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

Plant Methods

Kenji Omasa²  Peihua Shi¹  Dejian Wang³  Yuan Wang³ 

[1] National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, 1 Weigang Road, Nanjing 210095, PR China;Graduate School of Agricultural and Life Sciences, The University of Tokyo, Yayoi 1-1-1, Bunkyo-ku, Tokyo 113-8657, Japan;Institute of Soil Science, Chinese Academy of Sciences, 71 East Beijing Road, Nanjing 210008, PR China

关键词: Rice;    Nitrogen;    Natural light;    Image processing technology;    Leaf color;    Digital still color camera;   

Others  :  1151507
DOI  :  10.1186/1746-4811-10-36

 received in 2014-06-22, accepted in 2014-09-30,  发布年份 2014

【 摘 要 】

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^-1 in 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.

【 授权许可】

   
2014 Wang et al.; licensee BioMed Central Ltd.

【 图 表 】

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