Measurement index

Rice yield determination

Matured rice plants in each plot were harvested for threshing and measured for standard yield after drying (water content was 13.5%).

Photographing rice canopy

In the tillering, jointing, heading, filling, late filling, and maturity stages, images of the rice canopy of rice in 2016 and 2017 (Additional file 1: Figs. S1 and S2) were obtained in the field with a Canon IXUS140 digital camera following established methods [34]. Crops were photographed at a vertical height of 1.2 m from the ground (about 1 m from the rice canopy) and at a 60° angle to the ground. A 15 × 5 cm white plastic plate was used as the background for shooting in the camera’s automatic white balance mode. The image resolution was 1280 × 960, and the camera’s image of the ground rice canopy range was approximately 1.2 m × 1 m trapezoids. Digital images were transferred to the computer in JPEG format.

The image was processed using Adobe Photoshop. “Color selection” was used to select the plant canopy part of the digital image (without the interference of the soil or water surface), and then the “histogram procedure” was employed to obtain data. The R, G, and B values of the image were measured, and the corresponding NRI, NGI, and NBI were calculated. The calculation of each normalized value is as follows:

Statistical analysis and model validation

The yield difference between treatments was statistically analyzed in SPSS16.0. Differences were compared with the Duncan method, with differences in N fertilizer application rate and planting density distinguished. When p < 0.05, the difference was significant.

The evaluation model was constructed through linear relationships between grain yield and color parameters through data of 2016. In order to test the reliability and universality of the model, the established models were validated using the data of 2017. The validity of the models was estimated from the statistical values of RMSE (root mean square error), RE (relative error), and RRMSE (relative root mean square error), which were calculated as:

where X₀ and X_S represent measured and predicted values, respectively. The model is available when RRMSE < 25%.