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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 deter...

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Main Authors: Mohammad Zafrullah Salim,, Mohammad Hilmi Mohd Zahir,, Farrah Melissa Muharam,, Nur Azura Adam,, Dzolkhifli Omar,, Nor Azura Husin,, Syed Mohd Faizal Syed Ali,
Format: Article
Language:English
Published: Pusat Sistematik Serangga, Universiti Kebangsaan Malaysia 2022
Online Access:http://journalarticle.ukm.my/19370/1/51881-181128-1-PB.pdf
http://journalarticle.ukm.my/19370/
https://ejournal.ukm.my/serangga/issue/view/1475/showToc
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spelling my-ukm.journal.193702022-08-16T01:11:04Z http://journalarticle.ukm.my/19370/ Weather-based forecasting model for the presence of Metisa plana in oil palm plantation using feature selection in artificial neural network Mohammad Zafrullah Salim, Mohammad Hilmi Mohd Zahir, Farrah Melissa Muharam, Nur Azura Adam, Dzolkhifli Omar, Nor Azura Husin, Syed Mohd Faizal Syed Ali, 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 %. Pusat Sistematik Serangga, Universiti Kebangsaan Malaysia 2022 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/19370/1/51881-181128-1-PB.pdf Mohammad Zafrullah Salim, and Mohammad Hilmi Mohd Zahir, and Farrah Melissa Muharam, and Nur Azura Adam, and Dzolkhifli Omar, and Nor Azura Husin, and Syed Mohd Faizal Syed Ali, (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). pp. 138-151. ISSN 1394-5130 https://ejournal.ukm.my/serangga/issue/view/1475/showToc
institution Universiti Kebangsaan Malaysia
building Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
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 Mohammad Zafrullah Salim,
Mohammad Hilmi Mohd Zahir,
Farrah Melissa Muharam,
Nur Azura Adam,
Dzolkhifli Omar,
Nor Azura Husin,
Syed Mohd Faizal Syed Ali,
spellingShingle Mohammad Zafrullah Salim,
Mohammad Hilmi Mohd Zahir,
Farrah Melissa Muharam,
Nur Azura Adam,
Dzolkhifli Omar,
Nor Azura Husin,
Syed Mohd Faizal Syed Ali,
Weather-based forecasting model for the presence of Metisa plana in oil palm plantation using feature selection in artificial neural network
author_facet Mohammad Zafrullah Salim,
Mohammad Hilmi Mohd Zahir,
Farrah Melissa Muharam,
Nur Azura Adam,
Dzolkhifli Omar,
Nor Azura Husin,
Syed Mohd Faizal Syed Ali,
author_sort Mohammad Zafrullah Salim,
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 Pusat Sistematik Serangga, Universiti Kebangsaan Malaysia
publishDate 2022
url http://journalarticle.ukm.my/19370/1/51881-181128-1-PB.pdf
http://journalarticle.ukm.my/19370/
https://ejournal.ukm.my/serangga/issue/view/1475/showToc
_version_ 1743107991398252544
score 13.211869

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