A probabilistic graphical model for describing the grape berry maturity
•Dynamic Bayesian networks for coupling heterogeneous data and expertise knowledge.•The modeling of grape berry maturity over the time tainted with uncertainty.•Prediction of sugar, acidity and anthocyanin concentrations over the maturity. Grape berry maturation depends on complex and coupled physio...
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| Veröffentlicht in: | Computers and electronics in agriculture 2015-10, Vol.118, p.124-135 |
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| container_title | Computers and electronics in agriculture |
| container_volume | 118 |
| creator | Baudrit, Cédric Perrot, Nathalie Brousset, Jean Marie Abbal, Philippe Guillemin, Hervé Perret, Bruno Goulet, Etienne Guerin, Laurence Barbeau, Gérard Picque, Daniel |
| description | •Dynamic Bayesian networks for coupling heterogeneous data and expertise knowledge.•The modeling of grape berry maturity over the time tainted with uncertainty.•Prediction of sugar, acidity and anthocyanin concentrations over the maturity.
Grape berry maturation depends on complex and coupled physiological and biochemical reactions which are climate dependant. Moreover one experiment represents one year and the climate variability could not be covered exclusively by the experiments. Consequently, harvest mostly relies on expert prediction. A big challenge for the wine industry is nevertheless to be able to anticipate the reactions for sustainability purposes. We propose to implement a robust mathematical model able (1) to capitalize the heterogeneous fragmented available knowledge including data and expertise by means of probabilistic graphical approaches; and (2) to predict sugar, acidity and anthocyanin concentrations over the maturity. |
| doi_str_mv | 10.1016/j.compag.2015.08.019 |
| format | Article |
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Grape berry maturation depends on complex and coupled physiological and biochemical reactions which are climate dependant. Moreover one experiment represents one year and the climate variability could not be covered exclusively by the experiments. Consequently, harvest mostly relies on expert prediction. A big challenge for the wine industry is nevertheless to be able to anticipate the reactions for sustainability purposes. We propose to implement a robust mathematical model able (1) to capitalize the heterogeneous fragmented available knowledge including data and expertise by means of probabilistic graphical approaches; and (2) to predict sugar, acidity and anthocyanin concentrations over the maturity.</description><subject>Agricultural sciences</subject><subject>Berries</subject><subject>Biochemistry</subject><subject>Climate</subject><subject>Dynamic Bayesian networks</subject><subject>Grapes</subject><subject>Knowledge integration</subject><subject>Life Sciences</subject><subject>Mathematical models</subject><subject>Modeling</subject><subject>Probabilistic methods</subject><subject>Probability theory</subject><subject>Sustainability</subject><subject>Uncertainty</subject><subject>Vegetal Biology</subject><subject>Wine</subject><issn>0168-1699</issn><issn>1872-7107</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNp9kDFPwzAQhS0EEqXwDxgywpBgx0mcLEhVBS1SJRaYLftybl0ldbHTSv33uAQxMp3u9L13eo-Qe0YzRln1tM3A9Xu1znLKyozWGWXNBZmwWuSpYFRckknE6pRVTXNNbkLY0rg3tZiQxSzZe6eVtp0Ng4Vk7dV-Y0F1Se9a7BLjfNJiAG-13a2TYYM_CCYavT8lvRoO3g6nW3JlVBfw7ndOyefry8d8ma7eF2_z2SoFLviQckM1QFUgLTDnyhQGgHPDVCl4ZTTTRmuWF5oXeSWgbFpTMl5WvC21hgoMn5LH0XejOrn3tlf-JJ2ycjlbyfMtNsBLTumRRfZhZGPCrwOGQfY2AHad2qE7BMmEoJxVoikjWowoeBeCR_Pnzag8dyy3cuxYnjuWtI6Pmih7HmUYIx8tehnA4g6wtR5hkK2z_xt8A219hv4</recordid><startdate>20151001</startdate><enddate>20151001</enddate><creator>Baudrit, Cédric</creator><creator>Perrot, Nathalie</creator><creator>Brousset, Jean Marie</creator><creator>Abbal, Philippe</creator><creator>Guillemin, Hervé</creator><creator>Perret, Bruno</creator><creator>Goulet, Etienne</creator><creator>Guerin, Laurence</creator><creator>Barbeau, Gérard</creator><creator>Picque, Daniel</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>1XC</scope><orcidid>https://orcid.org/0000-0003-4320-3345</orcidid><orcidid>https://orcid.org/0000-0003-3795-0838</orcidid></search><sort><creationdate>20151001</creationdate><title>A