Automatic detection of Ceratocystis wilt in Eucalyptus crops from aerial images
Jefferson R. Souza, Caio C. T. Mendes, Vitor Guizilini, Kelen C. T. Vivaldini, Adimara Colturato, Fabio Ramos, Denis F. Wolf
Abstract
One of the challenges in precision agriculture is the detection of diseased crops in agricultural environments. This paper presents a methodology to detect the Ceratocystis wilt disease in Eucalyptus crops. An unmanned aerial vehicle is used to obtain high-resolution RGB images of a predefined area. The methodology enables the extraction of visual features from image regions and uses several supervised machine learning (ML) techniques to classify regions into three classes: ground, healthy and diseased plants. Several learning techniques were compared using data obtained from a commercial Eucalyptus plantation. Experimental results show that the GP learning model is more reliable than the other learning methods for accurately identifying diseased trees.
BibTeX
@inproceedings{icra2015_automaticdetecti,
title = {Automatic detection of Ceratocystis wilt in Eucalyptus crops from aerial images},
author = {Jefferson R. Souza and Caio C. T. Mendes and Vitor Guizilini and Kelen C. T. Vivaldini and Adimara Colturato and Fabio Ramos and Denis F. Wolf},
booktitle = {ICRA 2015},
year = {2015}
}