Wheat Growth Monitoring and Yield Estimation based on Multi-Rotor Unmanned Aerial Vehicle

Authors

Zhaopeng Fu, Nanjing Agricultural UniversityFollow
Jie Jang, Nanjing Agricultural UniversityFollow
Yang Gao, Nanjing Agricultural UniversityFollow
Brian Krienke, University of Nebraska - LincolnFollow
Meng Wang, Nanjing Agricultural UniversityFollow
Kaitai Zhong, Nanjing Agricultural UniversityFollow
Qiang Cao, Nanjing Agricultural UniversityFollow
Yongchao Tian, Nanjing Agricultural UniversityFollow
Yan Zhu, Nanjing Agricultural UniversityFollow
Weixing Cao, Nanjing Agricultural UniversityFollow
Xiaojun Liu, Nanjing Agricultural UniversityFollow

ORCID IDs

Qiang Cao

Yan Zhu

Xiaojun Liu

Date of this Version

2020

Citation

Remote Sens. 2020, 12, 508

Comments

© 2020 by the authors.

Open access

doi:10.3390/rs12030508

Abstract

Leaf area index (LAI) and leaf dry matter (LDM) are important indices of crop growth. Real-time, nondestructive monitoring of crop growth is instructive for the diagnosis of crop growth and prediction of grain yield. Unmanned aerial vehicle (UAV)-based remote sensing is widely used in precision agriculture due to its unique advantages in flexibility and resolution. This study was carried out on wheat trials treated with different nitrogen levels and seeding densities in three regions of Jiangsu Province in 2018–2019. Canopy spectral images were collected by the UAV equipped with a multi-spectral camera during key wheat growth stages. To verify the results of the UAV images, the LAI, LDM, and yield data were obtained by destructive sampling. We extracted the wheat canopy reflectance and selected the best vegetation index for monitoring growth and predicting yield. Simple linear regression (LR), multiple linear regression (MLR), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), artificial neural network (ANN), and random forest (RF) modeling methods were used to construct a model for wheat yield estimation. The results show that the multi-spectral camera mounted on the multi-rotor UAV has a broad application prospect in crop growth index monitoring and yield estimation. The vegetation index combined with the red edge band and the near-infrared band was significantly correlated with LAI and LDM. Machine learning methods (i.e., PLSR, ANN, and RF) performed better for predicting wheat yield. The RF model constructed by normalized difference vegetation index (NDVI) at the jointing stage, heading stage, flowering stage, and filling stage was the optimal wheat yield estimation model in this study, with an R² of 0.78 and relative root mean square error (RRMSE) of 0.1030. The results provide a theoretical basis for monitoring crop growth with a multi-rotor UAV platform and explore a technical method for improving the precision of yield estimation.