A Resource-Efficient Hybrid CNN-LSTM network for image-based bean leaf disease classification
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915938641641472 |
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| author | Rhee, Hye Jin Akinyemi, Joseph Damilola |
| author_facet | Rhee, Hye Jin Akinyemi, Joseph Damilola |
| contents | Accurate and resource-efficient automated diagnosis is a cornerstone of modern agricultural expert systems. While Convolutional Neural Networks (CNNs) have established benchmarks in plant pathology, their ability to capture long-range spatial dependencies is often limited by standard pooling layers, and their high memory footprint hinders deployment on portable devices. This paper proposes a lightweight hybrid CNN-LSTM system for bean leaf disease classification. By integrating an LSTM layer to model the spatial-sequential relationships within feature maps, our hybrid architecture achieves a 94.38% accuracy while maintaining an exceptionally small footprint of 1.86 MB; a 70% reduction in size compared to traditional CNN-based systems. Furthermore, we provide a systematic evaluation of image augmentation strategies, demonstrating that tailored transformations are superior to generic combinations for maintaining the integrity of diagnostic patterns. Results on the $\textit{ibean}$ dataset confirm that the proposed system achieves state-of-the-art F1 scores of 99.22% with EfficientNet-B7+LSTM, providing a robust and scalable framework for real-time agricultural decision support in resource-constrained environments. The code and augmented datasets used in this study are publicly available on this $\href{https://github.com/HJin-R/bean_disease}{Github}$ repo. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13835 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Resource-Efficient Hybrid CNN-LSTM network for image-based bean leaf disease classification Rhee, Hye Jin Akinyemi, Joseph Damilola Computer Vision and Pattern Recognition I.2; I.4; I.5 Accurate and resource-efficient automated diagnosis is a cornerstone of modern agricultural expert systems. While Convolutional Neural Networks (CNNs) have established benchmarks in plant pathology, their ability to capture long-range spatial dependencies is often limited by standard pooling layers, and their high memory footprint hinders deployment on portable devices. This paper proposes a lightweight hybrid CNN-LSTM system for bean leaf disease classification. By integrating an LSTM layer to model the spatial-sequential relationships within feature maps, our hybrid architecture achieves a 94.38% accuracy while maintaining an exceptionally small footprint of 1.86 MB; a 70% reduction in size compared to traditional CNN-based systems. Furthermore, we provide a systematic evaluation of image augmentation strategies, demonstrating that tailored transformations are superior to generic combinations for maintaining the integrity of diagnostic patterns. Results on the $\textit{ibean}$ dataset confirm that the proposed system achieves state-of-the-art F1 scores of 99.22% with EfficientNet-B7+LSTM, providing a robust and scalable framework for real-time agricultural decision support in resource-constrained environments. The code and augmented datasets used in this study are publicly available on this $\href{https://github.com/HJin-R/bean_disease}{Github}$ repo. |
| title | A Resource-Efficient Hybrid CNN-LSTM network for image-based bean leaf disease classification |
| topic | Computer Vision and Pattern Recognition I.2; I.4; I.5 |
| url | https://arxiv.org/abs/2604.13835 |