Literature DB >> 19839333

[Nitrogen status diagnosis of rice by using a digital camera].

Liang-Liang Jia1, Ming-Sheng Fan, Fu-Suo Zhang, Xin-Ping Chen, Shi-Hua Lü, Yan-Ming Sun.   

Abstract

In the present research, a field experiment with different N application rate was conducted to study the possibility of using visible band color analysis methods to monitor the N status of rice canopy. The Correlations of visible spectrum band color intensity between rice canopy image acquired from a digital camera and conventional nitrogen status diagnosis parameters of leaf SPAD chlorophyll meter readings, total N content, upland biomass and N uptake were studied. The results showed that the red color intensity (R), green color intensity (G) and normalized redness intensity (NRI) have significant inverse linear correlations with the conventional N diagnosis parameters of SPAD readings, total N content, upland biomass and total N uptake. The correlation coefficient values (r) were from -0.561 to -0.714 for red band (R), from -0.452 to -0.505 for green band (G), and from -0.541 to 0.817 for normalized redness intensity (NRI). But the normalized greenness intensity (NGI) showed a significant positive correlation with conventional N parameters and the correlation coefficient values (r) were from 0.505 to 0.559. Compared with SPAD readings, the normalized redness intensity (NRI), with a high r value of 0.541-0.780 with conventional N parameters, could better express the N status of rice. The digital image color analysis method showed the potential of being used in rice N status diagnosis in the future.

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Year:  2009        PMID: 19839333

Source DB:  PubMed          Journal:  Guang Pu Xue Yu Guang Pu Fen Xi        ISSN: 1000-0593            Impact factor:   0.589


  2 in total

1.  Identification of nitrogen, phosphorus, and potassium deficiencies in rice based on static scanning technology and hierarchical identification method.

Authors:  Lisu Chen; Lin Lin; Guangzhe Cai; Yuanyuan Sun; Tao Huang; Ke Wang; Jinsong Deng
Journal:  PLoS One       Date:  2014-11-26       Impact factor: 3.240

2.  Preliminary research on total nitrogen content prediction of sandalwood using the error-in-variable models based on digital image processing.

Authors:  Zhulin Chen; Xuefeng Wang; Huaijing Wang
Journal:  PLoS One       Date:  2018-08-21       Impact factor: 3.240

  2 in total

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