Identification of Soybean Mutant Lines Based on Dual-Branch CNN Model Fusion Framework Utilizing Images from Different Organs.

Wu, Guangxia; Fei, Lin; Deng, Limiao; Yang, Haoyan; Han, Meng; Han, Zhongzhi; Zhao, Longgang

Wu, Guangxia; Fei, Lin; Deng, Limiao; Yang, Haoyan; Han, Meng; Han, Zhongzhi; Zhao, Longgang.

Afiliação

Wu G; College of Agronomy, Qingdao Agricultural University, Qingdao 266109, China.
Fei L; Academy of Dongying Efficient Agricultural Technology and Industry on Saline and Alkaline Land in Collaboration with Qingdao Agricultural University, Dongying 257091, China.
Deng L; Qingdao Key Laboratory of Specialty Plant Germplasm Innovation and Utilization in Saline Soils of Coastal Beach, Qingdao Agricultural University, Qingdao 266109, China.
Yang H; College of Grassland Science, Qingdao Agricultural University, Qingdao 266109, China.
Han M; Academy of Dongying Efficient Agricultural Technology and Industry on Saline and Alkaline Land in Collaboration with Qingdao Agricultural University, Dongying 257091, China.
Han Z; College of Science and Information, Qingdao Agricultural University, Qingdao 266109, China.
Zhao L; College of Science and Information, Qingdao Agricultural University, Qingdao 266109, China.

Plants (Basel) ; 12(12)2023 Jun 14.

Article em En | MEDLINE | ID: mdl-37375940

Palavras-chave

dual-convolutional neural network; feature fusion; identification; mutant lines; soybean

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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