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Orchid2024: A cultivar-level dataset and methodology for fine-grained classification of Chinese Cymbidium Orchids.
Peng, Yingshu; Zhou, Yuxia; Zhang, Li; Fu, Hongyan; Tang, Guimei; Huang, Guolin; Li, Weidong.
Afiliación
  • Peng Y; Horticultural Research Institute, Hunan Academy of Agricultural Sciences, Changsha, 410125, PR China.
  • Zhou Y; Yuelu Mountain Laboratory, Changsha, PR China.
  • Zhang L; Horticultural Research Institute, Hunan Academy of Agricultural Sciences, Changsha, 410125, PR China.
  • Fu H; Yuelu Mountain Laboratory, Changsha, PR China.
  • Tang G; Horticultural Research Institute, Hunan Academy of Agricultural Sciences, Changsha, 410125, PR China.
  • Huang G; Yuelu Mountain Laboratory, Changsha, PR China.
  • Li W; Horticultural Research Institute, Hunan Academy of Agricultural Sciences, Changsha, 410125, PR China.
Plant Methods ; 20(1): 124, 2024 Aug 13.
Article en En | MEDLINE | ID: mdl-39138512
ABSTRACT

BACKGROUND:

Chinese Cymbidium orchids, cherished for their deep-rooted cultural significance and significant economic value in China, have spawned a rich tapestry of cultivars. However, these orchid cultivars are facing challenges from insufficient cultivation practices and antiquated techniques, including cultivar misclassification, complex identification, and the proliferation of counterfeit products. Current commercial techniques and academic research primarily emphasize species identification of orchids, rather than delving into that of orchid cultivars within species.

RESULTS:

To bridge this gap, the authors dedicated over a year to collecting a cultivar image dataset for Chinese Cymbidium orchids named Orchid2024. This dataset contains over 150,000 images spanning 1,275 different categories, involving visits to 20 cities across 12 provincial administrative regions in China to gather pertinent data. Subsequently, we introduced various visual parameter-efficient fine-tuning (PEFT) methods to expedite model development, achieving the highest top-1 accuracy of 86.14% and top-5 accuracy of 95.44%.

CONCLUSION:

Experimental results demonstrate the complexity of the dataset while highlighting the considerable promise of PEFT methods within flower image classification. We believe that our work not only provides a practical tool for orchid researchers, growers and market participants, but also provides a unique and valuable resource for further exploring fine-grained image classification tasks. The dataset and code are available at https//github.com/pengyingshu/Orchid2024 .
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Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Plant Methods Año: 2024 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Plant Methods Año: 2024 Tipo del documento: Article