Can machine learning classification methods improve the prediction of leaf wetness in North-Western Europe compared to established empirical methods?
Authors:
- Gary D. GillespieGGGary D. Gillespie
- Kevin P. McDonnellKMKevin P. McDonnell
- Gregory M.P. O'HAreGOGregory M.P. O'HAre
Open Access
- 1 November 2021
- journal article
- research article
- Published by Elsevier in Expert Systems with Applications
- Vol. 182
Abstract
No abstract availableKeywords
Funding Information
- Science Foundation Ireland (16/SPP/3296)
This publication has 53 references indexed in Scilit:
- Automated topic namingEmpirical Software Engineering, 2012
- Agrometeorology and plant disease management: a happy marriageScientia Agricola, 2008
- The effect of free water in a maize canopy on microwave emission at 1.4GHzAgricultural and Forest Meteorology, 2006
- Model to Enhance Site-Specific Estimation of Leaf Wetness DurationPlant Disease, 2002
- Septoria tritici Blotch Development as Affected by Temperature, Duration of Leaf Wetness, Inoculum Concentration, and HostPlant Disease, 2001
- Estimating wetness duration on maize ears from meteorological observationsCanadian Journal of Soil Science, 1998
- Artificial neural network models of wheat leaf wetnessAgricultural and Forest Meteorology, 1997
- Development and Validation of an Empirical Model to Estimate the Duration of Dew PeriodsPlant Disease, 1994
- Estimating dew duration. II. Utilizing standard weather station dataAgricultural Meteorology, 1981
- SENSING DURATION OF LEAF MOISTURE RETENTION USING ELECTRICAL IMPEDANCE GRIDSCanadian Journal of Plant Science, 1978