Cargando…
Importance of Meteorological Parameters and Airborne Conidia to Predict Risk of Alternaria 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...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9500921/ https://www.ncbi.nlm.nih.gov/pubmed/36146412 http://dx.doi.org/10.3390/s22187063 |
_version_ | 1784795342145847296 |
---|---|
author | Meno, Laura Escuredo, Olga Abuley, Isaac Kwesi Seijo, María Carmen |
author_facet | Meno, Laura Escuredo, Olga Abuley, Isaac Kwesi Seijo, María Carmen |
author_sort | Meno, Laura |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9500921 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95009212022-09-24 Importance of Meteorological Parameters and Airborne Conidia to Predict Risk of Alternaria on a Potato Crop Ambient Using Machine Learning Algorithms Meno, Laura Escuredo, Olga Abuley, Isaac Kwesi Seijo, María Carmen Sensors (Basel) Article 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. MDPI 2022-09-18 /pmc/articles/PMC9500921/ /pubmed/36146412 http://dx.doi.org/10.3390/s22187063 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Meno, Laura Escuredo, Olga Abuley, Isaac Kwesi Seijo, María Carmen Importance of Meteorological Parameters and Airborne Conidia to Predict Risk of Alternaria on a Potato Crop Ambient Using Machine Learning Algorithms |
title | Importance of Meteorological Parameters and Airborne Conidia to Predict Risk of Alternaria on a Potato Crop Ambient Using Machine Learning Algorithms |
title_full | Importance of Meteorological Parameters and Airborne Conidia to Predict Risk of Alternaria on a Potato Crop Ambient Using Machine Learning Algorithms |
title_fullStr | Importance of Meteorological Parameters and Airborne Conidia to Predict Risk of Alternaria on a Potato Crop Ambient Using Machine Learning Algorithms |
title_full_unstemmed | Importance of Meteorological Parameters and Airborne Conidia to Predict Risk of Alternaria on a Potato Crop Ambient Using Machine Learning Algorithms |
title_short | Importance of Meteorological Parameters and Airborne Conidia to Predict Risk of Alternaria on a Potato Crop Ambient Using Machine Learning Algorithms |
title_sort | importance of meteorological parameters and airborne conidia to predict risk of alternaria on a potato crop ambient using machine learning algorithms |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9500921/ https://www.ncbi.nlm.nih.gov/pubmed/36146412 http://dx.doi.org/10.3390/s22187063 |
work_keys_str_mv | AT menolaura importanceofmeteorologicalparametersandairborneconidiatopredictriskofalternariaonapotatocropambientusingmachinelearningalgorithms AT escuredoolga importanceofmeteorologicalparametersandairborneconidiatopredictriskofalternariaonapotatocropambientusingmachinelearningalgorithms AT abuleyisaackwesi importanceofmeteorologicalparametersandairborneconidiatopredictriskofalternariaonapotatocropambientusingmachinelearningalgorithms AT seijomariacarmen importanceofmeteorologicalparametersandairborneconidiatopredictriskofalternariaonapotatocropambientusingmachinelearningalgorithms |