Estimation of Growth Parameters in Magnolia sieboldii Saplings through Image Analysis

Image processing techniques for plant phenotypes are rapidly evolving, allowing faster, non-destructive and objective evaluation of plant growth parameters than conventional methods for plant physiological studies. The aim of this study was to evaluate the green area rate (GAR) per plant through smartphone images of magnolia, and to predict the differences in the growth parameters including root length from the differences in green area rate of ordinary saplings and ones grown using cerium chloride (CeCl3). An image analysis program developed using fuzzy C-means clustering algorithm from digital images was used to estimate GAR of magnolia saplings, and the correlation between green area rate and growth parameters was determined and regression model was constructed. Correlation coefficient of GAR was the highest with leaf length of 0.98 and lowest with above ground total dry mass of 0.87. Accuracy between the predicted values and measured values was estimated, and then difference of GAR between control saplings and ones grown using cerium chloride was calculated. Based on the changes in green area percentage, we predicted difference in growth parameters. No significant difference existed between the predicted and the measured values. The results obtained in present paper will play a significant role in predicting the growth parameters of saplings without affecting the growth and in detecting effectiveness of various growth promoters.