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

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.