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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.
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
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  Data: 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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  Data: <searchLink fieldCode="AU" term="%22Check+JC%22">Check JC</searchLink>; Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, U.S.A.<br /><searchLink fieldCode="AU" term="%22Bales+S%22">Bales S</searchLink>; Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, U.S.A.<br /><searchLink fieldCode="AU" term="%22Dong+Y%22">Dong Y</searchLink>; Department of Biosystems Engineering, Michigan State University, East Lansing, MI, U.S.A.<br /><searchLink fieldCode="AU" term="%22Smith+DL%22">Smith DL</searchLink>; Department of Plant Pathology, University of Wisconsin, Madison, WI, U.S.A.<br /><searchLink fieldCode="AU" term="%22Webster+RW%22">Webster RW</searchLink>; Department of Plant Pathology, North Dakota State University, Fargo, ND, U.S.A.<br /><searchLink fieldCode="AU" term="%22Willbur+JF%22">Willbur JF</searchLink>; Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, U.S.A.<br /><searchLink fieldCode="AU" term="%22Chilvers+MI%22">Chilvers MI</searchLink>; Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, U.S.A.
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  Data: <searchLink fieldCode="JN" term="%229427222%22">Phytopathology</searchLink> [Phytopathology] 2026 May; Vol. 116 (5), pp. 684-696. <i>Date of Electronic Publication: </i>2026 Apr 20.
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        Value: 10.1094/PHYTO-04-25-0126-R
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      – Code: eng
        Text: English
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      – TitleFull: 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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              Text: 2026 May
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              Y: 2026
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