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Chalkiness in Rice: Potential for Evaluation with Image Analysis

^a Graduate School of Life and Environmental Sciences, Univ. of Tsukuba, Tsukuba, Ibaraki 305-8572, Japan, present address: National Institute of Vegetable and Tea Science, National Agriculture and Food Research Organization, Tsu, Mie 514-2392, Japan
^b Graduate School of Life and Environmental Sciences, Univ. of Tsukuba, Tsukuba, Ibaraki 305-8572, Japan
^c National Agricultural Research Center, National Agriculture and Food Research Organization, Tsukuba, Ibaraki 305-8666, Japan
^d Plant Biotechnology Institute, Ibaraki Agricultural Center, Mito, Ibaraki 311-4203, Japan

View larger version (28K): [in this window] [in a new window]   Figure 1. Grayscale images (upper row) and binary images (lower row) of rice grains: perfect rice (PR), white-based rice (WBSR), white-back rice (WBCR), white-back and -based rice (WBBR), white-belly rice (WBR), white-core rice (WCR), and milky-white rice (MWR). Since the grains were illuminated from behind, the chalky parts of the grains appear darker than normal parts.

View larger version (21K): [in this window] [in a new window]   Figure 2. Schematic diagrams of the data-acquisition process using grayscale images of rice grains. (A) Rotation of each grain image so that the long axis was vertical and the embryo was at the bottom left of the image. Dashed contours represent cases in which the original image was rotated by 30, 60, and 90 degrees. (B) The original chalkiness curve created from the dataset containing the mean grayscale values and five reconstructed chalkiness curves based on a constant plus the first two, four, eight, and 16 Fourier coefficients. (C) Division of a rice grain into 18 sections.

View larger version (15K): [in this window] [in a new window]   Figure 3. Reconstructed chalkiness curves for the perfect rice grain category and for the six immature rice grain categories shown in Fig. 1, using the first nine Fourier coefficients (including one constant). The categories are perfect rice (PR), white-based rice (WBSR), white-back rice (WBCR), white-back and -based rice (WBBR), white-belly rice (WBR), white-core rice (WCR), and milky-white rice (MWR).

View larger version (16K): [in this window] [in a new window]   Figure 4. Accuracy of the leave-one-out cross-validation (LOOCV) data in a classification problem using a support vector machine with a radial basis function (RBF) kernel based on the Fourier coefficients of the chalkiness curves for the perfect rice grain category and the six immature rice grain categories. Accuracy represents the overall rate of accurate classification. The number of Fourier coefficients was increased, in order, starting with the coefficients of the low harmonics.

View larger version (25K): [in this window] [in a new window]   Figure 5. Variation in the first two principal components (PC1 and PC2) in perfect rice grains (PR) and in six immature rice categories: white-based rice (WBSR), white-back rice (WBCR), white-back and -based rice (WBBR), white-belly rice (WBR), white-core rice (WCR), and milky-white rice (MWR). (A) PC1 vs. PC2 for the chalkiness curve method. (B) PC1 vs. PC2 for the portioning method.