Estimating light interception using the color attributes of digital images of cotton canopies

文献类型： 外文期刊

第一作者： XUE Hui-yun

作者： XUE Hui-yun;Li Ya-bing;Mao Shu-chun;HAN Ying-chun;LI Ya-bing;WANG Gun-ping;FENG Lu;FAN Zheng-yi;DU Wen-li;YANG Bei-fang;MAO Shu-chun

作者机构：

关键词： canopy light interception (LI);digital image;color attributes;hue

期刊名称：JOURNAL OF INTEGRATIVE AGRICULTURE （ 影响因子：2.848； 五年影响因子：2.979 ）

ISSN： 2095-3119

年卷期： 2017 年 16 卷 7 期

页码：

收录情况： SCI

摘要： Crop growth and yield depend on canopy light interception (LI). To identify a low-cost and relatively efficient index for measuring LI, several color attributes of red-green-blue (RGB), hue-saturation-intensity (HSI), hue-saturation-value (HSV) color models and the component values of color attributes in the RGB color model were investigated using digital images at six cotton plant population densities in 2012-2014. The results showed that the LI values followed downward quadratic curves after planting. The red (R), green (G) and blue (B) values varied greatly over the years, in accordance with Cai's research demonstrating that the RGB model is affected by outside light. Quadratic curves were fit to these color attributes at six plant population densities. Additionally, linear regressions of LI on every color attribute revealed that the hue (H) values in HSI and HSV were significantly linearly correlated with LI with a determination coefficient (R-2)>= 0.89 and a root mean square error (RMSE)=0.05. Thus, the H values in the HSI and HSV models could be used to measure LI, and this hypothesis was validated. The H values are new indexes for quantitatively estimating the LI of heterogeneous crop canopies, which will provide a theoretical basis for optimizing the crop canopy structure. However, further research should be conducted in other crops and under other growing and environmental conditions to verify this finding.

分类号：

  • 相关文献

[1]Nitrogen Status Diagnosis of Rice by Using a Digital Camera. Fan Ming-sheng,Zhang Fu-suo,Chen Xin-ping,Jia Liang-liang,Sun Yan-ming,Lue Shi-hua. 2009

[2]Nitrogen Status Diagnosis and Yield Prediction of Spring Maize after Green Manure Incorporation by Using a Digital Camera. Bai Jin-shun,Cao Wei-dong,Zeng Nao-hua,Xiong Jing,Cao Wei-dong,Katshyoshi, Shimizu,Rui Yu-kui. 2013

[3]Digital image analysis of expansion growth of cultured cotton ovules with fibers and their responses to ABA. Fan, Ling,Lue, Meng,Ni, Zhi-Yong,Hu, Wen-Ran,Wang, Juan.

[4]Diagnosis of Nitrogen Nutrition of Rice Based on Image Processing of Visible Light. Yuan, Yuan,Chen, Lei,Li, Miao,Wu, Na,Wan, Li,Wang, Shimei. 2016

[5]Detection of Wheat Powdery Mildew with Hyperspectral Remote Sensing Technology and Aerial Digital Image. Cheng Dengfa. 2008

[6]The preliminary study of nitrogen in cotton leaf based on artificial neutral network. Li, Xiaozheng,Wang, Keru,Li, Shaokun,Xie, Ruizhi,Gao, Shiju. 2007

[7]Potato Spectrum and the Digital Image Feature Parameters on the Response of the Nitrogen Level and Its Application. Zheng Shun-lin,Wan Nian-xin,Zhao Ting-ting,Yuan Ji-chao,Zheng Shun-lin,Yuan Ji-chao,He Wei,Hu Jian-jun. 2016

作者其他论文 更多>>

微信小程序