biol. Technol. 2013, 85, 45–56.
  2. Zhao, P.; Liu, G.; Li, M.Z. Management information system for apple diseases and insect pests based on GIS. Trans. Chin. Soc. Agric. Eng. 2006, 22, 150–154.
  3. H. Sabrol, K. Satish, “Tomato Plant Disease Classification in Digital Images Using Classification Tree”, International Conference on Communication and Signal Processing, April 6-8, IEEE 2016 India.
  4. Wan Mohd Fadzil W.M.N, Shah Rizam M.S.B, R. Jailani, Nooritawati M.T, “Orchid Leaf Disease Detection using Border Segmentation Techniques”, 2014 IEEE Conference on Systems, Process and Control (ICSPC 2014), 12 - 14 December 2014, Kuala Lumpur, Malaysia.
  5. P.R. Rothe, Dr. R. V. Kshirsagar, “Automated Extraction of Digital Images Features of three kinds of Cotton Leaf Diseases” , 2014 International Conference on Electronics, Communication and Computational Engineering (ICECCE) IEEE 2014.
  6. Arya M S, Anjali K, and Mrs.Divya Unni, “Detection of unhealthy plant leaves using Image Processing and Genetic Algorithm with Arduino”, IEEE 2018.
  7. Pooja V, Rahul Das, and Kanchan V, “Identification of Plant Leaf Diseases using Image Processing Techniques”, IEEE 2017.
  8. R.MeenaPrakash,G.P.Saraswathy,Ramalakshmi, .H.Mangaleswari,           T.Kaviya, “Detection of Leaf Diseases and Classification using Digital ImageProcessing”2017InternationalConferenceon Innovations in Information Embedded and Communication Systems (ICIIECS) IEEE 2017.
  9. Tian, You-wen, and Xiao-juan Wang. "Analysis of leaf parameters measurement of cucumber based on image processing." 2009 WRI World Congress on Software Engineering. Vol. 3. IEEE, 2009.
  10. Kaiyan, Lin, et al. "Measurement of plant leaf area based on computer vision." 2014 Sixth International Conference on Measuring Technology and Mechatronics Automation. IEEE, 2014.
  11. N. R. Bhimte and V. R. Thool, Diseases detection of cotton leaf spot using image processing and SVM classifier," 2018 Second Int. Conf. Intell. Comput. Control Syst., no. Iciccs (2019), pp. 340– 344.
  12. S. Kai, L. Zhikun, S. Hang and G. Chunhong, \A research of maize disease image recognition of corn based on BP networks," Measuring Technology and Mechatronics Automation (ICMTMA), Third Int. Conf., 6 January 2011, Vol. 1 (IEEE), pp. 246–249.
  13. S. Kai, Z. Liu, H. Su and C. Guo, \A research of maize disease image recognition of corn based on BP networks," Proc. 3rd Int. Conf. Meas. Technol. Mechatronics Autom. ICMTMA 2011, Vol. 1, no. 2009921090 (2011), pp. 246–249.
  14. Jiang, Peng, et al. "Real- time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks." IEEE Access 7 (2019): 59069-59080.
  15. Chutinan Trongtorkid, Part Pramokchon, “Expert System for Diagnosis Mango Diseases Using Leaf Symptoms Analysis”, The 3rd International Conference on Digital Arts, Media and Technology (ICDAMT2018).
  16. Zhang, Chunlong, Kunlin Zou, and Yue Pan. "A method of apple image segmentation based on color-texture fusion feature and machine learning." Agronomy 10.7 (2020): 972.
  17. Chuanlei, Zhang, et al. "Apple leaf disease identification using genetic algorithm and correlation based feature selection method." International Journal of Agricultural and Biological Engineering 10.2 (2017): 74-83.
  18. Tahir, Muqadas Bin, et al. "Recognition of apple leaf diseases using deep learning and variances- controlled features reduction." Microprocessors and Microsystems (2021): 104027.
  19. Harshal Waghmare, Radha Kokare and Yogesh Dandawate, “Detection and Classification of Diseases of Grape Plant Using Opposite Colour Local Binary Pattern Feature and Machine Learning for Automated Decision Support System,” 3rd International Conference on Signal Processing and Integrated Networks (SPIN), 2016.
  20. Dheeb Al Bashish, Malik Braik, and Sulieman Bani-Ahmad, “A Framework for Detection and Classification of Plant Leaf and Stem Diseases,” International Conference on Signal and Image Processing, 2010.
  21. Pooja Pawar, Varsha Turkar, and Pravin Patil, “Cucumber disease detection using artificial neural network,” IEEE International Conference on Inventive        Computation Technologies (ICICT), vol. 3, pp. 1- 5, 2016.
