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Phenotyping of Corn Plants Using Unmanned Aerial Vehicle (UAV) Images

by 1,2, 1,2, 3, 1,2,*, 1,2, 1,2, 1,2 and 1,2
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
College of Agronomy, Hebei Agricultural University, Baoding 071001, China
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(17), 2021;
Received: 3 July 2019 / Revised: 12 August 2019 / Accepted: 23 August 2019 / Published: 28 August 2019
Phenotyping provides important support for corn breeding. Unfortunately, the rapid detection of phenotypes has been the major limiting factor in estimating and predicting the outcomes of breeding programs. This study was focused on the potential of phenotyping to support corn breeding using unmanned aerial vehicle (UAV) images, aiming at mining and deepening UAV techniques for comparing phenotypes and screening new corn varieties. Two geometric traits (plant height, canopy leaf area index (LAI)) and one lodging resistance trait (lodging area) were estimated in this study. It was found that stereoscopic and photogrammetric methods were promising ways to calculate a digital surface model (DSM) for estimating corn plant height from UAV images, with R2 = 0.7833 (p < 0.001) and a root mean square error (RMSE) = 0.1677. In addition to a height estimation, the height variation was analyzed for depicting and validating the corn canopy uniformity stability for different varieties. For the lodging area estimation, the normalized DSM (nDSM) method was more promising than the gray-level co-occurrence matrix (GLCM) textural features method. The estimation error using the nDSM ranged from 0.8% to 5.3%, and the estimation error using the GLCM ranged from 10.0% to 16.2%. Associations between the height estimation and lodging area estimation were done to find the corn varieties with optimal plant heights and lodging resistance. For the LAI estimation, the physical radiative transfer PROSAIL model offered both an accurate and robust estimation performance both at the middle (R2 = 0.7490, RMSE = 0.3443) and later growing stages (R2 = 0.7450, RMSE = 0.3154). What was more exciting was that the estimated sequential time series LAIs revealed a corn variety with poor resistance to lodging in a study area of Baogaofeng Farm. Overall, UAVs appear to provide a promising method to support phenotyping for crop breeding, and the phenotyping of corn breeding in this study validated this application. View Full-Text
Keywords: phenotyping; unmanned aerial vehicle (UAV); remote sensing; corn plant height; lodging; LAI phenotyping; unmanned aerial vehicle (UAV); remote sensing; corn plant height; lodging; LAI
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MDPI and ACS Style

Su, W.; Zhang, M.; Bian, D.; Liu, Z.; Huang, J.; Wang, W.; Wu, J.; Guo, H. Phenotyping of Corn Plants Using Unmanned Aerial Vehicle (UAV) Images. Remote Sens. 2019, 11, 2021.

AMA Style

Su W, Zhang M, Bian D, Liu Z, Huang J, Wang W, Wu J, Guo H. Phenotyping of Corn Plants Using Unmanned Aerial Vehicle (UAV) Images. Remote Sensing. 2019; 11(17):2021.

Chicago/Turabian Style

Su, Wei, Mingzheng Zhang, Dahong Bian, Zhe Liu, Jianxi Huang, Wei Wang, Jiayu Wu, and Hao Guo. 2019. "Phenotyping of Corn Plants Using Unmanned Aerial Vehicle (UAV) Images" Remote Sensing 11, no. 17: 2021.

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