Open AccessCNN-Based Terrain Classification with Moisture Content Using RGB-IR Images
Author(s) -
Tomoya Goto,
AUTHOR_ID,
Genya Ishigami
Publication year - 2021
Publication title -
journal of robotics and mechatronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.257
H-Index - 19
eISSN - 1883-8049
pISSN - 0915-3942
DOI - 10.20965/jrm.2021.p1294
Subject(s) - terrain , water content , rgb color model , precision agriculture , computer science , remote sensing , preprocessor , convolutional neural network , artificial intelligence , computer vision , mobile robot , robot , feature extraction , environmental science , engineering , agriculture , geology , geography , geotechnical engineering , cartography , archaeology
Unmanned mobile robots in rough terrains are a key technology for achieving smart agriculture and smart construction. The mobility performance of robots highly depends on the moisture content of soil, and past few studies have focused on terrain classification using moisture content. In this study, we demonstrate a convolutional neural network-based terrain classification method using RGB-infrared (IR) images. The method first classifies soil types and then categorizes the moisture content of the terrain. A three-step image preprocessing for RGB-IR images is also integrated into the method that is applicable to an actual environment. An experimental study of the terrain classification confirmed that the proposed method achieved an accuracy of more than 99% in classifying the soil type. Furthermore, the classification accuracy of the moisture content was approximately 69% for pumice and 100% for dark soil. The proposed method can be useful for different scenarios, such as small-scale agriculture with mobile robots, smart agriculture for monitoring the moisture content, and earthworks in small areas.