Remote Sensing for Detection of Ganoderma Disease and Bagworm Infestation in Oil Palm

Authors

  • Izzuddin Mohamad Anuar Agronomy and Geospatial Technology Unit (AGT), Biology and Sustainability Research Division (BSRD), Malaysian Palm Oil Board (MPOB)
  • Hamzah bin Arof Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
  • Nisfariza binti Mohd Nor Department of Geography, Faculty of Social Sciences and Arts, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
  • Zulkifli bin Hashim Agronomy and Geospatial Technology Unit (AGT), Biology and Sustainability Research Division (BSRD), Malaysian Palm Oil Board (MPOB)
  • Idris bin Abu Seman Biology and Sustainability Research Division (BSRD), Malaysian Palm Oil Board (MPOB)
  • Mazmira Mohamed Masri Biology and Sustainability Research Division (BSRD), Malaysian Palm Oil Board (MPOB)
  • Shukri Mohd Ibrahim Plant Pathology and Biosecurity Unit, Biology and Sustainability Research Division (BSRD), Malaysian Palm Oil Board (MPOB)
  • Ewe Hong Tat President, Universiti Tunku Abdul Rahman, Selangor, Malaysia
  • Chia Ming Toh Universiti Tunku Abdul Rahman, Selangor, Malaysia

DOI:

https://doi.org/10.36877/aafrj.a0000189

Abstract

Two major disease and pest in oil palm are Ganoderma disease and bagworm infestation. Ganoderma disease caused by Ganoderma boninense and bagworm infestation caused by Metisa Plana has caused significant loss to oil palm industry. Therefore, early detection and control are important to reduce the losses. This paper reviewed the existing approaches, challenges and future trend of aerial remote sensing technology for Ganoderma disease and bagworm infestation in oil palm. The aerial remote sensing technology comprises of multispectral, hyperspectral camera and radar which have different platform such as satellite, aircraft and Unmanned Aerial Vehicle (UAV). The aerial multispectral and hyperspectral remote sensing analysed spectral signatures from visible and near infrared spectrum range for detection of the disease and pest attacks. Studies showed that satellite-based multispectral remote sensing only provide moderate accuracy (<70%) compared to UAV-based multispectral remote sensing (>80%) for detection of disease and pest infestation. Meanwhile, our study using UAV showed 90% of accuracy for moderate and severe Ganoderma disease detection in oil palm. Meanwhile, application of aerial hyperspectral remote sensing for Ganoderma disease showed potential for early detection of Ganoderma disease in oil palm and also can be used to detect early pest infestation in oil palm based on field spectroscopy results. Other than that, radar remote sensing has also able to differentiate healthy and Ganoderma-infected oil palm and also pest infestation by analysis of radar backscatter image of the foliar, frond and crown of oil palm. The challenges for the implementation of aerial remote sensing technology for disease and pest detection in oil palm is in tackling problems from shadows, mixed-class from single canopy and false-positive classification and also producing equipment at a lower and affordable price and also a user-friendly data analysis system that can be used by the plantations for a fast disease and pest detection works. The introduction of Artificial Intelligence (AI), Machine Deep Learning (MDL), low-cost remote sensing camera and light-weight UAV has opened the opportunity to tackle the challenges. As a conclusion, aerial remote sensing provides better and faster disease and pest infestation detection system compared to ground-based inspection. The advancement of the aerial remote sensing technology can provide more economic and efficient disease and pest infestation detection system for large oil palm plantation areas.

Author Biographies

Izzuddin Mohamad Anuar, Agronomy and Geospatial Technology Unit (AGT), Biology and Sustainability Research Division (BSRD), Malaysian Palm Oil Board (MPOB)

Research Officer,

Geospatial and Precision Agriculture Technology Group,

Agronomy and Geospatial Technology Unit,

Biology and Sustainability Research Division, 

Malaysian Palm Oil Board

Hamzah bin Arof, Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia

Professor,

Department of Electrical Engineering,

Faculty of Engineering,

Universiti Malaya,

50603 Kuala Lumpur, Malaysia

Nisfariza binti Mohd Nor, Department of Geography, Faculty of Social Sciences and Arts, Universiti Malaya, 50603 Kuala Lumpur, Malaysia

Senior Lecturer,

Department of Geography,
Faculty of Social Sciences and Arts,
Universiti Malaya,
50603 Kuala Lumpur, Malaysia

Zulkifli bin Hashim, Agronomy and Geospatial Technology Unit (AGT), Biology and Sustainability Research Division (BSRD), Malaysian Palm Oil Board (MPOB)

Head of Unit,

Agronomy and Geospatial Technology Unit (AGT),

Biology and Sustainability Research Division (BSRD),

Malaysian Palm Oil Board (MPOB)

Idris bin Abu Seman, Biology and Sustainability Research Division (BSRD), Malaysian Palm Oil Board (MPOB)

Director,

Biology and Sustainability Research Division (BSRD),

Malaysian Palm Oil Board (MPOB)

Mazmira Mohamed Masri, Biology and Sustainability Research Division (BSRD), Malaysian Palm Oil Board (MPOB)

Head of Unit

Entomology and Integrated Pest Management,

Biology and Sustainability Research Division (BSRD),

Malaysian Palm Oil Board (MPOB)


Shukri Mohd Ibrahim, Plant Pathology and Biosecurity Unit, Biology and Sustainability Research Division (BSRD), Malaysian Palm Oil Board (MPOB)

Research Officer,

Plant Pathology and Biosecurity Unit,
Biology and Sustainability Research Division (BSRD),
Malaysian Palm Oil Board (MPOB)

Ewe Hong Tat, President, Universiti Tunku Abdul Rahman, Selangor, Malaysia

Professor,

President,

Universiti Tunku Abdul Rahman,
Selangor,
Malaysia

Chia Ming Toh, Universiti Tunku Abdul Rahman, Selangor, Malaysia

Phd Student,

Universiti Tunku Abdul Rahman,
Selangor,
Malaysia

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Published

2021-01-03

How to Cite

Anuar, I. M., Arof, H. bin, Mohd Nor, N. binti, Hashim, Z. bin, Abu Seman, I. bin, Masri, M. M., Ibrahim, S. M., Tat, E. H., & Toh, C. M. (2021). Remote Sensing for Detection of Ganoderma Disease and Bagworm Infestation in Oil Palm. Advances in Agricultural and Food Research Journal, 2(1). https://doi.org/10.36877/aafrj.a0000189

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ORIGINAL RESEARCH ARTICLE
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