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.
Highlights
Rice [Oryza Sativa L.] is one of the world’s major food crops, and is the staple food for more than half of the world’s population [1]
To overcome the disadvantages of using the constant coefficient k, this paper proposes a method of edge contour wavelet analysis to correct for overlap and occlusion among the grains
The frequency of fluctuations in the edge of the panicle was used to characterize the degree of overlap between rice grains, and instead of using a correction factor with a fixed value k, the wavelet frequency ration f’/f 0 was used because it varies with the grain density of the panicle. When this method was used to count the number of rice grains in 50 genetically diverse panicles, the results indicated that the average counting error was 6% when compared to manual counting results
Summary
Rice [Oryza Sativa L.] is one of the world’s major food crops, and is the staple food for more than half of the world’s population [1]. Major goals of current agricultural research are to maximize the yield potential, grain quality, and stress resistance of rice grown in today’s changing climate [2]. There are many high-throughput phenotyping platforms to measure plant growth and morphology [4]. These platforms can be divided into three categories depending upon the measurement environment: platforms for laboratory-, greenhouse-, or field-based measurements [5]. The Australian Plant Phenotypic Group Facility (APPF) combines digital image processing technology, large-scale computing technology and robotics, which has been successfully applied to high-throughput precision modeling and prediction of grain biomass of greenhouse plants [6]. PhenoFab is a high-tech phenotyping platform developed
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