SPATIAL MULTI-SCALE FRACTAL ANALYSIS OF TOMATO LEAF STRUCTURAL DEGRADATION UNDER DISEASE PROGRESSION
DOI:
https://doi.org/10.66302/api.vol.1.issue.01.01Keywords:
Tomato (Solanum lycopersicum), plant pathology, fractal dimension, lacunarity, image analysis, leaf morphology, disease progression, multi-scale spatial analysis, structural degradation, digital phenotyping.Abstract
Tomato (Solanum lycopersicum) is an important horticultural crop worldwide, and its productivity is significantly limited by the widespread occurrence of foliar diseases that gradually modify leaf morphology and physiological function. Disease progression in the leaves entails intricate spatial transformations like lesion development, vein proliferation and disruption in venation, tissue necrosis, and disruption of normal laminar architecture. Often, these structural alterations may be challenging to express in a conventional visual or categorical scoring system, which is often subjective and lacks granularity. In the present study, a spatial multi-scale fractal analysis framework has been proposed to quantitatively capture structural degeneration in tomato leaves during disease progression. Leaf images from sources with known disease severity levels were analyzed through fractal geometry-based metrics, focusing on two key descriptors: fractal dimension (FD) and lacunarity.to express structural complexity and heterogeneity. Fractal dimension serves as a description of overall morphological complexity in this context, whereas lacunarity is widely used to quantify spatial irregularity and gap distributions within leaf tissues. Multi-scale analysis was employed to examine structural changes at different spatial resolutions. This helped us better understand both global shape changes and localized tissue damage. The findings demonstrate a steady reduction in fractal dimension as disease severity escalates, indicating a gradual deterioration of structural complexity. On the other hand, lacunarity values increase as the disease gets worse, which means that the leaf architecture becomes more varied and broken up. The results show that fractal-based spatial analysis is a strong, non-destructive, and objective way to measure how disease damages the structure of plant leaves. This technique holds considerable promise for utilization in automated plant disease diagnostic systems, precision agriculture, and digital plant phenotyping platforms.
References
1. Achilov, B. (2025). Diagnosis of tomato leaf diseases
using fractal dimension and box-counting method based
on image analysis. International Journal of Scientific
Trends, 4(4), 41–49.
2. Agrios G.N. 2005. Plant pathology. 5th ed. Elsevier
Academic Press, Burlington, MA. 922 p.
3. Backes A.R., Casanova D. & Bruno O.M. 2009. Plant leaf
identification based on volumetric fractal dimension.
International Journal of Pattern Recognition and
Artificial Intelligence 23: 1145–1160.
4. Barbedo J.G.A. 2013. Digital image processing
techniques for detecting, quantifying and classifying
plant diseases. SpringerPlus 2: 660.
5. Batchuluun, G., Kim, S. G., Kim, J. S., Mahmood, T., &
Park, K. R. (2024). Artificial intelligence-based
segmentation and classification of plant images with
fractal dimension estimation. Fractal and Fractional,
8(11), 633.
6. Cross S.S. 1997. Fractals in pathology. Journal of
Pathology 182: 1–8.
7. Gonzalez R.C. & Woods R.E. 2008. Digital image
processing. 3rd ed. Pearson Education, Upper Saddle
River, NJ. 976 p.
8. Hughes D.P. & Salathé M. 2015. An open access
repository of images on plant health to enable the
development of mobile disease diagnostics. Plant
Methods 11: 8.
9. Mandelbrot B.B. 1982. The fractal geometry of nature.
W.H. Freeman and Company, New York. 468 p.
10. Mohanty S.P., Hughes D.P. & Salathé M. 2016. Using
deep learning for image-based plant disease detection.
Plant Methods 12: 1–10.
11. Montgomery D.C. 2017. Design and analysis of
experiments. 9th ed. John Wiley & Sons, New York. 752
p.
12. Narla, V. L., & Suresh, G. (2023). Multiple feature-based
tomato plant leaf disease classification using SVM
classifier. In Machine Learning, Image Processing,
Network Security, and Data Sciences. Springer.
13. Schindelin J., Arganda-Carreras I., Frise E. et al. 2012.
Fiji: An open-source platform for biological image
analysis. Nature Methods 9: 676–682.
14. Shafay, M., Hassan, T., Owais, M., Hussain, I., Khawaja,
S. G., & Seneviratne, L. (2025). Recent advances in plant
disease detection: Challenges and opportunities. Plant
Methods, 21, 140.
15. Sharma, A., Jain, A., Gupta, P., & Chowdary, V. (2020).
Machine learning applications for precision agriculture:
A comprehensive review. IEEE Access, 9, 4843–4873.
16. Tariq, M. H., Sultan, H., Akram, R., Kim, S. G., Kim, J.
S., Usman, M., Gondal, H. A. H., Seo, J., Lee, Y. H., &
Park, K. R. (2025). Estimation of fractal dimensions and
classification of plant disease with complex
backgrounds. Fractal and Fractional, 9(5), 315.
17. Tian, H., Wang, T., Liu, Y., Qiao, X., & Li, Y. (2020).
Computer vision technology in agricultural
automation—A review. Information Processing in
Agriculture, 7(1), 1–19.
18. Tukey J.W. 1949. Comparing individual means in the
analysis of variance. Biometrics 5: 99–114.
19. Zhao, J., Xu, L., Ma, Z., Li, J., Wang, X., Liu, Y., & Du,
X. (2025). A review of plant leaf disease identification
using deep learning algorithms. Frontiers in Plant
Science, 16, 1637241.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Agri Phytologica Insights
This work is licensed under a Creative Commons Attribution 4.0 International License.
