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Estimating rice chlorophyll content and leaf nitrogen concentration with a digital still color camera under natural light

Wang, Yuan, Wang, Dejian, Shi, Peihua, Omasa, Kenji
Plant methods 2014 v.10 no.1 pp. 304
Oryza sativa, chlorophyll, color, computer software, cultivars, developmental stages, humans, image analysis, leaves, lighting, linear models, monitoring, nitrogen, nitrogen content, prediction, regression analysis, rice, solar radiation
BACKGROUND: The color of crop leaves is closely correlated with nitrogen (N) status and can be quantified easily with a digital still color camera and image processing software. The establishment of the relationship between image color indices and N status under natural light is important for crop monitoring and N diagnosis in the field. In our study, a digital still color camera was used to take pictures of the canopies of 6 rice (Oryza sativa L.) cultivars with N treatments ranging from 0 to 315 kg N ha⁻¹in the field under sunny and overcast conditions in 2010 and 2011, respectively. RESULTS: Significant correlations were observed between SPAD readings, leaf N concentration (LNC) and 13 image color indices calculated from digital camera images using three color models: RGB, widely used additive color model; HSV, a cylindrical-coordinate similar to the human perception of colors; and the L * a * b * system of the International Commission on Illumination. Among these color indices, the index b * , which represents the visual perception of yellow-blue chroma, has the closest linear relationship with SPAD reading and LNC. However, the relationships between LNC and color indices were affected by the developmental phase. Linear regression models were used to predict LNC and SPAD from color indices and phasic development. After that, the models were validated with independent data. Generally, acceptable performance and prediction were found between the color index b * , SPAD reading and LNC with different cultivars and sampling dates under different natural light conditions. CONCLUSIONS: Our study showed that digital color image analysis could be a simple method of assessing rice N status under natural light conditions for different cultivars and different developmental stages.