A machine vision system for tomato cluster harvesting robot

Naoshi Kondo; Kazuya Yamamoto; Hiroshi Shimizu; Koki Yata; Mitsutaka Kurita; Tomoo Shiigi; Mitsuji Monta; Takahisa Nishizu

doi:10.1016/S1881-8366(09)80017-7

A machine vision system for tomato cluster harvesting robot

Naoshi Kondo, Kazuya Yamamoto, Hiroshi Shimizu, Koki Yata, Mitsutaka Kurita, Tomoo Shiigi, Mitsuji Monta, Takahisa Nishizu

Research output: Contribution to journal › Article › peer-review

33 Citations (Scopus)

Abstract

Dutch style greenhouse for tomato production has become popular recently in many countries while cluster tomatoes have gained popularity among consumers.. To improve harvest efficiency of the cluster tomatoes in large scale Dutch production systems, it is desirable to replace manual labor with automated machines. In this paper, a machine vision system developed for autonomous tomato fruit cluster harvesting is described. Since the difficulty of recognizing the grasping point depended on exposure of plant parts and on robot access angle, acquired images were classified into three groups. The research results show a 73% success rate in automatically locating grasping points for the robotic end-effector on main stems of the cluster tomatoes that can be visually identified by human eyes.

Original language	English
Pages (from-to)	60-65
Number of pages	6
Journal	Engineering in Agriculture, Environment and Food
Volume	2
Issue number	2
DOIs	https://doi.org/10.1016/S1881-8366(09)80017-7
Publication status	Published - Jan 1 2009

Keywords

Harvesting robot
Tomato cluster

ASJC Scopus subject areas

Food Science
Chemical Engineering(all)
Industrial and Manufacturing Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/S1881-8366(09)80017-7

Cite this

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abstract = "Dutch style greenhouse for tomato production has become popular recently in many countries while cluster tomatoes have gained popularity among consumers.. To improve harvest efficiency of the cluster tomatoes in large scale Dutch production systems, it is desirable to replace manual labor with automated machines. In this paper, a machine vision system developed for autonomous tomato fruit cluster harvesting is described. Since the difficulty of recognizing the grasping point depended on exposure of plant parts and on robot access angle, acquired images were classified into three groups. The research results show a 73% success rate in automatically locating grasping points for the robotic end-effector on main stems of the cluster tomatoes that can be visually identified by human eyes.",

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AU - Kondo, Naoshi

AU - Yamamoto, Kazuya

AU - Shimizu, Hiroshi

AU - Yata, Koki

AU - Kurita, Mitsutaka

AU - Shiigi, Tomoo

AU - Monta, Mitsuji

AU - Nishizu, Takahisa

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N2 - Dutch style greenhouse for tomato production has become popular recently in many countries while cluster tomatoes have gained popularity among consumers.. To improve harvest efficiency of the cluster tomatoes in large scale Dutch production systems, it is desirable to replace manual labor with automated machines. In this paper, a machine vision system developed for autonomous tomato fruit cluster harvesting is described. Since the difficulty of recognizing the grasping point depended on exposure of plant parts and on robot access angle, acquired images were classified into three groups. The research results show a 73% success rate in automatically locating grasping points for the robotic end-effector on main stems of the cluster tomatoes that can be visually identified by human eyes.

AB - Dutch style greenhouse for tomato production has become popular recently in many countries while cluster tomatoes have gained popularity among consumers.. To improve harvest efficiency of the cluster tomatoes in large scale Dutch production systems, it is desirable to replace manual labor with automated machines. In this paper, a machine vision system developed for autonomous tomato fruit cluster harvesting is described. Since the difficulty of recognizing the grasping point depended on exposure of plant parts and on robot access angle, acquired images were classified into three groups. The research results show a 73% success rate in automatically locating grasping points for the robotic end-effector on main stems of the cluster tomatoes that can be visually identified by human eyes.

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KW - Tomato cluster

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A machine vision system for tomato cluster harvesting robot

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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