Importance of meteorological parameters and airborne conidia to predict risk of alternaria on a potato crop ambient using machine learning algorithms
| Autor: | Seijo-Coello Maria Carmen, Laura Meno Fariñas, Isaac Kwesi Abuley, Olga Escuredo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
aerobiology
decision trees 3108.05 Hongos k-nearest neighbor Alternariaspp early blight Biochemistry 2502 Climatología Atomic and Molecular Physics and Optics weather factors Analytical Chemistry machine learning 3103.01 Producción de Cultivos Electrical and Electronic Engineering Instrumentation random forest Solanum tuberosum Alternaria spp |
| Zdroj: | Sensors; Volume 22; Issue 18; Pages: 7063 Meno, L, Escuredo, O, Abuley, I K & Seijo, M C 2022, ' Importance of Meteorological Parameters and Airborne Conidia to Predict Risk of Alternaria on a Potato Crop Ambient Using Machine Learning Algorithms ', Sensors, vol. 22, no. 18, 7063 . https://doi.org/10.3390/s22187063 |
| DOI: | 10.3390/s22187063 |
| Popis: | 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 concentrations of conidia occur. Therefore, the goals of this study were to understand the key weather variables that affect the hourly and daily conidia dispersal of Alternaria solani and A. alternata in a potato field, and to use these weather factors in different machine learning (ML) algorithms to predict the daily conidia levels. This study showed that conidia per hour in a day is influenced by the weather conditions that characterize the hour, but not the hour of the day. Specifically, the relative humidity and solar radiation were the most relevant weather parameters influencing the conidia concentration in the air and both in a linear model explained 98% of the variation of this concentration per hour. Moreover, the dew point temperature three days before was the weather variable with the strongest effect on conidia per day. An improved prediction of Alternaria conidia level was achieved via ML algorithms when the conidia of previous days is considered in the analysis. Among the ML algorithms applied, the CART model with an accuracy of 86% were the best to predict daily conidia level. Ministerio de Educación, Cultura y Deportes | Ref. FPU 17/00267 |
| Databáze: | OpenAIRE |
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