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

Johari, Siti Nurul Afiah Mohd; Khairunniza-Bejo, Siti; Shariff, Abdul Rashid Mohamed; Husin, Nur Azuan; Masri, Mohamed Mazmira Mohd; Kamarudin, Noorhazwani

doi:10.3390/agriculture13101886

Cited by 5 publications

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Utilizing the Mission Planner program (Version 1.3.76), the flight parameters were configured for all of the flights. The parameters of the flight were the following: flight altitude of 70 m, flight speed of 5 m/s, and image forward and side overlap of 80% [16,17]. This percentage of overlap was chosen in order to minimize potential data loss during the image acquisition.…”

Section: Flight Parametersmentioning

confidence: 99%

Early Detection of Potential Infestation by Capnodis tenebrionis (L.) (Coleoptera: Buprestidae), in Stone and Pome Fruit Orchards, Using Multispectral Data from a UAV

Arapostathi,

Panopoulou,

Antonopoulos

et al. 2023

Agronomy

View full text Add to dashboard Cite

Wood-boring insect pests pose a significant threat to orchards, potentially leading to tree mortality. In the initial stages of infestation, no visible symptoms are apparent, but as infestations progress, rapid and widespread symptoms emerge, resulting in accelerated tree decline. Therefore, the timely detection of early wood-boring insect symptoms is critical for effective pest control, necessitating advanced methods such as remote sensing. In this study, remote sensing is utilized to identify the early symptoms of peach flatheaded root borer (PFRB) infestation in trees. A multispectral sensor attached to a UAV captures aerial imagery data from stone fruit and pome fruit orchards. These data undergo processing in photogrammetric and GIS programs, where NDVI, NDRE, and the tree crown area are computed. On-site observations confirm PFRB infestations. Various machine-learning models, including logistic regression (LR), artificial neural network (NN), random forest (RF), and extreme gradient boosting (XGBoost), are compared using mean NDVI values, mean NDRE values, crown area, mean temperature, and mean relative humidity. Mean NDVI values emerge as the most crucial factor for predicting PFRB infestation across all machine-learning models. The XGBoost model proves the most effective, achieving an accuracy of 0.85, with marginal variations from the other tested models.

show abstract

Section: Flight Parametersmentioning

confidence: 99%