Multi-Crop Sclerotinia sclerotiorum Apothecia Prediction Models for Irrigated Environments are Improved by On-Site Weather Monitoring and Supervised Machine Learning.
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| Title: | Multi-Crop Sclerotinia sclerotiorum Apothecia Prediction Models for Irrigated Environments are Improved by On-Site Weather Monitoring and Supervised Machine Learning. |
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| Authors: | Check JC; Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, U.S.A., Bales S; Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, U.S.A., Dong Y; Department of Biosystems Engineering, Michigan State University, East Lansing, MI, U.S.A., Smith DL; Department of Plant Pathology, University of Wisconsin, Madison, WI, U.S.A., Webster RW; Department of Plant Pathology, North Dakota State University, Fargo, ND, U.S.A., Willbur JF; Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, U.S.A., Chilvers MI; Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, U.S.A. |
| Source: | Phytopathology [Phytopathology] 2026 May; Vol. 116 (5), pp. 684-696. Date of Electronic Publication: 2026 Apr 20. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: American Phytopathological Society] Country of Publication: United States NLM ID: 9427222 Publication Model: Print-Electronic Cited Medium: Print ISSN: 0031-949X (Print) Linking ISSN: 0031949X NLM ISO Abbreviation: Phytopathology Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
| ISSN: | 0031-949X |
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| DOI: | 10.1094/PHYTO-04-25-0126-R |
