A Research of Construction Method for Autonomous Tomato Harvesting Robot focusing on Harvesting Device and Visual Recognition

Abstract

In this paper we propse a construction method of autonomous tomato harvesting robot. For tomato harvesting we focus to harvesting device and visual recognition method. We propose 2 types of harvesting method; cutting scissor and rotational plucking gripper. For recognition, we compare different 3 types of 3D camera, then we construct tomato fruit recognition method using color detection and sphere shape fitting, and also propose tomato pedicel detection method by robust estimation using direction from center of fruit to pedicel and gradient orientation of pedicel edge. Combining these elements, we construct some autonomous harvesting systems and evaluate harvesting of real tomatoes in the tomato robot competition and the real farming veniel house. According to evaluation results, both cutting and plucking has different advantage and disadvantage, it is important to select appropriate one for breed or individual. Also for recognition, pedicel recognition, which must be broken, increases reliability harvesting.

Development of an Automatic Wood Ear Mushroom Harvesting System Based on the Collaboration of Robotics, VR, and AI Technologies

“…In addition, for harvesting, a cultivation method must be devised such that the fruits can be easily captured by a camera. Yaguchi et al developed an automatic harvesting system for tomatoes [3]. These systems are only applicable to crops with simple and, to an extent, with uniform shapes and rich colors, which are easy-to-extract features using image recognition technology.…”

“…With thousands of units in a single plastic greenhouse, the grower's harvesting operation is demanding, requiring unreasonable postures, in addition to environmental factors such as temperature and humidity. When considering the introduction of automatic harvesting robots in such an environment, self-propelled harvesting robots that traverse the plastic greenhouse are similar to those in [2,3] and cannot be used because of failures in the mechanism and other components.…”

Monitoring the Growth and Yield of Fruit Vegetables in a Greenhouse Using a Three-Dimensional Scanner

“…Hashimoto et al (2012) reported that the change in tomato surface color during maturation could be measured without the influence of solar radiation when color was calibrated between morning and evening [19]. Yaguchi et al (2018) detected tomato fruits using color information from point cloud data acquired by a 3D camera, and reported that tomato fruits were harvested at 10 s per fruit using a harvesting robot equipped with cutting scissors and rotational plucking gripper [20]. Ohmori et al (2015) detected tomato fruits based on the tone, value, and hue of a plant image and successfully harvested 76.9% of fruits using a robot [21].…”