Gaussian Kernel Fuzzy C-Means (GKFCM) and Correlation Weight Bayesian SqueezeNet (CWBSN) Classifier for Banana Leaf Diseases Diagnosis Using Electrochemical Sensor-Based Image Segmentation and AI Techniques

Abstract

Bananas are among the most widely cultivated fruits worldwide and constitute a major staple crop in many developing regions. Banana production is highly vulnerable to foliar diseases, which significantly reduce yield and economic sustainability. Early and accurate disease diagnosis plays a central role in mitigating crop losses. This study presents an image-based artificial intelligence framework for banana leaf disease diagnosis using advanced segmentation and deep learning techniques. Gaussian Kernel Fuzzy C-Means (GKFCM) clustering is employed for pixel-level segmentation, enabling precise isolation of diseased regions by preserving nonlinear boundary characteristics and minimizing intra-cluster variance. For disease classification, a Correlation Weight Bayesian SqueezeNet (CWBSN) model is introduced, integrating Bayesian optimization and correlation-aware feature weighting to enhance discriminative representation and classification robustness. The framework is evaluated using RGB images obtained from the BananaLSD dataset and field-acquired imagery collected at Bangabandhu Sheikh MujiburRahman Agricultural University (BSMRAU), Bangladesh. Performance is assessed using accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), and Receiver Operating Characteristic (ROC) analysis. Experimental results demonstrate that the proposed GKFCM–CWBSN framework achieves reliable and consistent disease recognition based solely on visual symptom analysis. The study establishes a computational foundation for image-driven plant disease diagnosis, with potential adaptability to future multimodal agricultural sensing systems.

Plant phenotyping relevance

バナナ葉の病斑領域を画像から抽出・分類する手法の開発が研究の中心であり、植物病害状態の表現型推定に該当する。

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