| dc.contributor.author |
Chevalier, RF |
en |
| dc.contributor.author |
Hoogenboom, G |
en |
| dc.contributor.author |
McClendon, RW |
en |
| dc.contributor.author |
Paz, JO |
en |
| dc.date.accessioned |
2014-06-06T06:51:36Z |
|
| dc.date.available |
2014-06-06T06:51:36Z |
|
| dc.date.issued |
2012 |
en |
| dc.identifier.issn |
13648152 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1016/j.envsoft.2012.02.010 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/5587 |
|
| dc.subject |
Agriculture |
en |
| dc.subject |
Agrometeorology |
en |
| dc.subject |
Artificial neural network |
en |
| dc.subject |
Decision support |
en |
| dc.subject |
Frost prediction |
en |
| dc.subject |
Fuzzy expert system |
en |
| dc.subject |
Fuzzy logic |
en |
| dc.subject.other |
Agrometeorology |
en |
| dc.subject.other |
Air temperature |
en |
| dc.subject.other |
Decision supports |
en |
| dc.subject.other |
Dewpoint temperature |
en |
| dc.subject.other |
Economic loss |
en |
| dc.subject.other |
Frost damage |
en |
| dc.subject.other |
Frost prediction |
en |
| dc.subject.other |
Fuzzy expert systems |
en |
| dc.subject.other |
Fuzzy logic rules |
en |
| dc.subject.other |
Georgia |
en |
| dc.subject.other |
Horticultural crops |
en |
| dc.subject.other |
Input variables |
en |
| dc.subject.other |
Meteorological condition |
en |
| dc.subject.other |
Web-based interface |
en |
| dc.subject.other |
Wind speed |
en |
| dc.subject.other |
Agriculture |
en |
| dc.subject.other |
Atmospheric temperature |
en |
| dc.subject.other |
Decision support systems |
en |
| dc.subject.other |
Frost protection |
en |
| dc.subject.other |
Fuzzy logic |
en |
| dc.subject.other |
Losses |
en |
| dc.subject.other |
Multimedia systems |
en |
| dc.subject.other |
Neural networks |
en |
| dc.subject.other |
Testbeds |
en |
| dc.subject.other |
Wind effects |
en |
| dc.subject.other |
Expert systems |
en |
| dc.subject.other |
agrometeorology |
en |
| dc.subject.other |
artificial neural network |
en |
| dc.subject.other |
climate prediction |
en |
| dc.subject.other |
damage |
en |
| dc.subject.other |
decision support system |
en |
| dc.subject.other |
dew point |
en |
| dc.subject.other |
expert system |
en |
| dc.subject.other |
freezing |
en |
| dc.subject.other |
frost |
en |
| dc.subject.other |
fuzzy mathematics |
en |
| dc.subject.other |
horticulture |
en |
| dc.subject.other |
risk assessment |
en |
| dc.subject.other |
warning system |
en |
| dc.subject.other |
World Wide Web |
en |
| dc.subject.other |
Georgia |
en |
| dc.subject.other |
United States |
en |
| dc.subject.other |
Prunus persica |
en |
| dc.subject.other |
Vaccinium |
en |
| dc.title |
A web-based fuzzy expert system for frost warnings in horticultural crops |
en |
| heal.type |
journalArticle |
en |
| heal.identifier.primary |
10.1016/j.envsoft.2012.02.010 |
en |
| heal.publicationDate |
2012 |
en |
| heal.abstract |
Frost damage is responsible for more economic losses than any other weather related phenomenon in the United States (USA) and many other regions across the globe. With sufficient warning, producers can minimize the potential damages caused by frost and freeze events. However, the severity of these events is dependent upon several factors including air temperature, dew point temperature, and wind speed. Methods for assessing this risk are not easily quantifiable and require the insight of experts familiar with the process. Georgia's Extreme-weather Neural-network Informed Expert (GENIE) incorporates the knowledge of expert agrometeorologists and additional information on air temperature, dew point temperature, and wind speed into a fuzzy expert system for use by Georgia producers to provide warning levels of frost and freeze for blueberries and peaches. Artificial neural network (ANN) predictions of air temperature and dew point temperature across the state of Georgia for one to 12 h ahead and observed wind speed are used as input variables for this fuzzy expert system. Meteorological conditions were classified into five levels of frost and freeze by the expert agrometeorologists. These expertly classified scenarios were then used to develop fuzzy logic rules and membership functions for GENIE. Additional scenarios were presented to GENIE for evaluation and it classified all scenarios correctly. This tool will be made available to Georgia producers through a web-based interface, which can be found at www.georgiaweather.net. © 2012 Elsevier Ltd. |
en |
| heal.journalName |
Environmental Modelling and Software |
en |
| dc.identifier.volume |
35 |
en |
| dc.identifier.doi |
10.1016/j.envsoft.2012.02.010 |
en |
| dc.identifier.spage |
84 |
en |
| dc.identifier.epage |
91 |
en |