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Weather-based forecasting model for the presence of Metisa plana in oil palm plantation using feature selection in artificial neural network

Bagworm is the most important insect defoliator of oil palm. The bagworm larvae scrape off the leaflets’ epidermis while the older larvae chew the leaflets and leaving multiple holes and causes the palm to lose its photosynthetic capability. A bagworm census should be carried out quickly to determin...

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Main Authors: Salim, Muhammad Zafrullah, Mohd Zahir, Mohammad Hilmi, Muharam, Farrah Melissa, Adam, Nur Azura, Omar, Dzolkhifli, Husin, Nor Azura, Syed Ali, Syed Mohd Faizal
Format: Article
Published: Universiti Kebangsaan Malaysia 2022
Online Access:http://psasir.upm.edu.my/id/eprint/102619/
https://ejournal.ukm.my/serangga/article/view/51881
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spelling my.upm.eprints.1026192024-02-14T05:06:34Z http://psasir.upm.edu.my/id/eprint/102619/ Weather-based forecasting model for the presence of Metisa plana in oil palm plantation using feature selection in artificial neural network Salim, Muhammad Zafrullah Mohd Zahir, Mohammad Hilmi Muharam, Farrah Melissa Adam, Nur Azura Omar, Dzolkhifli Husin, Nor Azura Syed Ali, Syed Mohd Faizal Bagworm is the most important insect defoliator of oil palm. The bagworm larvae scrape off the leaflets’ epidermis while the older larvae chew the leaflets and leaving multiple holes and causes the palm to lose its photosynthetic capability. A bagworm census should be carried out quickly to determine the extent of damage. However, the conventional practices are heavily dependent on in-situ data collection, which is destructive, less efficient, laborious, and costly. Recently, many studies have incorporated machine learning analysis such as artificial neural network (ANN) in agricultural fields especially in the development of pest prediction model. Therefore, this study was conducted to develop a weather-based bagworm prediction model using ANN-Feature Selection method. Bagworm censuses were done by identifying Metisa plana’s larval stage 1 (L1) to 7 (L7) from 13 random palms by cutting off frond number 17 biweekly and weather data was recorded by installing weather station in an oil palm plantation belongs to TH Plantation Berhad in Muadzam Shah, Pahang, Malaysia from July 2016 to June 2017. The results revealed that the significant weather parameters were frequent at time-lag 12. All the larval stage prediction models from ANN-Feature Selection were able to produce satisfactory R2 values ranging from 0.526 to 0.995. The best model was the L1 model with R2 value of 0.985 and the accuracy of more than 90%. Universiti Kebangsaan Malaysia 2022 Article NonPeerReviewed Salim, Muhammad Zafrullah and Mohd Zahir, Mohammad Hilmi and Muharam, Farrah Melissa and Adam, Nur Azura and Omar, Dzolkhifli and Husin, Nor Azura and Syed Ali, Syed Mohd Faizal (2022) Weather-based forecasting model for the presence of Metisa plana in oil palm plantation using feature selection in artificial neural network. Serangga, 27 (1). 138 - 151. ISSN 1394-5130 https://ejournal.ukm.my/serangga/article/view/51881
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description Bagworm is the most important insect defoliator of oil palm. The bagworm larvae scrape off the leaflets’ epidermis while the older larvae chew the leaflets and leaving multiple holes and causes the palm to lose its photosynthetic capability. A bagworm census should be carried out quickly to determine the extent of damage. However, the conventional practices are heavily dependent on in-situ data collection, which is destructive, less efficient, laborious, and costly. Recently, many studies have incorporated machine learning analysis such as artificial neural network (ANN) in agricultural fields especially in the development of pest prediction model. Therefore, this study was conducted to develop a weather-based bagworm prediction model using ANN-Feature Selection method. Bagworm censuses were done by identifying Metisa plana’s larval stage 1 (L1) to 7 (L7) from 13 random palms by cutting off frond number 17 biweekly and weather data was recorded by installing weather station in an oil palm plantation belongs to TH Plantation Berhad in Muadzam Shah, Pahang, Malaysia from July 2016 to June 2017. The results revealed that the significant weather parameters were frequent at time-lag 12. All the larval stage prediction models from ANN-Feature Selection were able to produce satisfactory R2 values ranging from 0.526 to 0.995. The best model was the L1 model with R2 value of 0.985 and the accuracy of more than 90%.
format Article
author Salim, Muhammad Zafrullah
Mohd Zahir, Mohammad Hilmi
Muharam, Farrah Melissa
Adam, Nur Azura
Omar, Dzolkhifli
Husin, Nor Azura
Syed Ali, Syed Mohd Faizal
spellingShingle Salim, Muhammad Zafrullah
Mohd Zahir, Mohammad Hilmi
Muharam, Farrah Melissa
Adam, Nur Azura
Omar, Dzolkhifli
Husin, Nor Azura
Syed Ali, Syed Mohd Faizal
Weather-based forecasting model for the presence of Metisa plana in oil palm plantation using feature selection in artificial neural network
author_facet Salim, Muhammad Zafrullah
Mohd Zahir, Mohammad Hilmi
Muharam, Farrah Melissa
Adam, Nur Azura
Omar, Dzolkhifli
Husin, Nor Azura
Syed Ali, Syed Mohd Faizal
author_sort Salim, Muhammad Zafrullah
title Weather-based forecasting model for the presence of Metisa plana in oil palm plantation using feature selection in artificial neural network
title_short Weather-based forecasting model for the presence of Metisa plana in oil palm plantation using feature selection in artificial neural network
title_full Weather-based forecasting model for the presence of Metisa plana in oil palm plantation using feature selection in artificial neural network
title_fullStr Weather-based forecasting model for the presence of Metisa plana in oil palm plantation using feature selection in artificial neural network
title_full_unstemmed Weather-based forecasting model for the presence of Metisa plana in oil palm plantation using feature selection in artificial neural network
title_sort weather-based forecasting model for the presence of metisa plana in oil palm plantation using feature selection in artificial neural network
publisher Universiti Kebangsaan Malaysia
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/102619/
https://ejournal.ukm.my/serangga/article/view/51881
_version_ 1792154410852286464
score 13.211869

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