Detection of Bagworm Infestation Area in Oil Palm Plantation Based on UAV Remote Sensing Using Machine Learning Approach

Siti Nurul Afiah Mohd Johari, Siti Khairunniza-Bejo (), Abdul Rashid Mohamed Shariff, Nur Azuan Husin, Mohamed Mazmira Mohd Masri and Noorhazwani Kamarudin
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Siti Nurul Afiah Mohd Johari: Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia
Siti Khairunniza-Bejo: Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia
Abdul Rashid Mohamed Shariff: Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia
Nur Azuan Husin: Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia
Mohamed Mazmira Mohd Masri: Malaysian Palm Oil Board (MPOB), No. 6, Persiaran Institusi, Bandar Baru Bangi, Kajang 43000, Selangor, Malaysia
Noorhazwani Kamarudin: Malaysian Palm Oil Board (MPOB), No. 6, Persiaran Institusi, Bandar Baru Bangi, Kajang 43000, Selangor, Malaysia

Agriculture, 2023, vol. 13, issue 10, 1-21

Abstract: Due to its rapid reproduction rate and brief life cycle, the most well-known oil palm pest, Metisa plana (Lepidoptera: Psychidae), also known as the bagworm, can spread to epidemic proportions. The outbreak can significantly reduce oil palm yield by resulting in 40% crop losses and 10% to 13% leaf defoliation. A manual census was conducted to count the number of pests and determine the category of infestation; however, when covering a large area, it typically takes more time and labour. Therefore, this study used unmanned aerial vehicles (UAVs) as a quick way to detect the severity levels of infestation in oil palm plantations, including healthy (zero), low, mild, and severe infestation using DJI Inspire 2 with Micasense Altum-PT multispectral camera at an altitude of 70 m above ground. Three combinations were created from the most significant vegetation indices: NDVI and NDRE, NDVI and GNDVI, and NDRE and GNDVI. According to the results, the best combination in classifying healthy and low levels was found to be NDVI and GNDVI, with 100% F1 score. In addition, the combination of NDVI and NDRE was found to be the best combination in classifying mild and severe level. The most important vegetation index that could detect every level of infestation was NDVI. Furthermore, Weighted KNN become the best model that constantly gave the best performance in classifying all the infestation levels (F1 score > 99.70%) in all combinations. The suggested technique is crucial for the early phase of severity-level detection and saves time on the preparation and operation of the control measure.

Keywords: multispectral image; bagworm; infestation; vegetation index; unmanned aerial vehicle; machine learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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