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A novel approach for estimating the flowering rate of litchi based on deep learning and UAV images.
Lin, Peiyi; Li, Denghui; Jia, Yuhang; Chen, Yingyi; Huang, Guangwen; Elkhouchlaa, Hamza; Yao, Zhongwei; Zhou, Zhengqi; Zhou, Haobo; Li, Jun; Lu, Huazhong.
Affiliation
  • Lin P; College of Engineering, South China Agricultural University, Guangzhou, China.
  • Li D; College of Engineering, South China Agricultural University, Guangzhou, China.
  • Jia Y; College of Engineering, South China Agricultural University, Guangzhou, China.
  • Chen Y; College of Engineering, South China Agricultural University, Guangzhou, China.
  • Huang G; College of Engineering, South China Agricultural University, Guangzhou, China.
  • Elkhouchlaa H; College of Engineering, South China Agricultural University, Guangzhou, China.
  • Yao Z; College of Engineering, South China Agricultural University, Guangzhou, China.
  • Zhou Z; College of Engineering, South China Agricultural University, Guangzhou, China.
  • Zhou H; College of Engineering, South China Agricultural University, Guangzhou, China.
  • Li J; College of Engineering, South China Agricultural University, Guangzhou, China.
  • Lu H; Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China.
Front Plant Sci ; 13: 966639, 2022.
Article in En | MEDLINE | ID: mdl-36092399
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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Front Plant Sci Year: 2022 Document type: Article Affiliation country: Country of publication:
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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Front Plant Sci Year: 2022 Document type: Article Affiliation country: Country of publication: