Improvement of Quantitative Evaluation Method for Plant Type of Rice

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1 Improvement of Quantitative Evaluation Method for Plant Type of Rice Katsuaki Suzuki 1, Zeyu Zheng 2 and Yutaka Hirata 1 1 Laboratory of Plant Genetics and Biotechnology, Tokyo University of Agriculture and Technology, Japan, peaton@cc.tuat.ac.jp 2 Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Science Abstract Conventional plant type evaluation has been performed by the observation based on Intuition and convenient measurements for length and angle. We tried to detect the shape of plant type and objectively evaluate the contour of plant type by using P-type Fourier Descriptors. At first, we analyzed young plants of some rice cultivars whether we can use the method to detect the characteristics of plant type in each variety. The images of individual plant type were taken by digital camera and the contours of the second, the third and the fourth leaves were extracted from these images. The contours were calculated by P-type Fourier Descriptor. Principle component analysis was performed on the data and applied to statistical methods. As a result, this method, using P-type Fourier Descriptors, could have been used to evaluate plant type in each variety quantitatively and objectively. Keywords: Plant type, P-type Fourier descriptors, Quantitative evaluation, Rice Introduction It is clear that improvement of Plant type which defines length, angle and placement of leaves and culms in rice and in wheat contributed to high yielding (e.g. green revolution). However, it has had still intrinsic problem that the high yield breeding inevitably inquire deeper plant type evaluation. When plant type of rice was evaluated in the previous studies, it was done by measuring length and angle of leaf and culm or it was classified by subjectively for some types such as elect, middle and open type. However, the classical methods have not been apprehended plant type objectively. Thus, it is necessary to develop and establish the objective and precise quantitative evaluation method for rice plant type. In recent years, image analytical method using Fourier descriptors has been one of the most widely-used methods for quantitative evaluation of plant morphology (Iwata et al. 1998, Yoshioka et al. 2004). With the image analytical method using Fourier descriptors, the target contours are apprehended as periodic functions. The Fourier transformation is performed for getting the Fourier coefficients. The obtained coefficients are used for description of plant morphology. P-type Fourier Descriptors (Uesaka 1984) among some Fourier descriptors can be applied to extract targeting open curve that a measurement start point and endpoint are different such as rice slender leaf. In addition, transforming P-type Fourier coefficients to principle component scores by principle component analysis, resulted scores can be used as an amount of characteristics for plant type, thus enable to apply for statistical genetics. Reconstructing the contours by performing Inverse Fourier Transformation using principle component scores, it is easy to visually look the result as evaluated figure by each principal component. As an example by the method for plant morphology, petal shape analysis was 1035

2 succeeded in Lotus (Zheng et al. 2005). However, there was no example applied to crops. We expect that the method using P-type Fourier descriptors can evaluate more objectively in rice plant type than conventional evaluation methods. The objective of this study is to establish the evaluation method for rice plant type at seedling stage. Finally, we expect that this method utilizes in breeding as juvenile stage selection. In this time, we studied on the effectiveness of objective evaluation method using P-type Fourier descriptors for rice plant type in seedling stages. Plant materials Five rice (Oryza sativa L.) varieties (three Indica type cv. IR64, MTL250 and Kasalath and two Japonica type cv. Nipponbare and Koshihikari) were used. Twenty plants per one variety (100 plants in total) were used. These plants were transplanted to 7 7 cm pots by single planting. These rice were grown in the greenhouse of Tokyo University of Agriculture and Technology in Image processing When the fifth leaf was developed for each plant (Fig. 1-A), a photo was taken with the farthest position between the tip of the second leaf and culm (Fig. 1-B). In the third and the fourth leaves, too (Fig. 1-B). The contours of leaf blade and culm part between leaf blades were extracted from digital images (Fig. 1-C). Below this study, the second leaf blade and culm part, the third leaf blade and culm part and the fourth leaf blade and culm part were called as the second leaf, the third leaf and the fourth leaf, respectively (Fig. 1-C). The 16 th order s coefficients of P-type Fourier descriptors were calculated from each leaf of each plant. Statistical analyses To summarize the morphological information of the coefficients of P-type Fourier descriptors, principle component analysis was performed to the coefficients in each leaf and contribution ratio of each principle component (PC) and principle component scores (PCSs) were obtained (Table 1). Obtained PCSs were used as an amount of characteristics for plant type in subsequent analyses. To detect plant type variation among varieties, we performed analysis of variance (ANOVA) using PCSs in each PC (Table 1). PCs with less than 5% significance called significant PCs as candidates of plant type variations and analized in order to determine the morphological meanings represented by significant PCs, the contours of the PCs were reconstructed by inverse Fourier transformation (Fig. 2). The morphological characters of significant PCs were determined by the reconstructed contours. To discuss whether significant PCs could adequately apprehend the characteristics of varieties, discriminant analysis using Support Vector Machine (SVM) was performed (Table 2). SVM is one of the best learning models in the ability of pattern recognition. Discriminant analysis using SVM was applied to estimate the correctness of discrimination, and the misclassification ratio was calculated by leave-one-out cross-validation method. The scatter plots in horizontal axis indicating varieties and vertical axis indicating significant PCSs were plotted to evaluate plant type of varieties (Fig. 3). In addition, plant type variations among varieties were detected by Tukey-Kramer multiple comparison test. We assumed that there was plant type variation between the combinations of varieties that were significant less than 5% and noted with different alphabet. 1036

3 Fig. 1 Extraction of leaf contours. (A) A picture of rice at 5 th leaf developing stage. (B) Digital images from each leaf. These were photographed by itself. (C) Extracted each leaf contours. Table 1 The contribution ratio of each principle component and the result of ANOVA by principle component score of three leaves. Second Leaf Third Leaf Fourth Leaf Component Contr (%) F value Contr (%) F value Contr (%) F value PC ** ** ** PC ** ** ** PC ** PC ** PC ** ** **, * P<0.01, P<0.05, PC Principle Component, Contr Contribution ratio. 1037

4 Second Leaf PC1: Erect or Droop, PC2: Leaf Angle, PC3: Leaf Sheath Length, PC5: Angle betwen Leaf Tip and Basal Part Third Leaf PC1: Leaf Angle, PC2: Leaf Length, PC4: Leaf Sheath Length Fourth Leaf PC1: Leaf Angle, PC2: Leaf Tip Angle Fig. 2 Visualizition of components in each leaf by Inverse Fourier transformation. The solid, broken or dashed lines showed contours that PCSs took 0 (average), +2 to -2 times of standard deviation, respectively. Table 2 Result of Misclassification ratio by Discriminant analysis using SVM Number of Correct Classification Misclassification Dataset IR64 MTL250 Kasalath Koshihikari Nipponbare Ratio (%) A 1) B 2) ) Dataset A included PC1 - PC3 with less than 5% significance in three leaves (7 variables) 2) Dataset B included PC1 - PC5 with less than 5% significance in three leaves (10 variables) 1038

5 P CS P C S Fig. 3 Scatter plots of each significant PC. Horizontal axis showed each variety. Vertical axis showed each PCS. Results Cumulative contribution ratios to the fifth principle component (PC5) were 79.5% in the second leaf, 93.5% in the third leaf and 95.5% in the fourth leaf, respectively (Table 1). By 1039

6 using PC5 in each leaf, greater parts of total variation were explained. Subsequently, we used PC1-PC5 in each leaf. To detect plant type variations among varieties, ANOVA was performed by using PCSs (Table 1). In the second leaf, PC1, PC2, PC3 and PC5 were significant at 1%. In the third leaf, PC1, PC2, PC4 and PC5 were significant at 1%. In the fourth leaf, PC1 and PC2 were significant at 1%. PCs with less than 5% significance were used in the subsequent analyses. Morphological characteristics of the significant PCs were visualized by inverse Fourier transformation (Fig. 2).In PC1 of the second leaf, it seemed to evaluate leaf blade angle. But, PC1 of the second leaf could actually evaluate whether the tip of leaf blade was erect or droop. Therefore, this PC might be used to evaluate the environmental variation. Misclassification ratios were calculated by discriminant analysis using SVM (Table 2). In dataset A including PC1, PC2 and PC3 in the significant PCs (with 7 variables), the misclassification ratio was 20%. In dataset B with including PC1 to PC5 in the significant PCs (10 variables), misclassification ratio was 7%. In dataset B, each variety could be discriminated and total discrimination ratio was very high. Therefore, dataset B could adequately apprehend the characteristics of each variety in plant type. The scatter plots were plotted the varieties on horizontal axis and the significant PCSs on vertical axis (Fig. 3). In these figures, plant type could be quantitatively evaluated. In PC1 of the second leaf, some individuals with PCSs in the larger minus range had drooped tip of leaf blade. This PC was not represented for the characteristics of each variety. For this reason, PC1 of the second leaf might not be genetical variation but environmental variation. Discussion From the result of discriminant analysis using SVM, dataset B using PC1 - PC5 had a larger probability of discrimination than dataset A using PC1-PC3 (Table 2). Cumulative contribution ratios of PC1-PC5 of the second, the third and the fourth leaves were 79.5%, 93.5% and 95.5% and greater parts of total variation in each leaf could be explained. Scatter plots and Tukey-Kramer multiple comparison test were also effect to detect the differences of plant type among the varieties. Therefore, characteristics of the varieties could be adequately apprehended using significant PCs. In conclusion, it is probable that this method using P-type Fourier descriptors is effective for the evaluation of rice plant type in seedling stage. References Iwata H, Niikura S, Matsuura S, Takano Y, Ukai Y (1998) Evaluation of variation of root shape of Japanese radish (Raphanus sativus L.) based on image analysis using elliptic Fourier descriptors. Euphytica 102: Uesaka Y (1984) A new type Fourier descriptor method that is effective also to open contour. IEICE Trans Inf Syst J67 JA3: (in Japanese) Yoshioka Y, Iwata H, Ohasawa R, Ninomiya S (2004) Analysis of petal shape variation of Primula sieboldii by elliptic fourier descriptors and principal component analysis. Ann Bot 94: Zheng Z, Iwata H, Ninomiya S, Tamura Y (2005) Quantitative evaluation of partial shape characteristics of petals of the sacred lotus based on P-type Fourier descriptors. Breed Res 7: (in Japanese) 1040