Estimation of chlorophyll contents in cotton leaves using computer vision based on gray board

Abstract: This paper was an attempt to develop a low-cost quick method that is easy to use to assess the chlorophyll content of cotton plants using color characteristic parameters which was adjusted by a grey board from plant images. The cotton plant images were obtained at different growth periods from different water treatments. Images were adjusted and normalized by a grey board. The second leaf from the top of a cotton stem image was taken by a CMOS digital camera. The camera lens maintained a 90 degree angle with the cotton leaf vertical to shoot. Before taking an image, the location of the camera lens and the cotton leaves was fixed, and the focal length of the lens was fixed. The gray board image was taken first every time. Using the RGB and HSB color system to split the cotton leaf color characteristics, red value (R), green values (G), blue value (B), and Hue (H), saturation (S) and brightness (Br) of the cotton image were obtained through cotton leaf analysis software that was developed by the VB. Chlorophyll content of the cotton leaf was obtained by spectrophotometer determination. The correlation analysis was set up between color characteristics parameters and chlorophyll content. The correlation coefficients between DGCI (dark green color index) or Red-Blue which were not adjusted by grey broad and cotton chlorophyll were 0.8857 or -0.8726, and they were 0.9073 or -0.9016 respectively after correction. The correlation coefficient between parameters and chlorophyll content were improved after grey board adjustment. The result showed that there were a series of color parameters combination obtained from cotton leaf images that were a highly significant linear correlation with chlorophyll content of the cotton leaf in various growth periods. The color characteristic parameters and chlorophyll content in different periods were combined, and the correlation between them was analyzed. DGCI and Red-Blue had the most highly significant linear correlation with cotton leaf chlorophyll content. Comparing the chlorophyll content prediction accuracy of DGCI or Red-Blue before and after the correction, it showed that the parameter DGCI or Red-Blue after adjustment model prediction accuracy is higher than before calibration. The prediction accuracy of DGCI is higher than Red-Blue parameters after calibration. The prediction for DGCI after adjusted was Chl.a+b= 8.3265DGCI-2.0456. Between the predicted values, which were calculated by the equation, and the measured values of chlorophyll, its root mean square errors (RMSE) was 0.1200, and the relative errors (RE %) was 4.71%. The decision coefficient was 0.8812. The prediction accuracy was better. Our results demonstrated that the adjusted DGCI was the best indicator to predict cotton leaf chlorophyll content, and the prediction model was feasible for applying computer vision technology to rapidly predict cotton chlorophyll content.