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Predictive zoning of pest and disease infestations in rice field based on uav aerial imagery

Bacterial leaf blight (BLB), bacterial panicle blight (BPB), and stem borer (SB) are serious infestations to the rice crop. Detection is the first essential step for effective management. The objective of the study is to provide a fast and accurate tool in detecting the infestation damages through U...

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Main Authors: Abd. Kharim, Muhammad Nurfaiz, Wayayok, Aimrun, Abdullah, Ahmad Fikri, Mohamed Shariff, Abdul Rashid, Mohd Husin, Ezrin, Mahadi, Muhammad Razif
Format: Article
Published: Elsevier BV 2022
Online Access:http://psasir.upm.edu.my/id/eprint/102838/
https://linkinghub.elsevier.com/retrieve/pii/S1110982322000722
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spelling my.upm.eprints.1028382024-06-30T14:52:28Z http://psasir.upm.edu.my/id/eprint/102838/ Predictive zoning of pest and disease infestations in rice field based on uav aerial imagery Abd. Kharim, Muhammad Nurfaiz Wayayok, Aimrun Abdullah, Ahmad Fikri Mohamed Shariff, Abdul Rashid Mohd Husin, Ezrin Mahadi, Muhammad Razif Bacterial leaf blight (BLB), bacterial panicle blight (BPB), and stem borer (SB) are serious infestations to the rice crop. Detection is the first essential step for effective management. The objective of the study is to provide a fast and accurate tool in detecting the infestation damages through Unmanned Aerial Vehicle (UAV) aerial imagery. System of Rice Intensification (SRI) was implemented and a UAV equipped with a digital multispectral camera was used to capture image of 20 rice plots that were treated with two types of fertilizers (organic and inorganic) in two different treatment rates namely; uniform rate and variable rate. Ground truths of infestation were observed and collected. Geospatial interpolation (kriging), linear regression analysis, and Soil Plant Analysis Development (SPAD) value models were carried out to predict the zones and level of infestation damages in the rice field. Maps showing areas with high, medium, and low counts of infestation damages were prepared using spatial analysis. The results of the relationship indicate that there were a strong correlation and high R2 between SPAD values obtained through the UAV method and infestation counts during the growth stages of 60 Days After Transplanting (DAT), 80 DAT, and 100 DAT. The findings show that the high severity of infestation happened in the plot that used a high amount of fertilizer compared to the plot that supplied with variable rate fertilizer. Infestation maps produced from the UAV aerial image would be an effective tool in detecting the pest and disease in the rice field. Elsevier BV 2022 Article PeerReviewed Abd. Kharim, Muhammad Nurfaiz and Wayayok, Aimrun and Abdullah, Ahmad Fikri and Mohamed Shariff, Abdul Rashid and Mohd Husin, Ezrin and Mahadi, Muhammad Razif (2022) Predictive zoning of pest and disease infestations in rice field based on uav aerial imagery. The Egyptian Journal of Remote Sensing and Space Science, 25 (3). pp. 831-840. ISSN 1110-9823 https://linkinghub.elsevier.com/retrieve/pii/S1110982322000722 10.1016/j.ejrs.2022.08.001
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 Bacterial leaf blight (BLB), bacterial panicle blight (BPB), and stem borer (SB) are serious infestations to the rice crop. Detection is the first essential step for effective management. The objective of the study is to provide a fast and accurate tool in detecting the infestation damages through Unmanned Aerial Vehicle (UAV) aerial imagery. System of Rice Intensification (SRI) was implemented and a UAV equipped with a digital multispectral camera was used to capture image of 20 rice plots that were treated with two types of fertilizers (organic and inorganic) in two different treatment rates namely; uniform rate and variable rate. Ground truths of infestation were observed and collected. Geospatial interpolation (kriging), linear regression analysis, and Soil Plant Analysis Development (SPAD) value models were carried out to predict the zones and level of infestation damages in the rice field. Maps showing areas with high, medium, and low counts of infestation damages were prepared using spatial analysis. The results of the relationship indicate that there were a strong correlation and high R2 between SPAD values obtained through the UAV method and infestation counts during the growth stages of 60 Days After Transplanting (DAT), 80 DAT, and 100 DAT. The findings show that the high severity of infestation happened in the plot that used a high amount of fertilizer compared to the plot that supplied with variable rate fertilizer. Infestation maps produced from the UAV aerial image would be an effective tool in detecting the pest and disease in the rice field.
format Article
author Abd. Kharim, Muhammad Nurfaiz
Wayayok, Aimrun
Abdullah, Ahmad Fikri
Mohamed Shariff, Abdul Rashid
Mohd Husin, Ezrin
Mahadi, Muhammad Razif
spellingShingle Abd. Kharim, Muhammad Nurfaiz
Wayayok, Aimrun
Abdullah, Ahmad Fikri
Mohamed Shariff, Abdul Rashid
Mohd Husin, Ezrin
Mahadi, Muhammad Razif
Predictive zoning of pest and disease infestations in rice field based on uav aerial imagery
author_facet Abd. Kharim, Muhammad Nurfaiz
Wayayok, Aimrun
Abdullah, Ahmad Fikri
Mohamed Shariff, Abdul Rashid
Mohd Husin, Ezrin
Mahadi, Muhammad Razif
author_sort Abd. Kharim, Muhammad Nurfaiz
title Predictive zoning of pest and disease infestations in rice field based on uav aerial imagery
title_short Predictive zoning of pest and disease infestations in rice field based on uav aerial imagery
title_full Predictive zoning of pest and disease infestations in rice field based on uav aerial imagery
title_fullStr Predictive zoning of pest and disease infestations in rice field based on uav aerial imagery
title_full_unstemmed Predictive zoning of pest and disease infestations in rice field based on uav aerial imagery
title_sort predictive zoning of pest and disease infestations in rice field based on uav aerial imagery
publisher Elsevier BV
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/102838/
https://linkinghub.elsevier.com/retrieve/pii/S1110982322000722
_version_ 1804067011532161024
score 13.211869

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