Energy-Aware Ensemble Learning for Coffee Leaf Disease Classification
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866914261190574080 |
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| author | Moreira, Larissa Ferreira Rodrigues Moreira, Rodrigo Rodrigues, Leonardo Gabriel Ferreira |
| author_facet | Moreira, Larissa Ferreira Rodrigues Moreira, Rodrigo Rodrigues, Leonardo Gabriel Ferreira |
| contents | Coffee yields are contingent on the timely and accurate diagnosis of diseases; however, assessing leaf diseases in the field presents significant challenges. Although Artificial Intelligence (AI) vision models achieve high accuracy, their adoption is hindered by the limitations of constrained devices and intermittent connectivity. This study aims to facilitate sustainable on-device diagnosis through knowledge distillation: high-capacity Convolutional Neural Networks (CNNs) trained in data centers transfer knowledge to compact CNNs through Ensemble Learning (EL). Furthermore, dense tiny pairs were integrated through simple and optimized ensembling to enhance accuracy while adhering to strict computational and energy constraints. On a curated coffee leaf dataset, distilled tiny ensembles achieved competitive with prior work with significantly reduced energy consumption and carbon footprint. This indicates that lightweight models, when properly distilled and ensembled, can provide practical diagnostic solutions for Internet of Things (IoT) applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_12109 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Energy-Aware Ensemble Learning for Coffee Leaf Disease Classification Moreira, Larissa Ferreira Rodrigues Moreira, Rodrigo Rodrigues, Leonardo Gabriel Ferreira Computer Vision and Pattern Recognition Coffee yields are contingent on the timely and accurate diagnosis of diseases; however, assessing leaf diseases in the field presents significant challenges. Although Artificial Intelligence (AI) vision models achieve high accuracy, their adoption is hindered by the limitations of constrained devices and intermittent connectivity. This study aims to facilitate sustainable on-device diagnosis through knowledge distillation: high-capacity Convolutional Neural Networks (CNNs) trained in data centers transfer knowledge to compact CNNs through Ensemble Learning (EL). Furthermore, dense tiny pairs were integrated through simple and optimized ensembling to enhance accuracy while adhering to strict computational and energy constraints. On a curated coffee leaf dataset, distilled tiny ensembles achieved competitive with prior work with significantly reduced energy consumption and carbon footprint. This indicates that lightweight models, when properly distilled and ensembled, can provide practical diagnostic solutions for Internet of Things (IoT) applications. |
| title | Energy-Aware Ensemble Learning for Coffee Leaf Disease Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.12109 |