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

Naveen, Kothakota, Ajitha, D.·2025·Scientific Reports
DOI 10.1038/s41598-025-25888-3
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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.

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Cited by 5

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Metadata

Authors
Naveen, K., & Ajitha, D.
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2025-11-25
Volume
15
Issue
1
ISSN
2045-2322
DOI
10.1038/s41598-025-25888-3
Cited by
5
Language
en
Licence
Funders
Vellore Institute of Technology, Vellore

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Naveen, K., & Ajitha, D. (2025). OptiNet-B3: a lightweight explainable deep learning model for multiclass classification of fruit and leaf diseases. Scientific Reports. https://doi.org/10.1038/s41598-025-25888-3

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