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Article

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

1
National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, 1 Weigang Road, Nanjing 210095, China
2
Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
*
Author to whom correspondence should be addressed.
Received: 6 January 2020 / Revised: 30 January 2020 / Accepted: 1 February 2020 / Published: 5 February 2020
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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. View Full-Text
Keywords: UAV multispectral image; leaf area index; leaf dry matter; grain yield; estimation; wheat; machine learning UAV multispectral image; leaf area index; leaf dry matter; grain yield; estimation; wheat; machine learning
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MDPI and ACS Style

Fu, Z.; Jiang, J.; Gao, Y.; Krienke, B.; Wang, M.; Zhong, K.; Cao, Q.; Tian, Y.; Zhu, Y.; Cao, W.; Liu, X. Wheat Growth Monitoring and Yield Estimation based on Multi-Rotor Unmanned Aerial Vehicle. Remote Sens. 2020, 12, 508. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12030508

AMA Style

Fu Z, Jiang J, Gao Y, Krienke B, Wang M, Zhong K, Cao Q, Tian Y, Zhu Y, Cao W, Liu X. Wheat Growth Monitoring and Yield Estimation based on Multi-Rotor Unmanned Aerial Vehicle. Remote Sensing. 2020; 12(3):508. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12030508

Chicago/Turabian Style

Fu, Zhaopeng, Jie Jiang, Yang Gao, Brian Krienke, Meng Wang, Kaitai Zhong, Qiang Cao, Yongchao Tian, Yan Zhu, Weixing Cao, and Xiaojun Liu. 2020. "Wheat Growth Monitoring and Yield Estimation based on Multi-Rotor Unmanned Aerial Vehicle" Remote Sensing 12, no. 3: 508. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12030508

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