Literature DB >> 19839332

[Diagnoses of rice nitrogen status based on characteristics of scanning leaf].

Jin-Xia Zhu1, Jin-Song Deng, Yuan-Yuan Shi, Zhu-Lu Chen, Ning Han, Ke Wang.   

Abstract

In the present research, the scanner was adopted as the digital image sensor, and a new method to diagnose the status of rice based on image processing technology was established. The main results are as follows: (1) According to the analysis of relations between leaf percentage nitrogen contents and color parameter, the sensitive color parameters were abstracted as B, b, b/(r+g), b/r and b/g. The leaf position (vertical spatial variation) effects on leaf chlorophyll contents were investigated, and the third fully expanded leaf was selected as the diagnosis leaf. (2) Field ground data such as ASD were collected simultaneously. Then study on the relationships between scanned leaf color characteristics and hyperspectral was carried out. The results indicated that the diagnosis of nitrogen status based on the scanned color characteristic is able to partly reflect the hyperspectral properties. (3) The leaf color and shape features were intergrated and the model of diagnosing the status of rice was established with calculated at YIQ color system. The distinct accuracy of nitrogen status was as follows: N0: 74.9%; N1 : 52%; N2 : 84.7%; N3 : 75%. The preliminary study showed that the methodology has been proved successful in this study and provides the potential to monitor nitrogen status in a cost-effective and accurate way based on the scanned digital image. Although, some confusion exists, with rapidly increasing resolution of digital platform and development of digital image technology, it will be more convenient for larger farms that can afford to use mechanized systems for site-specific nutrient management. Moreover, deeper theory research and practice experiment should be needed in the future.

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Year:  2009        PMID: 19839332

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


  2 in total

1.  Optimal Leaf Positions for SPAD Meter Measurement in Rice.

Authors:  Zhaofeng Yuan; Qiang Cao; Ke Zhang; Syed Tahir Ata-Ul-Karim; Yongchao Tian; Yan Zhu; Weixing Cao; Xiaojun Liu
Journal:  Front Plant Sci       Date:  2016-05-26       Impact factor: 5.753

2.  Skewed distribution of leaf color RGB model and application of skewed parameters in leaf color description model.

Authors:  Zhengmeng Chen; Fuzheng Wang; Pei Zhang; Chendan Ke; Yan Zhu; Weixing Cao; Haidong Jiang
Journal:  Plant Methods       Date:  2020-02-26       Impact factor: 4.993

  2 in total

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