Interpretable Color–Texture–Shape Feature Fusion for RGB-Based Citrus Canker and Melanose Classification on Orange Fruit

Abstract

Abstract Citrus canker and melanose substantially reduce the visual quality and commercial value of orange fruit, yet routine diagnosis remains largely dependent on subjective visual inspection. This study presents an interpretable color–texture–shape feature fusion framework for RGB-based classification of healthy, canker-infected, and melanose-affected orange fruit. Each image was represented by a compact descriptor integrating HSV color histograms, Local Binary Pattern micro-texture features, and Histogram of Oriented Gradients edge–shape information, followed by normalized feature concatenation and one-vs-rest Logistic Regression classification. On a balanced held-out test set, the proposed pipeline achieved 93.93% overall accuracy and a macro-F1 score of approximately 0.94, with class-wise F1-scores of 0.927 for citrus canker, 0.933 for healthy fruit, and 0.958 for melanose. Error analysis showed that residual misclassifications were concentrated mainly along the canker–healthy boundary. These findings demonstrate that well-designed handcrafted descriptors can provide accurate, transparent, and diagnostically meaningful citrus fruit disease recognition.

Code and data availability

The paper uses an open orange-fruit RGB image dataset and an HSV–LBP–HOG + Logistic Regression pipeline, but no public URL or repository for the dataset or the authors' code is provided. The Data Availability statement says feature code and training scripts can be packaged for release only upon acceptance, so assets (e

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