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Article

Mapping Tobacco Fields Using UAV RGB Images

1
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
2
Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China
3
Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
4
Powerchina Kunming Engineering Corporation Limited, Kunming 650051, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(8), 1791; https://doi.org/10.3390/s19081791
Received: 5 March 2019 / Revised: 6 April 2019 / Accepted: 6 April 2019 / Published: 15 April 2019
(This article belongs to the Special Issue Sensors in Agriculture 2019)
Tobacco planting information is an important part of tobacco production management. Unmanned aerial vehicle (UAV) remote sensing systems have become a popular topic worldwide because they are mobile, rapid and economic. In this paper, an automatic identification method for tobacco fields based on UAV images is developed by combining supervised classifications with image morphological operations, and this method was used in the Yunnan Province, which is the top province for tobacco planting in China. The results show that the produce accuracy, user accuracy, and overall accuracy of tobacco field identification using the method proposed in this paper are 92.59%, 96.61% and 95.93%, respectively. The method proposed in this paper has the advantages of automation, flow process, high accuracy and easy operation, but the ground sampling distance (GSD) of the UAV image has an effect on the accuracy of the proposed method. When the image GSD was reduced to 1 m, the overall accuracy decreased by approximately 10%. To solve this problem, we further introduced the convolution method into the proposed method, which can ensure the recognition accuracy of tobacco field is above 90% when GSD is less than or equal to 1 m. Some other potential improvements of methods for mapping tobacco fields were also discussed in this paper. View Full-Text
Keywords: tobacco field; UAV image; morphology; convolution tobacco field; UAV image; morphology; convolution
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MDPI and ACS Style

Zhu, X.; Xiao, G.; Wen, P.; Zhang, J.; Hou, C. Mapping Tobacco Fields Using UAV RGB Images. Sensors 2019, 19, 1791. https://doi.org/10.3390/s19081791

AMA Style

Zhu X, Xiao G, Wen P, Zhang J, Hou C. Mapping Tobacco Fields Using UAV RGB Images. Sensors. 2019; 19(8):1791. https://doi.org/10.3390/s19081791

Chicago/Turabian Style

Zhu, Xiufang, Guofeng Xiao, Ping Wen, Jinshui Zhang, and Chenyao Hou. 2019. "Mapping Tobacco Fields Using UAV RGB Images" Sensors 19, no. 8: 1791. https://doi.org/10.3390/s19081791

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