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Estimation of wheat planting density using UAV image |
WANG Wei1,2( ), WANG Xinsheng1,2, YAO Chan1,2, JIN Tian1,2, WU Jiayu1,2, SU Wei1,2( ) |
1. College of Land Science and Technology, China Agricultural University, Beijing 100083, China 2. Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China |
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Abstract Wheat is a densely planted crop, and the planting volume per acre is nearly 20 kg. The plant density of winter wheat will directly affect the final yield. Therefore, real-time monitoring of wheat plant density is an important way to ensure wheat yield. At present, the main method for obtaining the plant density of wheat is mainly manual measurement, which is time-consuming and laborious. In this paper, the DJ inspire 2 UAV is equipped with a Zens X4S camera to obtain high-resolution visible light images of wheat planting areas, extract wheat coverage based on UAV images, and establish the relationship between plant density and plant density so as to achieve rapid acquisition of wheat plant density based on UAV image. Experiments show the following results: ① Using the improved HSI color model to extract wheat coverage improves accuracy and extraction efficiency compared with traditional visual estimation, manual counting and other classification methods, and overcomes differences in lighting conditions and shadows of different sorts of UAV images influences. ② There is a high correlation between wheat coverage and plant density at the seedling stage, overwintering stage and turning green stage. Among them, the correlation coefficient R2 between the coverage based on drone image and the plant density of wheat are 0.737 9, 0.898 1 and 0.897 6 in three growth stages. The verification results of the relationship model using Niutengyu Village samples show that the inversion results based on the established relationship model also have a good correlation with the measured values, and R2 reaches 0.919 8.
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Keywords
wheat
UAV image
vegetation cover
planting density
HSI color model
Hough transform
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Corresponding Authors:
SU Wei
E-mail: wangwei007@cau.edu.cn;suwei@cau.edu.cn
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Issue Date: 23 December 2020
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