TITLE:
An Automated Solution for Detecting Polyalthia longifolia Diseases from Images of Its Infected Leaves
AUTHORS:
Guié Toa Bi, Sandjé Marcelin, Sié Ouattara, Alico Jérome, Alain Clement
KEYWORDS:
Foliar Disease, Polyalthia longifolia, Texture Parameters, Color, Overall Score
JOURNAL NAME:
American Journal of Plant Sciences,
Vol.17 No.10,
October
22,
2026
ABSTRACT: Early detection of foliar diseases is essential for the health of Polyalthia longifolia. This article proposes an automated image analysis-based method for classifying leaves as healthy or infected. Texture parameters (Entropy, Contrast, Correlation, Energy, Homogeneity) and color parameters (R, G, B, ExG, ExR, VARI) are extracted for each leaf and then normalized to a healthy reference leaf. These parameters are combined into an overall score, calculated by Euclidean distance, which reflects the divergence from the healthy state. A unique decision curve with a threshold allows for the visual and quantitative differentiation of infected leaves from healthy leaves. The results show that healthy leaves have a score below the threshold, while infected leaves exceed it, with varying levels of severity. The percentages exceeding the threshold and the reliability estimates (80% to 98%) confirm the robustness of the approach. This solution provides a fast, accurate, and non-destructive tool for the automated diagnosis of diseases and paves the way for applications in large-scale monitoring and precision agriculture, enabling early and targeted intervention for the protection of Polyalthia longifolia.