Hybrid optimization of genetic algorithm and interior point methods for anfis-based detection of Xanthomonas campestris
Introduction: This article presents the results of the research project entitled “Integration of Foliar Segmentation and Multivariable Analysis for the Detection of Diseases in Short-Cycle Crops Using Images,” conducted at the Universidad Distrital Francisco José de Caldas in 2025.
Problem: Early symptoms of Xanthomonas campestris are subtle and difficult to distinguish from natural pigmentation or environmental stress using traditional visual inspection, often leading to delayed diagnosis and suboptimal disease management.
Objective: This research aims to develop an interpretable early detection system based on RGB leaf images by integrating a two-level Sugeno fuzzy inference system, an Adaptive Neuro-Fuzzy Inference System (ANFIS), and a hybrid optimization strategy to improve diagnostic accuracy and robustness.
Methodology: Cropped and standardized RGB images were transformed into the HSB color space to emphasize hue and saturation variations related to chlorosis and necrosis. At the pixel level, channel-wise membership functions and Sugeno rules generated local evidence. At the leaf level, aggregated evidence was mapped into five fuzzy memberships for binary classification. Parameter tuning combined a genetic algorithm for global exploration with an interior point method for constrained refinement. ANFIS employed hybrid learning with backpropagation and least squares estimation.
Results: Experiments on 1,685 images from 15 plant species achieved 94.23% accuracy, 92.96% precision, 95.84% sensitivity, and 92.94% specificity, with statistical consistency across 50 repetitions.
Conclusion: The proposed framework balances performance and interpretability, supporting precision agriculture.
Originality: It integrates two-level Sugeno inference, ANFIS hybrid learning, and GA–IPM optimization into a unified and interpretable model.
Limitations: Performance may be affected by background interference and very early-stage lesions.
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