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JRM Vol.30 No.2 pp. 187-197
doi: 10.20965/jrm.2018.p0187
(2018)

Paper:

Image Mosaicing Using Multi-Modal Images for Generation of Tomato Growth State Map

Takuya Fujinaga, Shinsuke Yasukawa, Binghe Li, and Kazuo Ishii

Kyushu Institute of Technology
2-4 Hibikino, Wakamatsu-ku, Fukuoka 808-0196, Japan

Received:
October 3, 2017
Accepted:
March 5, 2018
Published:
April 20, 2018
Keywords:
agriculture robot, tomato harvesting robot, infrared image, depth image, image mosaicing
Abstract
Image Mosaicing Using Multi-Modal Images for Generation of Tomato Growth State Map

The tomato harvesting robot moves on the rail

Due to the aging and decreasing the number of workers in agriculture, the introduction of automation and precision is needed. Focusing on tomatoes, which is one of the major types of vegetables, we are engaged in the research and development of a robot that can harvest the tomatoes and manage the growth state of tomatoes. For the robot to automatically harvest tomatoes, it must be able to automatically detect harvestable tomatoes positions, and plan the harvesting motions. Furthermore, it is necessary to grasp the positions and maturity of tomatoes in the greenhouse, and to estimate their yield and harvesting period so that the robot and workers can manage the tomatoes. The purpose of this study is to generate a tomato growth state map of a cultivation lane, which consists of a row of tomatoes, aimed at achieving the automatic harvesting and the management of tomatoes in a tomato greenhouse equipped with production facilities. Information such as the positions and maturity of the tomatoes is attached to the map. As the first stage, this paper proposes a method of generating a greenhouse map (a wide-area mosaic image of a tomato cultivation lane). Using the infrared image eases a correspondence point problem of feature points when the mosaic image is generated. Distance information is used to eliminate the cultivation lane behind the targeted one as well as the background scenery, allowing the robot to focus on only those tomatoes in the targeted cultivation lane. To verify the validity of the proposed method, 70 images captured in a greenhouse were used to generate a single mosaic image from which tomatoes were detected by visual inspection.

Cite this article as:
T. Fujinaga, S. Yasukawa, B. Li, and K. Ishii, “Image Mosaicing Using Multi-Modal Images for Generation of Tomato Growth State Map,” J. Robot. Mechatron., Vol.30, No.2, pp. 187-197, 2018.
Data files:
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Last updated on Oct. 19, 2018