Use of Unmanned Aerial Vehicle Imagery and Deep Learning UNet to Extract Rice Lodging
1
National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
2
Faculty of International Trade, Shanxi University of Finance and Economics, Taiyuan 030006, China
3
School of Geography and Remote Sensing, Nanjing University of Information Science & Technology, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(18), 3859; https://doi.org/10.3390/s19183859
Received: 16 July 2019
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Revised: 3 September 2019
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Accepted: 4 September 2019
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Published: 6 September 2019
(This article belongs to the Section Remote Sensors)
Rice lodging severely affects harvest yield. Traditional evaluation methods and manual on-site measurement are found to be time-consuming, labor-intensive, and cost-intensive. In this study, a new method for rice lodging assessment based on a deep learning UNet (U-shaped Network) architecture was proposed. The UAV (unmanned aerial vehicle) equipped with a high-resolution digital camera and a three-band multispectral camera synchronously was used to collect lodged and non-lodged rice images at an altitude of 100 m. After splicing and cropping the original images, the datasets with the lodged and non-lodged rice image samples were established by augmenting for building a UNet model. The research results showed that the dice coefficients in RGB (Red, Green and Blue) image and multispectral image test set were 0.9442 and 0.9284, respectively. The rice lodging recognition effect using the RGB images without feature extraction is better than that of multispectral images. The findings of this study are useful for rice lodging investigations by different optical sensors, which can provide an important method for large-area, high-efficiency, and low-cost rice lodging monitoring research.
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Keywords:
rice lodging; UAV; UNet; semantic segmentation; assessment; Oryza sativa L.
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MDPI and ACS Style
Zhao, X.; Yuan, Y.; Song, M.; Ding, Y.; Lin, F.; Liang, D.; Zhang, D. Use of Unmanned Aerial Vehicle Imagery and Deep Learning UNet to Extract Rice Lodging. Sensors 2019, 19, 3859. https://doi.org/10.3390/s19183859
AMA Style
Zhao X, Yuan Y, Song M, Ding Y, Lin F, Liang D, Zhang D. Use of Unmanned Aerial Vehicle Imagery and Deep Learning UNet to Extract Rice Lodging. Sensors. 2019; 19(18):3859. https://doi.org/10.3390/s19183859
Chicago/Turabian StyleZhao, Xin, Yitong Yuan, Mengdie Song, Yang Ding, Fenfang Lin, Dong Liang, and Dongyan Zhang. 2019. "Use of Unmanned Aerial Vehicle Imagery and Deep Learning UNet to Extract Rice Lodging" Sensors 19, no. 18: 3859. https://doi.org/10.3390/s19183859
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