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An Overview of High-Throughput Crop Phenotyping: Platform, Image Analysis, Data Mining, and Data Management.
Yang, Wanneng; Feng, Hui; Hu, Xiao; Song, Jingyan; Guo, Jing; Lu, Bingjie.
Afiliación
  • Yang W; National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China. ywn@mail.hzau.edu.cn.
  • Feng H; National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China.
  • Hu X; National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China.
  • Song J; National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China.
  • Guo J; National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China.
  • Lu B; National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China.
Methods Mol Biol ; 2787: 3-38, 2024.
Article en En | MEDLINE | ID: mdl-38656479
ABSTRACT
In this chapter, we explore the application of high-throughput crop phenotyping facilities for phenotype data acquisition and the extraction of significant information from the collected data through image processing and data mining methods. Additionally, the construction and outlook of crop phenotype databases are introduced and the need for global cooperation and data sharing is emphasized. High-throughput crop phenotyping significantly improves accuracy and efficiency compared to traditional measurements, making significant contributions to overcoming bottlenecks in the phenotyping field and advancing crop genetics.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Fenotipo / Procesamiento de Imagen Asistido por Computador / Productos Agrícolas / Minería de Datos Idioma: En Revista: Methods Mol Biol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2024 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Fenotipo / Procesamiento de Imagen Asistido por Computador / Productos Agrícolas / Minería de Datos Idioma: En Revista: Methods Mol Biol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2024 Tipo del documento: Article País de afiliación: China