Recognition and counting of oil palm tree with deep learning using satellite image
I Nurhabib, Kudang Boro Seminar, Sudradjat Sudradjat
Abstract
Abstract One of the challenges in oil palm plantation is to accurately the number of palm trees in a unit of area. Knowing the number of trees will contribute to better managerial and operational tasks for fertilization. This requires a method that can be used to calculate oil palm accurately in a quick fashion. This study aims to discuss the method of identifying and counting oil palm trees using a deep learning system based on the YOLO algorithm to process the images captured by a satellite from Google Earth. The system prototype has been successfully built and tested to recognize oil palm trees and to count them in a case study area IPB-Cargill Oil Palm Education Garden, commonly called IPB-Cargill Oil Palm Teaching Farm, is in Singasari, Jonggol, Bogor West Java. Based on the experimental results, the used training dataset using YOLO with 2500 step iterations with a loss value of 0.6 obtained an accuracy values of 85.6%, 98.9% for precision, and 86.6% for recall. This shows that the model is adequate to correctly recognize and to count palm tree objects.