Oil palm tree detection and counting in aerial images based on faster R-CNN - Xinni, Liu; Kamarul Hawari, Ghazali; Fengrong, Han; Izzeldin, I. Mohd; Yue, Zhao; Yuanfa, Ji (Springer, Singapore, 2020)
Malaysian oil palm industry has been a great contributor to the country’s creation of job opportunity, foreign exchange earnings and GDP. Information about the amount and the distribution of oil palm trees in a plantation are important for sustainable management. In this paper, we propose an oil palm tree detection and counting method based on the Faster Regions with Convolutional Neural Network algorithm (Faster R-CNN). Experiment on the oil palm tree images collected by a drone shows that the proposed method can effectively detect the oil palm trees and counting its number when the age of the trees in a plantation is different from 2 years old to 8 years old. The proposed approach can be used to predict the scale of the plantation and meets the requirements of real-time detection.