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LightTeaNet: A Weakly Supervised Lightweight CNN for Multi-Label Tea Leaf Disease Detection and Localization — arXiv:2608.14178

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.14178
License (as archived): CC0-1.0
Archived: 2026-10-19T04:04:26.516Z
SHA-256 of the archived text: d2e5286d8b49fe42ceb030f3ef75b68a427ec5c1b77895118b6755f705284818
Stable URL of this page: https://metraipolis.com/documento/dc_2bdba46bb2b2

How to cite

LightTeaNet: A Weakly Supervised Lightweight CNN for Multi-Label Tea Leaf Disease Detection and Localization — arXiv:2608.14178 — arXiv (en), https://arxiv.org/abs/2608.14178 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2bdba46bb2b2 (Metraipolis archive dc_2bdba46bb2b2, sha256 d2e5286d8b49fe42ceb030f3ef75b68a427ec5c1b77895118b6755f705284818)

BibTeX

@misc{dc2bdba46bb2b2,
  title = {LightTeaNet: A Weakly Supervised Lightweight CNN for Multi-Label Tea Leaf Disease Detection and Localization — arXiv:2608.14178},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.14178},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2bdba46bb2b2; sha256 d2e5286d8b49fe42ceb030f3ef75b68a427ec5c1b77895118b6755f705284818},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.14178. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Tea is known as an important crop in many parts of South and Southeast Asia, yet the production of tea is still hampered by the multiple diseases that decrease the quantity and quality. Traditional methods of inspection, which are manual, are not consistent, labor-intensive, and depend on extensive monitoring. This paper introduces a lightweight convolutional neural network (CNN) designed for weakly supervised multi-label classification and disease localization in tea leaves called LightTeaNet. LightTeaNet learns directly from image-level labels and employs Class Activation Mapping (CAM) to localize disease-affected regions automatically, unlike conventional object detection models such as YOLO, which require extensive bounding box annotations. For Parameter efficiency, the network integrates Depthwise Separable Convolutions, and for enhanced feature discrimination, it integrates Channel Attention. LightTeaNet has achieved a Precision of 0.9615, a Recall of 0.8772, and an F1-score of 0.9179, while it shows [email protected]=0.1810 without any manual annotations, which delivers a competitive localization performance in the experimental results. These results validate the model as an interpretable as well as a resource-efficient framework for intelligent disease monitoring in agriculture.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-17 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.14178 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T03:57:57.942Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-08-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).