<![CDATA[Early diagnosis of rice leaf diseases remains challenging due to subtle symptom manifestation, uncontrolled illumination, heterogeneous backgrounds, and the limited interpretability of purely data-driven models. This study proposes an explainable hybrid framework integrating a Mamdani Fuzzy Inference System (FIS) with an Artificial Neural Network (ANN) for early rice leaf disease diagnosis under real-field conditions. The framework combines engineered symptom descriptors extracted from segmented leaf regions (GLCM texture and HSV color features), acquisition-time environmental measurements, and a fuzzy-derived disease severity cue to mitigate symptom ambiguity while preserving rule-based interpretability. Experiments were conducted on 8,000 field-acquired rice leaf images collected from multiple locations, covering Healthy, bacterial leaf blight, brown spot, and leaf smut classes. Evaluation followed a leakage-controlled, location-disjoint protocol. Across five independent runs, the proposed FIS–ANN achieved an average accuracy of 91.3 ± 0.6% and a macro-F1 score of 90.8 ± 0.7%, significantly outperforming a feature-based ANN and a fine-tuned ResNet-18 baseline (paired McNemar test, p < 0.05). Per-class analysis shows consistent recall improvements for visually overlapping diseases, and additional evaluation on mild-severity samples confirms maintained sensitivity at early disease stages. Field deployment experiments using smartphone-acquired images from unseen locations further demonstrate robust generalization with low on-device inference latency. These results indicate that integrating fuzzy severity reasoning into a lightweight neural classifier provides a practical balance between performance, interpretability, and computational efficiency, supporting early disease screening and mobile decision-support applications in precision agriculture.]]>
