Importance of Meteorological Parameters and Airborne Conidia to Predict Risk of <i>Alternaria</i> on a Potato Crop Ambient Using Machine Learning Algorithms

Importance of Meteorological Parameters and Airborne Conidia to Predict Risk of <i>Alternaria</i> on a Potato Crop Ambient Using Machine Learning Algorithms

Secondary infections of early blight during potato crop season are conditioned by aerial inoculum. However, although aerobiological studies have focused on understanding the key factors that influence the spore concentration in the air, less work has been carried out to predict when critical concent...

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Bibliographic Details
Main Authors: Laura Meno, Olga Escuredo, Isaac Kwesi Abuley, María Carmen Seijo
Format: Article
Language:English
Published: MDPI AG 2022-09-01
Series:Sensors
Subjects:
aerobiology
<i>Solanum tuberosum</i>
early blight
<i>Alternaria</i> spp.
weather factors
machine learning
Online Access:https://www.mdpi.com/1424-8220/22/18/7063

Internet

https://www.mdpi.com/1424-8220/22/18/7063

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