Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/119027
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Type: Journal article
Title: Image-based on-panicle rice [Oryza sativa L.] grain counting with a prior edge wavelet correction model
Author: Gong, L.
Lin, K.
Wang, T.
Liu, C.
Yuan, Z.
Zhang, D.
Hong, J.
Citation: Agronomy, 2018; 8(6):91-1-91-14
Publisher: MDPI
Issue Date: 2018
ISSN: 2073-4395
2073-4395
Statement of
Responsibility: 
Liang Gong, Ke Lin, Tao Wang, Chengliang Liu, Zheng Yuan, Dabing Zhang and Jun Hong
Abstract: The number of rice grains on a panicle is an important index for variety screening during high-quality rice [Oryza Sativa L.] breeding. For an in-vivo image-based measurement, the occlusion and overlapping among grains are the major challenges in non-destructive precise phenotyping of the on-panicle grains. In order to tackle these challenges, this paper describes a correction-model-referred on-panicle grain counting method based on the area of the rice panicle and its edge contour wavelet analysis. First, we assume that a deterministic correlation exists between the number of grains of the panicle and the traits of its edge contour morphology, which reflects the extent to which the grains are occluded. Second, a method for coarsely estimating grain number per panicle is proposed based on the projective area of the panicle in the image and the average area of a rice grain. Finally, a correction model which is built with the average wavelet frequency of the edge contour of the panicle is employed to correct the estimated value of the grain number. Two randomly selected cases are investigated in detail, showing that computation accuracy with a correction model is increased by 26% and 23% respectively when compared to that of the naive area-based computation. In conclusion, we reveal and validate the relationship between the number of grains of the panicle and the fluctuation frequency of its edge contours. Further, experiments show that errors caused by overlapping and occlusion scenarios can be alleviated with the estimation and correction hybrid models, achieving an average accuracy of 94% compared to the results of manual counting.
Keywords: Rice panicle; phenotyping; grain counting; image processing; wavelet analysis
Rights: © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
RMID: 0030095032
DOI: 10.3390/agronomy8060091
Appears in Collections:Agriculture, Food and Wine publications

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