Computer and Computing Technologies in Agriculture IV: 4th by Laipeng Luo, Ergen Liu (auth.), Daoliang Li, Yande Liu,

By Laipeng Luo, Ergen Liu (auth.), Daoliang Li, Yande Liu, Yingyi Chen (eds.)

This e-book constitutes half IV of the refereed four-volume post-conference court cases of the 4th IFIP TC 12 overseas convention on desktop and Computing applied sciences in Agriculture, CCTA 2010, held in Nanchang, China, in October 2010. The 352 revised papers provided have been conscientiously chosen from a number of submissions. They conceal quite a lot of fascinating theories and purposes of data know-how in agriculture, together with simulation versions and decision-support structures for agricultural creation, agricultural product caliber trying out, traceability and e-commerce know-how, the appliance of knowledge and communique know-how in agriculture, and common details carrier know-how and repair structures improvement in rural areas.

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Extra resources for Computer and Computing Technologies in Agriculture IV: 4th IFIP TC 12 Conference, CCTA 2010, Nanchang, China, October 22-25, 2010, Selected Papers, Part IV

Sample text

The properties of temporal and spatial distributions relationship between leaf nitrogen and grain quality leaf nitrogen and the winter wheat. : The vertical distribution characteristic and spectral response of canopy nitrogen in different layer of winter wheat. : Nitrogen influence on fresh-leaf NIR spectra. : Remote sensing of nitrogen and lignin in mediterranean vegetation from AVIRIS data: decomposing biochemical from structural signals. : A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method.

Spectra of removing the forth leaf layer at different angles for Jingdong8 and Zhongyou9507 wheat  5HIOHFWLRQ 5HIOHFWLRQ Jingdong8                      Zhongyou9507  :DYHOHQJWKQP ,//& ,//& (/& ,//& ,//& ::& ,//& ::&    :DYHOHQJWKQP ,//& ,//& (/&  ,//& Fig. 4. 4% higher at 800-1300nm. 4). 8% higher at 800-1300nm. To further analyze the relation between canopy spectra and leaves, we selected six spectral reflectivities at 450, 550, 670, 980, 1090, 1200nm.

01 probability levels, respectively. ULNC: upper layer nitrogen content; LLNC: lower layer nitrogen content; △LNC: the difference between ULNC and LLNC. LSCP: spectral characteristics of leaf layers. NDVI: normalized difference vegetation index; PRI: pigment ratio index; PPR: plant pigment ratio; OSAVI: optical soil adjusted index; RVI: ratio of vegetation index. -H. Xiao et al. 6659**, n=24 (6) The determinant coefficients, R2 of models (5) and (6) were 18 and 17% higher than those of models (3) and (4), hence, the predicted GPC model of leaf layer spectral was better.

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