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Remote Sens. 2018, 10(2), 246; https://doi.org/10.3390/rs10020246

Recognition of Wheat Spike from Field Based Phenotype Platform Using Multi-Sensor Fusion and Improved Maximum Entropy Segmentation Algorithms

1
School of Electronics and Information Engineering, Anhui University, Hefei 230601, China
2
Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture China, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
3
National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China
4
Key Laboratory of Agri-Informatics, Ministry of Agriculture, Beijing 100097, China
*
Author to whom correspondence should be addressed.
Received: 20 November 2017 / Revised: 31 January 2018 / Accepted: 2 February 2018 / Published: 6 February 2018
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Abstract

To obtain an accurate count of wheat spikes, which is crucial for estimating yield, this paper proposes a new algorithm that uses computer vision to achieve this goal from an image. First, a home-built semi-autonomous multi-sensor field-based phenotype platform (FPP) is used to obtain orthographic images of wheat plots at the filling stage. The data acquisition system of the FPP provides high-definition RGB images and multispectral images of the corresponding quadrats. Then, the high-definition panchromatic images are obtained by fusion of three channels of RGB. The Gram–Schmidt fusion algorithm is then used to fuse these multispectral and panchromatic images, thereby improving the color identification degree of the targets. Next, the maximum entropy segmentation method is used to do the coarse-segmentation. The threshold of this method is determined by a firefly algorithm based on chaos theory (FACT), and then a morphological filter is used to de-noise the coarse-segmentation results. Finally, morphological reconstruction theory is applied to segment the adhesive part of the de-noised image and realize the fine-segmentation of the image. The computer-generated counting results for the wheat plots, using independent regional statistical function in Matlab R2017b software, are then compared with field measurements which indicate that the proposed method provides a more accurate count of wheat spikes when compared with other traditional fusion and segmentation methods mentioned in this paper. View Full-Text
Keywords: ground phenotype platform; counting of wheat; Gram-Schmidt fusion algorithm; firefly algorithm based on chaos theory ground phenotype platform; counting of wheat; Gram-Schmidt fusion algorithm; firefly algorithm based on chaos theory
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhou, C.; Liang, D.; Yang, X.; Xu, B.; Yang, G. Recognition of Wheat Spike from Field Based Phenotype Platform Using Multi-Sensor Fusion and Improved Maximum Entropy Segmentation Algorithms. Remote Sens. 2018, 10, 246.

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