Modeling of maize canopy color in whole growth period based on in-situ monitoring system and its application

Abstract: Maize canopy leaf color is an intuitive reflection of maize growth, development, and physiological and biochemical status, and also an important trait for maize phenotypic detection in the field investigation. Both visual observation and quantitative analysis showed that different solar radiation had significant effects on maize canopy hue (CH), and the CH value of maize canopy had significant changes at different growth stages. Nowadays, high-throughput phenotyping platforms have gradually been applied from controllable indoor environment to uncontrollable field environment, however, the complex field condition brings a lot of challenges to the current phenotyping techniques. In the field-based maize growth monitoring application, how to quantitatively analyze the color variation tendency of maize canopy in field environment is still an urgent problem to be solved. In this study, we developed sets of in-situ monitoring systems in the field to continuously capture canopy image sequences for 2 maize cultivars (DH 605 and ND 108) in the whole growth stage, and respectively collected 6 data sets of maize canopy image in consideration of 2 types of different weather conditions (sunny and cloudy days) and 6 key growth stages (4 leaves, 9 leaves, 16 leaves, silk, blister and milk stages). These image data sets of maize canopy not only reflected the effect of different weather conditions on canopy color, but also reflected the natural changes of canopy color at different growth stages, so they could be used for the color quantification and evaluation of maize canopy. With these data sets, statistical analysis based on the HSV (hue, saturation, value) color space in the pixel level was utilized to reveal the relationship among solar radiation, image color and canopy color. The results of quantitative analysis showed: Solar radiation had little effect on image value (IV) and CH, but had great effect on image hue (IH) and canopy value (CV), and the distribution of the canopy pixels at the same CV value was approximately consistent with the normal distribution. And then, the canopy CV-CH distributions of 6 key growth stages of maize were calculated respectively by probability density statistical techniques. These distributions manifested clear variation tendency and distinction degree in CV domain from 80 to 200, which meant that the CH statistical values in this CV domain could be used to quantify and evaluate color differences among various growth stages of maize. Therefore, a continuous maize canopy color model (MCCM) was established based on the statistical results of 6 key growth stages, which described the successive color change of maize canopy in the whole growth stage. During the stage from leaf emergence to development (4 leaves, 9 leaves and 16 leaves stages), the CH values of maize showed a significant decreasing trend, and then the CH values increased gradually in the silk, blister and milk stages. Based on this model and CV-CH distribution, maize canopy segmentation method was further designed for different growth stages and field conditions. By the comparison with other segmentation methods based on color indices, such as color index of vegetation extraction (CIVE), excess green (ExG), excess green-excess red (ExGR), vegetation (VEG) and hue (H), the presented method could effectively improve the canopy segmentation accuracy, and obtain a segmentation accuracy of over 82.6% for maize canopy images in the whole growth stage. At the same time, this model revealed a significant correlation between the CH value and emerged leaf number (ELN) of 2 maize cultivars (i.e. Denghai605 and Nongda108), and the RMSE (root mean square error) values were 1.14 and 1.41 leaves respectively. The experimental results demonstrate that the maize canopy color model can quantitatively describe canopy color variation in different maize stages, and has important application value for the automatic image segmentation of maize canopy, the prediction of growth stages, and the phenotype identification of maize cultivars.