Journal article
Predicting Ascospore Release of Monilinia vaccinii-corymbosi of Blueberry with Machine Learning
Phytopathology, Vol.107(11), pp.1364-1371
11/2017
Handle:
https://hdl.handle.net/2376/112111
PMID: 28696170
Abstract
Mummy berry, caused by Monilinia vaccinii-corymbosi, causes economic losses of highbush blueberry in the U.S. Pacific Northwest (PNW). Apothecia develop from mummified berries overwintering on soil surfaces and produce ascospores that infect tissue emerging from floral and vegetative buds. Disease control currently relies on fungicides applied on a calendar basis rather than inoculum availability. To establish a prediction model for ascospore release, apothecial development was tracked in three fields, one in western Oregon and two in northwestern Washington in 2015 and 2016. Air and soil temperature, precipitation, soil moisture, leaf wetness, relative humidity and solar radiation were monitored using in-field weather stations and Washington State University's AgWeatherNet stations. Four modeling approaches were compared: logistic regression, multivariate adaptive regression splines, artificial neural networks, and random forest. A supervised learning approach was used to train the models on two data sets: training (70%) and testing (30%). The importance of environmental factors was calculated for each model separately. Soil temperature, soil moisture, and solar radiation were identified as the most important factors influencing ascospore release. Random forest models, with 78% accuracy, showed the best performance compared with the other models. Results of this research helps PNW blueberry growers to optimize fungicide use and reduce production costs.
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Details
- Title
- Predicting Ascospore Release of Monilinia vaccinii-corymbosi of Blueberry with Machine Learning
- Creators
- Dalphy O C Harteveld - First and fourth authors: Department of Plant Pathology, Washington State University, Northwestern Research and Extension Center, 16650 State Route 536, Mount Vernon 98273; second author: School of Computer Science & Engineering, University of Washington, Box 352350, Seattle 98195; and third author: Department of Botany and Plant Pathology, Oregon State University, 1089 Cordley Hall, Corvallis 97331Michael R Grant - First and fourth authors: Department of Plant Pathology, Washington State University, Northwestern Research and Extension Center, 16650 State Route 536, Mount Vernon 98273; second author: School of Computer Science & Engineering, University of Washington, Box 352350, Seattle 98195; and third author: Department of Botany and Plant Pathology, Oregon State University, 1089 Cordley Hall, Corvallis 97331Jay W Pscheidt - First and fourth authors: Department of Plant Pathology, Washington State University, Northwestern Research and Extension Center, 16650 State Route 536, Mount Vernon 98273; second author: School of Computer Science & Engineering, University of Washington, Box 352350, Seattle 98195; and third author: Department of Botany and Plant Pathology, Oregon State University, 1089 Cordley Hall, Corvallis 97331Tobin L Peever - First and fourth authors: Department of Plant Pathology, Washington State University, Northwestern Research and Extension Center, 16650 State Route 536, Mount Vernon 98273; second author: School of Computer Science & Engineering, University of Washington, Box 352350, Seattle 98195; and third author: Department of Botany and Plant Pathology, Oregon State University, 1089 Cordley Hall, Corvallis 97331
- Publication Details
- Phytopathology, Vol.107(11), pp.1364-1371
- Academic Unit
- Plant Pathology, Department of
- Publisher
- United States
- Identifiers
- 99900547587501842
- Language
- English
- Resource Type
- Journal article