Automatic detection of individual oil palm trees from UAV images using HOG features and an SVM classifier

Abstract

Oil palm trees are important economic crops in tropical areas. Accurate knowledge of the number of oil palm trees in a plantation area is important to predict the yield of palm oil, manage the growing situation of the palm trees and maximise their productivity. In this study, we propose a novel automatic method for detection and enumeration of individual oil palm trees using images from unmanned aerial vehicles. This method required three major steps. First, images from unmanned aerial vehicles were classified as vegetation or non-vegetation by the support vector machine classifier. Second, a feature descriptor based on the histogram of oriented gradient was designed for palm trees and used to extract features for machine learning. Finally, a support vector machine classifier was trained and optimised using the histogram of oriented gradient features from positive (i.e., oil palm trees) and negative samples (i.e., objects other than oil palm trees). The trained classifier was then applied to detect individual oil palm trees using adaptive moving windows that allowed it to also return the crown size of each oil palm tree. The method was trained at one site and validated independently at four other sites with different situations. The overall accuracy of palm tree detection was 99.21% at the training site and 99.39%, 99.06%, 99.90% and 94.63% at the four validation sites; the last one was for the most challenging site, in which palm trees were mixed with other trees. These tests confirm the effectiveness of the proposed method. The simplicity and great efficiency of the proposed method allow it to support oil palm tree counting for large areas using imagery from unmanned aerial vehicles.

Assessment of Tree Detection Methods in Multispectral Aerial Images

“…These features are fed to a Support Vector Machine (SVM) with a Radial Basis Function (RBF) to classify the tree species. Similarly, Wang et al [16] first separated images between vegetation and non-vegetation with an SVM. After the extraction of HOG, these features were used to train an SVM to detect palms.…”

Oil Palm Detection via Deep Transfer Learning

“…However, sometimes satellite images are too expensive or not available in real-time to analyse these crops. Hence, alternatives making use of UAVs to collect multi-spectral photographs have been investigated and has been shown to provide suitable images to detect palm trees with CNNs [22] and a Support Vector Machines (SVMs) [23].…”

Improved Fast R-CNN with Fusion of Optical and 3D Data for Robust Palm Tree Detection in High Resolution UAV Images

“…Zi Yan Chen and Iman Yi Liao SVM was applied for classification. Similarly, histogram of gradients (HOG) [10], local binary patterns (LBP) [11] and HAAR-like features [12] were used to extract shape and texture features from satellite or UAV images. SVM was utilized in the studies for classification and achieved up to 100% detection accuracy especially in young palm areas.…”