Share Email Print
cover

Journal of Applied Remote Sensing • Open Access • new

Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles
Author(s): Jin Gwan Ha; Hyeonjoon Moon; Jin Tae Kwak; Syed Ibrahim Hassan; Minh Dang; O New Lee; Han Yong Park

Paper Abstract

Recently, unmanned aerial vehicles (UAVs) have gained much attention. In particular, there is a growing interest in utilizing UAVs for agricultural applications such as crop monitoring and management. We propose a computerized system that is capable of detecting Fusarium wilt of radish with high accuracy. The system adopts computer vision and machine learning techniques, including deep learning, to process the images captured by UAVs at low altitudes and to identify the infected radish. The whole radish field is first segmented into three distinctive regions (radish, bare ground, and mulching film) via a softmax classifier and K -means clustering. Then, the identified radish regions are further classified into healthy radish and Fusarium wilt of radish using a deep convolutional neural network (CNN). In identifying radish, bare ground, and mulching film from a radish field, we achieved an accuracy of 97.4 % . In detecting Fusarium wilt of radish, the CNN obtained an accuracy of 93.3%. It also outperformed the standard machine learning algorithm, obtaining 82.9% accuracy. Therefore, UAVs equipped with computational techniques are promising tools for improving the quality and efficiency of agriculture today.

Paper Details

Date Published: 1 December 2017
PDF: 14 pages
J. Appl. Rem. Sens. 11(4) 042621 doi: 10.1117/1.JRS.11.042621
Published in: Journal of Applied Remote Sensing Volume 11, Issue 4
Show Author Affiliations
Jin Gwan Ha, Sejong Univ. (Republic of Korea)
Hyeonjoon Moon, Sejong Univ. (Republic of Korea)
Jin Tae Kwak, Sejong Univ. (Republic of Korea)
Syed Ibrahim Hassan, Sejong Univ. (Republic of Korea)
Minh Dang, Sejong Univ. (Republic of Korea)
O New Lee, Sejong Univ. (Republic of Korea)
Han Yong Park, Sejong Univ. (Republic of Korea)


© SPIE. Terms of Use
Back to Top