Detecting maize leaf water status by using digital RGB images

To explore the correlation between crop leaf digital RGB (Red, Green and Blue) image features and the corresponding moisture content of the leaf, a Canon digital camera was used to collect image information from detached leaves of heading-stage maize. A drying method was adopted to measure the moisture content of the leaf samples, and image processing technologies, including gray level co-occurrence matrices and grayscale histograms, was used to extract the maize leaf texture feature parameters and color feature parameters. The correlations of these feature parameters with moisture content were analyzed. It is found that the texture parameters of maize leaf RGB images, including contrast, correlation, entropy and energy, were not significantly correlated with moisture content. Thus, it was difficult to use these features to predict moisture content. Of the six groups of eigenvalues for the leaf color feature parameters, including mean, variance, energy, entropy, kurtosis and skewness, mean and kurtosis were found to be correlated with moisture content. Thus, these features could be used to predict the leaf moisture content. The correlation coefficient (R-2) of the mean-moisture content relationship model was 0.7017, and the error of the moisture content prediction was within +/- 2%. The R-2 of the kurtosis-moisture content relationship model was 0.7175, and the error of the moisture content prediction was within +/- 1.5%. The study results proved that RUB images of crop leaves could be used to measure moisture content.