A Comprehensive Review of Clustering Techniques in Leaf Image Processing for Plant Analysis

Applications including disease diagnosis, species identification, and phenotypic trait evaluation are made possible by leaf image processing, which is crucial to automated plant analysis. Clustering algorithms are one of the most popular image analysis approaches for grouping visually related regions in leaf images without the need for annotated data. This makes them appropriate for agricultural settings where manual annotation is difficult. The clustering methods used in leaf image processing for plant analysis are thoroughly examined in this article. Partitional, hierarchical, density-based, fuzzy, and hybrid clustering techniques are comprehensively categorized in the paper, and their efficacy in tasks like leaf segmentation, lesion localization, and feature grouping is discussed. To provide a cohesive analytical framework, popular preprocessing procedures, feature extraction techniques, and clustering evaluation metrics are also examined. Additionally emphasized are recent developments that combine clustering with machine learning and deep learning models, highlighting their capacity to tackle issues with illumination variance, backdrop complexity, and leaf morphological diversity. Lastly, this review highlights important research issues and suggests future areas of inquiry to strengthen the reliability and effectiveness of clustering-based leaf image analysis systems.

Fuzzy and Hybrid Clustering, lesion localization

1. J. Zhang, Y. Huang, and L. Wang, “A survey on image segmentation techniques for plant leaf disease detection,” Computers and Electronics in Agriculture, vol. 178, pp. 105–114, 2020. [Google Scholar] [Crossref]

2. A. Mohanty, D. Hughes, and M. Salathé, “Using deep learning for image-based plant disease detection,” Frontiers in Plant Science, vol. 7, art. no. 1419, 2016. [Google Scholar] [Crossref]

3. S. Sladojevic et al., “Deep neural networks based recognition of plant diseases by leaf image classification,” Computational Intelligence and Neuroscience, pp. 1–11, 2016. [Google Scholar] [Crossref]

4. P. Revathi and M. Hemalatha, “Classification of cotton leaf spot diseases using image processing edge detection techniques,” in Proc. IEEE Int. Conf. Emerging Trends in Science, 2019, pp. 169–173. [Google Scholar] [Crossref]

5. R. Singh and A. Kumar, “K-means and fuzzy C-means for clustering-based segmentation of plant leaf diseases,” Journal of Intelligent Systems, vol. 29, no. 1, pp. 118–129, 2020. [Google Scholar] [Crossref]

6. H. Al-Hiary et al., “Fast and accurate detection and classification of plant diseases,” International Journal of Computer Applications, vol. 17, no. 1, pp. 31–38, 2019. [Google Scholar] [Crossref]

7. M. Brahimi, K. Boukhalfa, and A. Moussaoui, “Deep learning for tomato diseases: Classification and symptoms visualization,” Applied Artificial Intelligence, vol. 31, no. 4, pp. 299–315, 2019. [Google Scholar] [Crossref]

8. Y. Lu, S. Yi, N. Zeng, Y. Liu, and Y. Zhang, “Deep convolutional neural networks for rice disease identification,” Neurocomputing, vol. 267, pp. 378–384, 2019. [Google Scholar] [Crossref]

9. T. Kaur and J. Kaur, “Performance analysis of clustering techniques for plant leaf image segmentation,” Multimedia Tools and Applications, vol. 79, pp. 30845–30863, 2020. [Google Scholar] [Crossref]

10. A. Kumar and P. Sharma, “Image processing and clustering techniques for plant leaf disease detection,” Procedia Computer Science, vol. 167, pp. 2112–2120, 2020. [Google Scholar] [Crossref]

11. S. Chouhan, A. Kaul, U. Singh, and S. Jain, “A deep learning based approach for plant disease detection,” International Journal of Advanced Computer Science and Applications, vol. 10, no. 4, pp. 229–235, 2019. [Google Scholar] [Crossref]

12. R. Pydipati, T. Burks, and W. Lee, “Identification of citrus disease using color texture features and discriminant analysis,” Computers and Electronics in Agriculture, vol. 52, pp. 49–59, 2018. [Google Scholar] [Crossref]

13. K. Thenmozhi and U. S. Reddy, “Crop pest classification based on deep convolutional neural network and transfer learning,” Computers and Electronics in Agriculture, vol. 164, art. no. 104906, 2019. [Google Scholar] [Crossref]

14. J. Wang, C. Zhang, and J. Song, “Leaf disease detection using clustering and texture features,” IEEE Access, vol. 8, pp. 189032–189043, 2020. [Google Scholar] [Crossref]

15. S. Barbedo, “Effect of dataset size and variety on the efficacy of transfer learning and deep learning for plant disease classification,” Computers and Electronics in Agriculture, vol. 153, pp. 46–53, 2018. [Google Scholar] [Crossref]

16. R. Singh and A. Kumar, “Unsupervised clustering approaches for leaf disease segmentation,” Journal of Intelligent Systems, vol. 30, no. 1, pp. 987–1002, 2021. [Google Scholar] [Crossref]

17. S. Kaur and R. K. Aggarwal, “Hierarchical clustering for plant leaf vein structure analysis,” Multimedia Tools and Applications, vol. 79, pp. 16521–16538, 2020. [Google Scholar] [Crossref]

18. A. K. Mishra and S. K. Rai, “Computational challenges in hierarchical image clustering for agriculture,” Information Processing in Agriculture, vol. 8, no. 2, pp. 256–268, 2021. [Google Scholar] [Crossref]

19. M. A. Hossain and M. A. H. Akhand, “Density-based clustering for plant leaf disease region detection,” IEEE Access, vol. 8, pp. 175541–175552, 2020. [Google Scholar] [Crossref]

