OptiNet-B3: a lightweight explainable deep learning model for multiclass classification of fruit and leaf diseases.

Abstract

Early and accurate detection of diseases is very important for the health of crops and ensuring sustainable agricultural productivity. This paper proposes OptiNet-B3, a novel approach and an efficient deep model for the multiclass classification of fruit and leaf diseases for apples, bananas, and oranges. Through two diverse and comprehensive image datasets, the model performs well for both fruit 13,602 images and leaf 11,199 images classification. OptiNet-B3 optimizes learning in low computational budget by integrating Mish activation, Convolutional Block Attention Module (CBAM), Group Normalization, and knowledge distillation. Great care in preprocessing and augmenting data was taken to improve generalization. Comparison with state-of-the-art models-including DenseNet121, ResNet50, MobileNetV3, and InceptionV3-based models-reveals that OptiNet-B3 substantially outperforms in terms of accuracy, with 98.12% and 99.23% on the fruit and leaf datasets, respectively. Due to its light-weight architecture, real-time deployment for in-field diagnosis on mobile and edge devices is much more feasible. The results underscore the potential of explainable, AI-driven tools in transforming plant disease management practices.

Plant phenotyping relevance

果実・葉の画像から植物病害を分類するモデルを開発し、複数データセットと既存モデルとの比較で性能検証しているため、植物状態の画像ベース表現型推定が中心です。

Code and data availability

The paper uses two publicly available Kaggle image datasets (D-I fruits, D-II leaves) as inputs, but these are third-party public datasets rather than paper-specific assets deposited by the authors. No author analysis code, trained model checkpoints, or data availability statement with an authors' public URL appears in

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