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

Dahong Bian,Jiayu Wu,Mingzheng Zhang,Jianxi Huang,Zhe Liu,Hao Guo,Wei Wang,Wei Su

doi:10.3390/rs11172021

Abstract

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.

Highlights

Phenotyping is the process of rapidly profiling crop phenotypic traits such as plant height, canopy cover, density, biomass, and yield [1,2,3]
Compared to satellite remote sensing technologies [9], unmanned aerial vehicle (UAV) provide better spatial resolution and better temporal resolution. They have the potential to improve the identification of desirable traits and reduce the risk of data loss due to cloud/raining/smog cover and limitations resulting from the long revisit periods of satellites [10], since UAVs generally operate below the clouds
A methodology for estimating breeding corn plant heights, lodging areas, and canopy LAIs using calibrated UAV images has been proposed in this study

Summary

Introduction

Phenotyping is the process of rapidly profiling crop phenotypic traits such as plant height, canopy cover, density, biomass, and yield [1,2,3]. Compared to satellite remote sensing technologies [9], UAVs provide better spatial resolution and better temporal resolution. They have the potential to improve the identification of desirable traits and reduce the risk of data loss due to cloud/raining/smog cover and limitations resulting from the long revisit periods of satellites [10], since UAVs generally operate below the clouds. Remote sensing with UAVs will be an increasingly important and indispensable tool for phenotyping to support genomics-assisted plant breeding [11]

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Conclusion