JRM Vol.33 No.6 pp. 1294-1302
doi: 10.20965/jrm.2021.p1294


CNN-Based Terrain Classification with Moisture Content Using RGB-IR Images

Tomoya Goto and Genya Ishigami

Keio University
3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8522, Japan

May 20, 2021
October 10, 2021
December 20, 2021
terrain classification, moisture content, machine learning, CNN

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.

Typical examples of RGB-IR images of each soil sample (IC: initial condition)

Typical examples of RGB-IR images of each soil sample (IC: initial condition)

Cite this article as:
T. Goto and G. Ishigami, “CNN-Based Terrain Classification with Moisture Content Using RGB-IR Images,” J. Robot. Mechatron., Vol.33 No.6, pp. 1294-1302, 2021.
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