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Smart Agriculture ›› 2022, Vol. 4 ›› Issue (4): 74-83.doi: 10.12133/j.smartag.SA202209001

• Topic--Smart Farming of Field Crops • Previous Articles     Next Articles

High Quality Ramie Resource Screening Based on UAV Remote Sensing Phenotype Monitoring

FU Hongyu(), WANG Wei, LIAO Ao, YUE Yunkai, XU Mingzhi, WANG Ziwei, CHEN Jianfu, SHE Wei, CUI Guoxian()   

  1. College of Agronomy, Hunan Agricultural University, Changsha 410128, China
  • Received:2022-09-05 Online:2022-12-30
  • corresponding author: CUI Guoxian, E-mail:627274845@qq.com
  • About author:FU Hongyu, E-mail:347180050@qq.com
  • Supported by:
    National Key Research and Development Program of China (2018YFD0201106); Ministry of Finance and Ministry of Agriculture and Rural Affairs: National Modern Agricultural Industry Technology System (CARS-16-E11); National Natural Science Foundation of China (31471543)


Ramie is an important fiber crop. Due to the shortage of land resources and the promotion of excellent varieties, the genetic variation and diversity of ramie decreased, which increased the need for investigation and protection of the ramie germplasm resources diversity. The crop phenotype measurement method based on UAV remote sensing can conduct frequent, rapid, non-destructive and accurate monitoring of different genotypes, which can fulfill the investigation of crop germplasm resources and screen specific and high-quality varieties. In order to realize efficient comprehensive evaluation of ramie germplasm phenotype and assist in screening of dominant ramie varieties, a method for monitoring and screening ramie germplasm phenotype was proposed based on UAV remote sensing images. Firstly, based on UAV remote sensing images, the digital surface model (DSM) and orthophoto of the test area were generated by Pix4dmapper. Then, the key phenotypic parameters (plant height, plant number, leaf area index, leaf chlorophyll content and water content) of ramie germplasm resources were estimated. The subtraction method was used to extract ramie plant height based on DSM, while the target detection algorithm was applied to extract ramie plant number based on orthographic images, and four machine learning methods were used to estimate the leaf area index (LAI), leaf chlorophyll content (SPAD value) and water content. Finally, according to the extracted remote sensing phenotypic parameters, the genetic diversity of ramie germplasm was analyzed by using variability analysis and principal component analysis. The results showed that: (1) The ramie phenotype estimation based on UAV remote sensing was effective, with the fitting accuracy of plant height 0.93, and the root mean square error (RMSE) 5.654 cm. The fitting indexes of SPAD value, water content and LAI were 0.66, 0.79 and 0.74, respectively, and RMSE were 2.03, 2.21 and 0.63, respectively; (2) The remote sensing phenotypes of ramie germplasm were significantly different, as the coefficients of variation of LAI, plant height and plant number reached 20.82%, 24.61% and 35.48%, respectively; (3) Principal component analysis was used to cluster the remote sensing phenotypes into factor 1 (plant height and LAI) and factor 2 (LAI and SPAD value), factor 1 can be used to evaluate the structural characteristics of ramie germplasm resources, and factor 2 can be used as the screening index of high-light efficiency ramie resources. This study could provide references for crop germplasm phenotypic monitoring and breeding correlation analysis.

Key words: ramie, diversity of germplasm resources, phenotype, UAV remote sensing, digital surface model, machine learning

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