[Application of mutual information to variable selection in diagnosis of phosphorus nutrition in rice].
Identifieur interne : 000778 ( Main/Exploration ); précédent : 000777; suivant : 000779[Application of mutual information to variable selection in diagnosis of phosphorus nutrition in rice].
Auteurs : Fen-Fang Lin [République populaire de Chine] ; Xiao-Dong Ding ; Zhi-Peng Fu ; Jin-Song Deng ; Zhang-Quan ShenSource :
- Guang pu xue yu guang pu fen xi = Guang pu [ 1000-0593 ] ; 2009.
English descriptors
- KwdEn :
- MESH :
- chemical , metabolism : Phosphorus.
- chemical : Chlorophyll.
- metabolism : Oryza.
- Linear Models, Models, Theoretical, Neural Networks (Computer), Plant Leaves, Regression Analysis.
Abstract
The present study obtained data of rice canopy spectrum, and P and chlorophyll content at typical growth stages with different rates of P supply by means of solution experiment. The effects of P treatments on leaf P and chlorophyll content were analyzed statistically using LSD's multiple comparison at a probability of 0.05; By mutual information (MI) variable selection procedure, the optimal spectral variables were identified at 536, 630, 1040, 551 and 656 nm, and their corresponding mutual information values were 1.0575, 1.1039, 1.135 3, 1.1417 and 1.1494 respectively; based on these sensitive bands, the built feed-forward artificial neural network model (ANN) had higher precision for P content estimation than the multiple linear regression model (MLR). Its RMSE of cross-validation and R were 0.038 8 and 0.9882, respectively, for the calibration data set, and the RMSE of prediction and R were 0.0505 and 0.9892, respectively, for the test data set. Therefore, it was suggested that MI was encouraged for quantitative prediction of leaf P content in rice with visible/near infrared hyperspectral information without assumption on the relationship between independent and dependent variables. But more work is needed to explain why these bands are sensitive to leaf P content in rice.
PubMed: 19950654
Affiliations:
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Le document en format XML
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type="province">Zhejiang</region></placeName></affiliation></author><author><name sortKey="Ding, Xiao Dong" sort="Ding, Xiao Dong" uniqKey="Ding X" first="Xiao-Dong" last="Ding">Xiao-Dong Ding</name></author><author><name sortKey="Fu, Zhi Peng" sort="Fu, Zhi Peng" uniqKey="Fu Z" first="Zhi-Peng" last="Fu">Zhi-Peng Fu</name></author><author><name sortKey="Deng, Jin Song" sort="Deng, Jin Song" uniqKey="Deng J" first="Jin-Song" last="Deng">Jin-Song Deng</name></author><author><name sortKey="Shen, Zhang Quan" sort="Shen, Zhang Quan" uniqKey="Shen Z" first="Zhang-Quan" last="Shen">Zhang-Quan Shen</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2009">2009</date><idno type="RBID">pubmed:19950654</idno><idno type="pmid">19950654</idno><idno type="wicri:Area/PubMed/Corpus">000283</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000283</idno><idno type="wicri:Area/PubMed/Curation">000283</idno><idno 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Information System Application, Zhejiang University, Hangzhou 310029, China. florence@zju.edu.cn</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Institute of Agriculture Remote Sensing & Information System Application, Zhejiang University, Hangzhou 310029</wicri:regionArea><placeName><settlement type="city">Hangzhou</settlement><region type="province">Zhejiang</region></placeName><orgName type="university">Université de Zhejiang</orgName><placeName><settlement type="city">Hangzhou</settlement><region type="province">Zhejiang</region></placeName></affiliation></author><author><name sortKey="Ding, Xiao Dong" sort="Ding, Xiao Dong" uniqKey="Ding X" first="Xiao-Dong" last="Ding">Xiao-Dong Ding</name></author><author><name sortKey="Fu, Zhi Peng" sort="Fu, Zhi Peng" uniqKey="Fu Z" first="Zhi-Peng" last="Fu">Zhi-Peng Fu</name></author><author><name sortKey="Deng, Jin Song" sort="Deng, Jin Song" uniqKey="Deng J" first="Jin-Song" last="Deng">Jin-Song Deng</name></author><author><name sortKey="Shen, Zhang Quan" sort="Shen, Zhang Quan" uniqKey="Shen Z" first="Zhang-Quan" last="Shen">Zhang-Quan Shen</name></author></analytic><series><title level="j">Guang pu xue yu guang pu fen xi = Guang pu</title><idno type="ISSN">1000-0593</idno><imprint><date when="2009" type="published">2009</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Chlorophyll</term><term>Linear Models</term><term>Models, Theoretical</term><term>Neural Networks (Computer)</term><term>Oryza (metabolism)</term><term>Phosphorus (metabolism)</term><term>Plant Leaves</term><term>Regression Analysis</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Phosphorus</term></keywords><keywords scheme="MESH" type="chemical" xml:lang="en"><term>Chlorophyll</term></keywords><keywords scheme="MESH" qualifier="metabolism" xml:lang="en"><term>Oryza</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Linear Models</term><term>Models, Theoretical</term><term>Neural Networks (Computer)</term><term>Plant Leaves</term><term>Regression Analysis</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The present study obtained data of rice canopy spectrum, and P and chlorophyll content at typical growth stages with different rates of P supply by means of solution experiment. The effects of P treatments on leaf P and chlorophyll content were analyzed statistically using LSD's multiple comparison at a probability of 0.05; By mutual information (MI) variable selection procedure, the optimal spectral variables were identified at 536, 630, 1040, 551 and 656 nm, and their corresponding mutual information values were 1.0575, 1.1039, 1.135 3, 1.1417 and 1.1494 respectively; based on these sensitive bands, the built feed-forward artificial neural network model (ANN) had higher precision for P content estimation than the multiple linear regression model (MLR). Its RMSE of cross-validation and R were 0.038 8 and 0.9882, respectively, for the calibration data set, and the RMSE of prediction and R were 0.0505 and 0.9892, respectively, for the test data set. Therefore, it was suggested that MI was encouraged for quantitative prediction of leaf P content in rice with visible/near infrared hyperspectral information without assumption on the relationship between independent and dependent variables. But more work is needed to explain why these bands are sensitive to leaf P content in rice.</div></front></TEI><affiliations><list><country><li>République populaire de Chine</li></country><region><li>Zhejiang</li></region><settlement><li>Hangzhou</li></settlement><orgName><li>Université de Zhejiang</li></orgName></list><tree><noCountry><name sortKey="Deng, Jin Song" sort="Deng, Jin Song" uniqKey="Deng J" first="Jin-Song" last="Deng">Jin-Song Deng</name><name sortKey="Ding, Xiao Dong" sort="Ding, Xiao Dong" uniqKey="Ding X" first="Xiao-Dong" last="Ding">Xiao-Dong Ding</name><name sortKey="Fu, Zhi Peng" sort="Fu, Zhi Peng" uniqKey="Fu Z" first="Zhi-Peng" last="Fu">Zhi-Peng Fu</name><name sortKey="Shen, Zhang Quan" sort="Shen, Zhang Quan" uniqKey="Shen Z" first="Zhang-Quan" last="Shen">Zhang-Quan Shen</name></noCountry><country name="République populaire de Chine"><region name="Zhejiang"><name sortKey="Lin, Fen Fang" sort="Lin, Fen Fang" uniqKey="Lin F" first="Fen-Fang" last="Lin">Fen-Fang Lin</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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