Novel and Automatic Rice Thickness Extraction Based on Photogrammetry Using Rice Edge Features
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School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
2
Lab for Remote Sensing of Crop Phenotyping (LRSCP), Wuhan University, Wuhan 430079, China
3
College of Life Sciences, Wuhan University, Wuhan 430079, China
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(24), 5561; https://doi.org/10.3390/s19245561
Received: 31 October 2019
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Revised: 10 December 2019
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Accepted: 13 December 2019
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Published: 16 December 2019
(This article belongs to the Special Issue Advanced Sensor Technologies for Crop Phenotyping Application)
The dimensions of phenotyping parameters such as the thickness of rice play an important role in rice quality assessment and phenotyping research. The objective of this study was to propose an automatic method for extracting rice thickness. This method was based on the principle of binocular stereovision but avoiding the problem that it was difficult to directly match the corresponding points for 3D reconstruction due to the lack of texture of rice. Firstly, the shape features of edge, instead of texture, was used to match the corresponding points of the rice edge. Secondly, the height of the rice edge was obtained by way of space intersection. Finally, the thickness of rice was extracted based on the assumption that the average height of the edges of multiple rice is half of the thickness of rice. According to the results of the experiments on six kinds of rice or grain, errors of thickness extraction were no more than the upper limit of 0.1 mm specified in the national industry standard. The results proved that edge features could be used to extract rice thickness and validated the effectiveness of the thickness extraction algorithm we proposed, which provided technical support for the extraction of phenotyping parameters for crop researchers.
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MDPI and ACS Style
Kong, Y.; Fang, S.; Wu, X.; Gong, Y.; Zhu, R.; Liu, J.; Peng, Y. Novel and Automatic Rice Thickness Extraction Based on Photogrammetry Using Rice Edge Features. Sensors 2019, 19, 5561. https://doi.org/10.3390/s19245561
AMA Style
Kong Y, Fang S, Wu X, Gong Y, Zhu R, Liu J, Peng Y. Novel and Automatic Rice Thickness Extraction Based on Photogrammetry Using Rice Edge Features. Sensors. 2019; 19(24):5561. https://doi.org/10.3390/s19245561
Chicago/Turabian StyleKong, Yuchen, Shenghui Fang, Xianting Wu, Yan Gong, Renshan Zhu, Jian Liu, and Yi Peng. 2019. "Novel and Automatic Rice Thickness Extraction Based on Photogrammetry Using Rice Edge Features" Sensors 19, no. 24: 5561. https://doi.org/10.3390/s19245561
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