Estimating Oil Palm Tree Yield and Soil Composition Using Multi-Scale CNN and Vegetation Indices - Irwansyah , Edy; Santoso Gunawan, Alexander Agung; Dzikri , Izzi (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Indonesia is one of the countries in the world with the largest oil palm plantations besides Malaysia which is administratively a neighbour. The process of monitoring oil palm plantation is an important part of supporting the sustainability of the industry in a fluctuating market. Currently, monitoring of oil palm plantations is still using conventional methods through surveys at the plantation location which take a long time. The aim of the study was to count the number and assess the health level of oil palm trees and soil composition to assist plantation managers in making decisions to provide maximum economic benefits. By implementing the Multi-Scale CNN model, oil palm trees can be detected faster and accurately, where the combination of land cover and object classification architecture of ResNet-18 can produce accuracy with an average F1-score of 90.25%. Then by developing a prototype that integrates the Multi-Scale CNN model and the vegetation index, the yields and soil composition can be estimated, thus assisting the user in making a quick decision.