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Using satellite-measured relative humidity for prediction of Metisa plana’s population in oil palm plantations: A comparative assessment of regression and artificial neural network models

Metisa plana (Walker) is a leaf defoliating pest that is able to cause staggering economical losses to oil palm cultivation. Considering the economic devastation that the pest could bring, an early warning system to predict its outbreak is crucial. The state of art of satellite technologies are now...

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Autores principales: Ruslan, Siti Aisyah, Muharam, Farrah Melissa, Zulkafli, Zed, Omar, Dzolkhifli, Zambri, Muhammad Pilus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6799924/
https://www.ncbi.nlm.nih.gov/pubmed/31626637
http://dx.doi.org/10.1371/journal.pone.0223968
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author Ruslan, Siti Aisyah
Muharam, Farrah Melissa
Zulkafli, Zed
Omar, Dzolkhifli
Zambri, Muhammad Pilus
author_facet Ruslan, Siti Aisyah
Muharam, Farrah Melissa
Zulkafli, Zed
Omar, Dzolkhifli
Zambri, Muhammad Pilus
author_sort Ruslan, Siti Aisyah
collection PubMed
description Metisa plana (Walker) is a leaf defoliating pest that is able to cause staggering economical losses to oil palm cultivation. Considering the economic devastation that the pest could bring, an early warning system to predict its outbreak is crucial. The state of art of satellite technologies are now able to derive environmental factors such as relative humidity (RH) that may influence pest population’s fluctuations in rapid, harmless, and cost-effective manners. This study examined the relationship between the presence of Metisa plana at different time lags and remote sensing (RS) derived RH by using statistical and machine learning approaches. Metisa plana census data of cumulated larvae instar 1, 2, 3, and 4 were collected biweekly in 2014 and 2015 in an oil palm plantation in Muadzam Shah, Pahang, Malaysia. Relative humidity values derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were apportioned to 6 time lags; 1 week (T1), 2 weeks (T2), 3 week (T3), 4 weeks (T4), 5 week (T5) and 6 weeks (T6) and paired with the respective census data. Pearson’s correlation was carried out to analyse the relationship between Metisa plana and RH at different time lags. Regression analyses and artificial neural network (ANN) were also conducted to develop the best prediction model of Metisa plana’s outbreak. The results showed relatively high correlations, positively or negatively, between the presences of Metisa plana with RH ranging from 0.46 to 0.99. ANN was found to be superior to regression models with the adjusted coefficient of determination (R(2)) between the actual and predicted Metisa plana values ranging from 0.06 to 0.57 versus 0.00 to 0.05. The analysis on the best time lags illustrated that the multiple time lags were more influential on the Metisa plana population than the individual time lags. The best Metisa plana prediction model was derived from T1, T2 and T3 multiple time lags modelled using the ANN algorithm with R(2) value of 0.57, errors below 1.14 and accuracies above 93%. Based on the result of this study, the elucidation of Metisa plana’s landscape ecology was possible with the utilization of RH as the predictor variable in consideration of the time lag effects of RH on the pest’s population.
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spelling pubmed-67999242019-10-25 Using satellite-measured relative humidity for prediction of Metisa plana’s population in oil palm plantations: A comparative assessment of regression and artificial neural network models Ruslan, Siti Aisyah Muharam, Farrah Melissa Zulkafli, Zed Omar, Dzolkhifli Zambri, Muhammad Pilus PLoS One Research Article Metisa plana (Walker) is a leaf defoliating pest that is able to cause staggering economical losses to oil palm cultivation. Considering the economic devastation that the pest could bring, an early warning system to predict its outbreak is crucial. The state of art of satellite technologies are now able to derive environmental factors such as relative humidity (RH) that may influence pest population’s fluctuations in rapid, harmless, and cost-effective manners. This study examined the relationship between the presence of Metisa plana at different time lags and remote sensing (RS) derived RH by using statistical and machine learning approaches. Metisa plana census data of cumulated larvae instar 1, 2, 3, and 4 were collected biweekly in 2014 and 2015 in an oil palm plantation in Muadzam Shah, Pahang, Malaysia. Relative humidity values derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were apportioned to 6 time lags; 1 week (T1), 2 weeks (T2), 3 week (T3), 4 weeks (T4), 5 week (T5) and 6 weeks (T6) and paired with the respective census data. Pearson’s correlation was carried out to analyse the relationship between Metisa plana and RH at different time lags. Regression analyses and artificial neural network (ANN) were also conducted to develop the best prediction model of Metisa plana’s outbreak. The results showed relatively high correlations, positively or negatively, between the presences of Metisa plana with RH ranging from 0.46 to 0.99. ANN was found to be superior to regression models with the adjusted coefficient of determination (R(2)) between the actual and predicted Metisa plana values ranging from 0.06 to 0.57 versus 0.00 to 0.05. The analysis on the best time lags illustrated that the multiple time lags were more influential on the Metisa plana population than the individual time lags. The best Metisa plana prediction model was derived from T1, T2 and T3 multiple time lags modelled using the ANN algorithm with R(2) value of 0.57, errors below 1.14 and accuracies above 93%. Based on the result of this study, the elucidation of Metisa plana’s landscape ecology was possible with the utilization of RH as the predictor variable in consideration of the time lag effects of RH on the pest’s population. Public Library of Science 2019-10-18 /pmc/articles/PMC6799924/ /pubmed/31626637 http://dx.doi.org/10.1371/journal.pone.0223968 Text en © 2019 Ruslan et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Ruslan, Siti Aisyah
Muharam, Farrah Melissa
Zulkafli, Zed
Omar, Dzolkhifli
Zambri, Muhammad Pilus
Using satellite-measured relative humidity for prediction of Metisa plana’s population in oil palm plantations: A comparative assessment of regression and artificial neural network models
title Using satellite-measured relative humidity for prediction of Metisa plana’s population in oil palm plantations: A comparative assessment of regression and artificial neural network models
title_full Using satellite-measured relative humidity for prediction of Metisa plana’s population in oil palm plantations: A comparative assessment of regression and artificial neural network models
title_fullStr Using satellite-measured relative humidity for prediction of Metisa plana’s population in oil palm plantations: A comparative assessment of regression and artificial neural network models
title_full_unstemmed Using satellite-measured relative humidity for prediction of Metisa plana’s population in oil palm plantations: A comparative assessment of regression and artificial neural network models
title_short Using satellite-measured relative humidity for prediction of Metisa plana’s population in oil palm plantations: A comparative assessment of regression and artificial neural network models
title_sort using satellite-measured relative humidity for prediction of metisa plana’s population in oil palm plantations: a comparative assessment of regression and artificial neural network models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6799924/
https://www.ncbi.nlm.nih.gov/pubmed/31626637
http://dx.doi.org/10.1371/journal.pone.0223968
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