  22. H. Al-Hiary, S. Bani- Ahmad, M. Reyalat, M. Braik, and Z. ALRahamneh, “Fast and accurate detection and classification of plant diseases,” Machine learning, vol. 14, no. 5, 2011.
  23. Kaur, Iqbaldeep, Gifty Aggarwal, and Amit Verma, “Detection and Classification of Disease Affected Region of Plan Leaves using Image Processing Technique,” Indian Journal of Science and Technology, vol. 9, no. 48, 2016.
  24. Bin Liu, Yun Zhang, Dong Jian He and Yuxiang Li, “Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks,” Symmetry, vol. 10, no. 11, 2017.
  25. A. Joshi and B. D. Jadhav, “Monitoring and Controlling Rice Diseases Using Image Processing Techniques,” 2016 International Conference on Computing, Analytics and Security Trends (CAST) College of Engineering Pune, Dec 2016.
  26. D. Luna, G. Robert G. Renann, Baldovino, E. A. Cotoco, Anton Louise P. de Ocampo, C. Ira,Valenzuela, A. B. Culaba, and E.P. Dadio  Gokongwei, “Identification of philippine herbal medicine plant leaf using artificial neural network,” 9th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), pp. 1-8, 2017.
  27. Es-Saady, Y.; Massi, I.E.; Yassa, M.E.; Mammass, D.; Benazoun, A. Automatic recognition of plant leaves diseases based on serial combination of two SVM classifiers. In Proceedings of the 2nd International Conference on Electrical and Information Technologies, Tangiers, Morocco, 4–7 May 2016; pp. 561–566.
  28. Padol, P.B.; Yadav, A.A. SVM classifier based grape leaf disease detection. In Proceedings of the 2016 Advances in Signal Processing, Pune, India, 9–11 June 2016; pp. 175–179.
  29. Sannakki, S.S.; Rajpurohit, V.S.; Nargund, V.B.; Kumar, A.; Yallur, P.S. Diagnosis and classification of grape leaf diseases using neural networks. In Proceedings of the 4th International Conference on Computing, Tiruchengode, India, 4–6 July 2013; pp. 1–5.
  30. Qin, F.; Liu, D.X.; Sun, B.D.; Ruan, L.; Ma, Z.; Wang, H. Identification of alfalfa leaf diseases using image recognition technology. PLoS ONE 2016, 11, e0168274. [CrossRef] [PubMed].
  31. Rothe, P.R.; Kshirsagar, R.V. Cotton leaf disease identification using pattern recognition techniques. In Proceedings of the 2015 International Conference on Pervasive Computing, Pune, India, 8–10 January 2015; pp. 1–6.
  32. Islam, M.; Dinh, A.; Wahid, K.; Bhowmik, P. Detection of potato diseases using image segmentation and multiclass support vector machine. In Proceedings of the 30th IEEE Canadian Conference on Electrical and Computer Engineering, Windsor, ON, Canada, 30 April–3 May 2017; pp. 1–4. Symmetry 2018, 10, 11 16 of 16.
  33. Gupta, T. Plant leaf disease analysis using image processing technique with modified SVM-CS classifier. Int. J. Eng. Manag. Technol. 2017, 5, 11–17.
  34. Dhakate, M.; Ingole, A.B. Diagnosis of pomegranate plant diseases using neural network. In Proceedings of the 5th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, Patna, India, 16–19 December 2015; pp. 1–4.
  35. Gavhale, M.K.R.; Gawande, U. An overview of the research on plant leaves disease detection using image processing techniques. J. Comput. Eng. 2014, 16, 10–16.
  36. Wang, G.; Sun, Y.; Wang, J.X. Automatic image-based plant disease severity estimation using deep learning. Comput. Intell. Neurosci. 2017, 2017, 1–8.
  37. Thapa, Ranjita, et al. "The plant pathology 2020 challenge dataset to   classify   foliar   disease   of apples." arXiv preprint arXiv:2004.11958 (2020).
  38. https://www.cabi.org/isc/datasheet/31964#toPictures
  39. https://www.nps.gov/shen/learn/nature/apple_tree.htm

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2022-04-30

Issue

Volume 8, Issue 2, March-April-2022

Section

Research Articles

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How to Cite

[1]
Chandru Jathar, Suresh Kumar Rudrahithlu, " A Review on Leaf Parameter Analysis and Disease Identification using Machine Learning" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 2, pp.220-228, March-April-2022.