Discovering weather periods and crop properties favorable for coffee rust incidence from feature selection approaches

Coffee Leaf Rust (CLR) is a disease that leads to considerable losses in the worldwide coffee industry; as those that have been reported recently in Colombia and Central America. The early detection of favorable conditions for epidemics could be used to improve decision making for the coffee grower...

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Main Authors:	Lasso, Emmanuel, Corrales, David Camilo, Avelino, Jacques, Virginio Filho, Elias de Melo, Corrales, Juan Carlos
Format:	Artículo
Language:	Inglés
Published:	2021
Subjects:	ROYA DEL CAFE INDUSTRIA CAFETALERA VARIACION CLIMATICA HEMILEIA VASTATRIX ENFERMEDAD DE LAS PLANTAS TEJIDO FOLIAR EPIDEMIA PRODUCCION CAFICULTORES TRABAJADORES AGRICOLAS
Online Access:	https://doi.org/10.1016/j.compag.2020.105640 https://repositorio.catie.ac.cr/handle/11554/10289

id	RepoCATIE10289
record_format	dspace
spelling	RepoCATIE102892022-08-05T18:54:58Z Discovering weather periods and crop properties favorable for coffee rust incidence from feature selection approaches Lasso, Emmanuel Corrales, David Camilo Avelino, Jacques Virginio Filho, Elias de Melo Corrales, Juan Carlos ROYA DEL CAFE INDUSTRIA CAFETALERA VARIACION CLIMATICA HEMILEIA VASTATRIX ENFERMEDAD DE LAS PLANTAS TEJIDO FOLIAR EPIDEMIA PRODUCCION CAFICULTORES TRABAJADORES AGRICOLAS Coffee Leaf Rust (CLR) is a disease that leads to considerable losses in the worldwide coffee industry; as those that have been reported recently in Colombia and Central America. The early detection of favorable conditions for epidemics could be used to improve decision making for the coffee grower and thus reduce the losses due to the disease. Researchers tried to predict the occurrence of the disease earlier through statistical and machine learning models from crop properties, disease indicators and weather conditions. These studies considered the impact of weather variables in a common period for all. Assuming that the dynamics of weather that most impact the development of the disease occur in the same time periods is simplistic. We propose an approach to discover the time period (window) for each weather variables and crop related features that most explain a future observed CLR incidence, in order to obtain a prediction model through machine learning. The selection of the variables more related with coffee rust incidence and rejection of the features with no significant contribution of information in machine learning tasks were approached from Feature Selection methods (Filter, Wrapper, Embedded). In this way, a CLR incidence prediction model based on the features with the greatest impact on the development of the disease was obtained. Moreover, the use of SHapley Additive exPlanations allowed us to identify the impact of features in the model prediction... 2021-02-09T17:51:50Z 2021-02-09T17:51:50Z 2020 Artículo https://doi.org/10.1016/j.compag.2020.105640 https://repositorio.catie.ac.cr/handle/11554/10289 en Computers and Electronics in Agriculture Computers and Electronics in Agriculture, Volume 176, (2020) info:eu-repo/semantics/openAccess application/pdf
institution	Centro Agronómico Tropical de Investigación y Enseñanza
collection	Repositorio CATIE
language	Inglés
topic	ROYA DEL CAFE INDUSTRIA CAFETALERA VARIACION CLIMATICA HEMILEIA VASTATRIX ENFERMEDAD DE LAS PLANTAS TEJIDO FOLIAR EPIDEMIA PRODUCCION CAFICULTORES TRABAJADORES AGRICOLAS
spellingShingle	ROYA DEL CAFE INDUSTRIA CAFETALERA VARIACION CLIMATICA HEMILEIA VASTATRIX ENFERMEDAD DE LAS PLANTAS TEJIDO FOLIAR EPIDEMIA PRODUCCION CAFICULTORES TRABAJADORES AGRICOLAS Lasso, Emmanuel Corrales, David Camilo Avelino, Jacques Virginio Filho, Elias de Melo Corrales, Juan Carlos Discovering weather periods and crop properties favorable for coffee rust incidence from feature selection approaches
description	Coffee Leaf Rust (CLR) is a disease that leads to considerable losses in the worldwide coffee industry; as those that have been reported recently in Colombia and Central America. The early detection of favorable conditions for epidemics could be used to improve decision making for the coffee grower and thus reduce the losses due to the disease. Researchers tried to predict the occurrence of the disease earlier through statistical and machine learning models from crop properties, disease indicators and weather conditions. These studies considered the impact of weather variables in a common period for all. Assuming that the dynamics of weather that most impact the development of the disease occur in the same time periods is simplistic. We propose an approach to discover the time period (window) for each weather variables and crop related features that most explain a future observed CLR incidence, in order to obtain a prediction model through machine learning. The selection of the variables more related with coffee rust incidence and rejection of the features with no significant contribution of information in machine learning tasks were approached from Feature Selection methods (Filter, Wrapper, Embedded). In this way, a CLR incidence prediction model based on the features with the greatest impact on the development of the disease was obtained. Moreover, the use of SHapley Additive exPlanations allowed us to identify the impact of features in the model prediction...
format	Artículo
author	Lasso, Emmanuel Corrales, David Camilo Avelino, Jacques Virginio Filho, Elias de Melo Corrales, Juan Carlos
author_facet	Lasso, Emmanuel Corrales, David Camilo Avelino, Jacques Virginio Filho, Elias de Melo Corrales, Juan Carlos
author_sort	Lasso, Emmanuel
title	Discovering weather periods and crop properties favorable for coffee rust incidence from feature selection approaches
title_short	Discovering weather periods and crop properties favorable for coffee rust incidence from feature selection approaches
title_full	Discovering weather periods and crop properties favorable for coffee rust incidence from feature selection approaches
title_fullStr	Discovering weather periods and crop properties favorable for coffee rust incidence from feature selection approaches
title_full_unstemmed	Discovering weather periods and crop properties favorable for coffee rust incidence from feature selection approaches
title_sort	discovering weather periods and crop properties favorable for coffee rust incidence from feature selection approaches
publishDate	2021
url	https://doi.org/10.1016/j.compag.2020.105640 https://repositorio.catie.ac.cr/handle/11554/10289
work_keys_str_mv	AT lassoemmanuel discoveringweatherperiodsandcroppropertiesfavorableforcoffeerustincidencefromfeatureselectionapproaches AT corralesdavidcamilo discoveringweatherperiodsandcroppropertiesfavorableforcoffeerustincidencefromfeatureselectionapproaches AT avelinojacques discoveringweatherperiodsandcroppropertiesfavorableforcoffeerustincidencefromfeatureselectionapproaches AT virginiofilhoeliasdemelo discoveringweatherperiodsandcroppropertiesfavorableforcoffeerustincidencefromfeatureselectionapproaches AT corralesjuancarlos discoveringweatherperiodsandcroppropertiesfavorableforcoffeerustincidencefromfeatureselectionapproaches
_version_	1808117275737194496

Discovering weather periods and crop properties favorable for coffee rust incidence from feature selection approaches

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