probabilistic graphical model for describing the grape berry maturity</title><author>Baudrit, Cédric ; Perrot, Nathalie ; Brousset, Jean Marie ; Abbal, Philippe ; Guillemin, Hervé ; Perret, Bruno ; Goulet, Etienne ; Guerin, Laurence ; Barbeau, Gérard ; Picque, Daniel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c373t-3f0bcc64e04e23af4fcc33f1a5736fb1bfbb124b34267c59df513563d5bbc6cf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Agricultural sciences</topic><topic>Berries</topic><topic>Biochemistry</topic><topic>Climate</topic><topic>Dynamic Bayesian networks</topic><topic>Grapes</topic><topic>Knowledge integration</topic><topic>Life Sciences</topic><topic>Mathematical models</topic><topic>Modeling</topic><topic>Probabilistic methods</topic><topic>Probability theory</topic><topic>Sustainability</topic><topic>Uncertainty</topic><topic>Vegetal Biology</topic><topic>Wine</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Baudrit, Cédric</creatorcontrib><creatorcontrib>Perrot, Nathalie</creatorcontrib><creatorcontrib>Brousset, Jean Marie</creatorcontrib><creatorcontrib>Abbal, Philippe</creatorcontrib><creatorcontrib>Guillemin, Hervé</creatorcontrib><creatorcontrib>Perret, Bruno</creatorcontrib><creatorcontrib>Goulet, Etienne</creatorcontrib><creatorcontrib>Guerin, Laurence</creatorcontrib><creatorcontrib>Barbeau, Gérard</creatorcontrib><creatorcontrib>Picque, Daniel</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Hyper Article en Ligne (HAL)</collection><jtitle>Computers and electronics in agriculture</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Baudrit, Cédric</au><au>Perrot, Nathalie</au><au>Brousset, Jean Marie</au><au>Abbal, Philippe</au><au>Guillemin, Hervé</au><au>Perret, Bruno</au><au>Goulet, Etienne</au><au>Guerin, Laurence</au><au>Barbeau, Gérard</au><au>Picque, Daniel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A probabilistic graphical model for describing the grape berry maturity</atitle><jtitle>Computers and electronics in agriculture</jtitle><date>2015-10-01</date><risdate>2015</risdate><volume>118</volume><spage>124</spage><epage>135</epage><pages>124-135</pages><issn>0168-1699</issn><eissn>1872-7107</eissn><abstract>•Dynamic Bayesian networks for coupling heterogeneous data and expertise knowledge.•The modeling of grape berry maturity over the time tainted with uncertainty.•Prediction of sugar, acidity and anthocyanin concentrations over the maturity.
Grape berry maturation depends on complex and coupled physiological and biochemical reactions which are climate dependant. Moreover one experiment represents one year and the climate variability could not be covered exclusively by the experiments. Consequently, harvest mostly relies on expert prediction. A big challenge for the wine industry is nevertheless to be able to anticipate the reactions for sustainability purposes. We propose to implement a robust mathematical model able (1) to capitalize the heterogeneous fragmented available knowledge including data and expertise by means of probabilistic graphical approaches; and (2) to predict sugar, acidity and anthocyanin concentrations over the maturity.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.compag.2015.08.019</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0003-4320-3345</orcidid><orcidid>https://orcid.org/0000-0003-3795-0838</orcidid></addata></record> |
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| language | eng |
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| source | Access via ScienceDirect (Elsevier) |
| subjects | Agricultural sciences Berries Biochemistry Climate Dynamic Bayesian networks Grapes Knowledge integration Life Sciences Mathematical models Modeling Probabilistic methods Probability theory Sustainability Uncertainty Vegetal Biology Wine |
| title | A probabilistic graphical model for describing the grape berry maturity |
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