Assessment of spinach seedling health status and chlorophyll content by multivariate data analysis and multiple linear regression of leaf image features

摘要

Plant health and physiological status significantly influence chlorophyll content and photosynthetic capacity. Analysis of leaf reflectance information from digitized leaf images allows high-throughput, non-invasive and real-time estimation of chlorophyll content in a cost-effective manner. In the present study the application of multivariate data analysis tools, viz. principal component analysis (PCA) and agglomerative hierarchical clustering analysis (AHCA), has been discussed for distinguishing between spinach seedlings having high and low chlorophyll contents by simultaneously using the information provided by various image features. Further, leaf color information contained within different color spaces, viz. RGB (red, green and blue),rgb(normalized red, green and blue), HSI (hue, saturation and intensity), CIE (Commission Internationale de l’Eclairage) L?a?b?, CIE-XYZ, and CIE-xyY color spaces, has been used to predict chlorophyll content in terms of SPAD (Soil Plant Analysis Development) chlorophyll meter values by multiple linear regression. It was observed that the color indices R, G, R?+?G, R?B, G?B, R?+?G?B, Y (luminance) and DGCI (dark-green color index) exhibited high correlation (R2?>?0.8) with the SPAD values. Further, subjecting the leaf reflectance information provided by these color indices to PCA and AHCA enabled a clear segregation of seedlings with high and low chlorophyll contents. SPAD values predicted by the L?a?b?color space information yielded the lowest RMSE (root mean square error) and the highestR2(coefficient of determination) amongst the six color space features assessed. The findings of the present study indicate that concatenation of leaf reflectance information provided by different color indices may be more useful than individual color indices for assessing plant health status and predicting chlorophyll content using machine vision.