20. L. Zhang and X. Wu, “OPTICS-based segmentation of complex leaf images under natural conditions,” Pattern Recognition Letters, vol. 145, pp. 36–44, 2021. [Google Scholar] [Crossref]

21. J. Bezdek, “Fuzzy clustering in pattern recognition,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 21, no. 5, pp. 113–125, 2019. [Google Scholar] [Crossref]

22. P. Gupta and S. Mehta, “Spatial fuzzy C-means clustering for plant disease detection,” Applied Soft Computing, vol. 93, art. no. 106385, 2020. [Google Scholar] [Crossref]

23. A. Verma and R. Gupta, “Hybrid clustering using genetic algorithms for leaf image segmentation,” Expert Systems with Applications, vol. 168, art. no. 114255, 2021. [Google Scholar] [Crossref]

24. T. Rumpf et al., “Hybrid image analysis techniques for plant disease detection,” Computers and Electronics in Agriculture, vol. 174, art. no. 105457, 2020. [Google Scholar] [Crossref]

25. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436–444, 2019. [Google Scholar] [Crossref]

26. S. Min, B. Lee, and S. Yoon, “Deep feature clustering for plant image analysis,” IEEE Access, vol. 9, pp. 72830–72842, 2021. [Google Scholar] [Crossref]

27. A. Saxena and S. Verma, “Comparative analysis of clustering techniques for agricultural image datasets,” Journal of King Saud University – Computer and Information Sciences, vol. 34, no. 6, pp. 3479–3491, 2022. [Google Scholar] [Crossref]

28. D. Sharma, R. Kumar, and A. Singh, “Performance evaluation of unsupervised learning techniques for leaf image analysis,” Neural Computing and Applications, vol. 34, pp. 10951–10965, 2022. [Google Scholar] [Crossref]

29. J. Rousseeuw, “Silhouette-based clustering validation,” Journal of Computational and Applied Mathematics, vol. 20, pp. 53–65, 2019. [Google Scholar] [Crossref]

30. S. Barbedo, “Plant disease datasets: Challenges and future directions,” Computers and Electronics in Agriculture, vol. 153, pp. 46–53, 2018. [Google Scholar] [Crossref]

31. K. Kaur and V. Arora, “Evaluation metrics for image clustering in plant phenotyping,” Information Processing in Agriculture, vol. 9, no. 1, pp. 14–26, 2022. [Google Scholar] [Crossref]

32. A. Kumar and N. Sharma, “Comparative study of clustering-based leaf segmentation methods,” Procedia Computer Science, vol. 171, pp. 1294–1303, 2020. [Google Scholar] [Crossref]

33. H. Nguyen and S. Lee, “Noise-resistant clustering for leaf disease region detection,” Pattern Analysis and Applications, vol. 24, pp. 1385–1397, 2021. [Google Scholar] [Crossref]

34. M. Jain and S. K. Singh, “Optimization-assisted fuzzy clustering for plant leaf analysis,” Soft Computing, vol. 25, pp. 11271–11284, 2021. [Google Scholar] [Crossref]

35. A. Paul and S. Dutta, “Hybrid clustering frameworks for agricultural image analysis,” Journal of Ambient Intelligence and Humanized Computing, vol. 13, pp. 5111–5123, 2022. [Google Scholar] [Crossref]

36. H. Chen, Y. Li, and J. Zhang, “Deep feature representation for unsupervised plant image clustering,” Neurocomputing, vol. 458, pp. 198–209, 2021. [Google Scholar] [Crossref]

37. S. Roy and A. Banerjee, “Computational efficiency of deep clustering models for agriculture,” Future Generation Computer Systems, vol. 128, pp. 345–357, 2022. [Google Scholar] [Crossref]

38. Mohan and Prasad, “Comparative performance analysis of clustering techniques for plant disease detection,” Applied Artificial Intelligence, vol. 36, no. 1, pp. 201–219, 2022. [Google Scholar] [Crossref]

39. Singh and Chaudhary, “Difficulties in deploying clustering-based agricultural systems in real time,” Journal of Cleaner Production, vol. 312, art. no. 127678, 2021. [Google Scholar] [Crossref]

40. A. K. Sinha and D. Gupta, “Explainable clustering models for precision agriculture,” Artificial Intelligence in Agriculture, vol. 6, pp. 65–76, 2022. [Google Scholar] [Crossref]

41. S. Kamilaris and F. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Computers and Electronics in Agriculture, vol. 147, pp. 70–90, 2018. [Google Scholar] [Crossref]

42. N. Saleem et al., “Machine learning and clustering approaches for crop disease detection,” IEEE Access, vol. 9, pp. 32135–32149, 2021. [Google Scholar] [Crossref]

43. R. Mishra and S. Patel, “Scalability issues in agricultural image processing systems,” Information Sciences, vol. 585, pp. 214–229, 2022. [Google Scholar] [Crossref]

44. A. T. Jahnke et al., “Benchmark datasets for plant phenotyping,” Plant Methods, vol. 16, no. 1, pp. 1–15, 2020. [Google Scholar] [Crossref]

45. F. Doshi-Velez and B. Kim, “Towards a rigorous science of interpretable machine learning,” arXiv preprint arXiv:1702.08608, 2019. [Google Scholar] [Crossref]

46. P. K. Sethy and S. K. Behera, “Future directions of AI-based plant disease detection systems,” Artificial Intelligence in Agriculture, vol. 7, pp. 100–112, 2023. [Google Scholar] [Crossref]

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