A Machine Vision for Tomato Cluster Harvesting Robot

Abstract

Dutch style greenhouses for tomato production are recently becoming popular in many countries while fruit cluster harvesting is also becoming popular in the Netherland and other countries where the Dutch system is introduced due to higher workability and fruit freshness. In the large scale Dutch production system, it is desirable to replace human operations into automated machines. In this paper, a machine vision system for a tomato fruit cluster harvesting robot is described. This machine vision system consisted of two identical color TV cameras (VGA class), four lighting devices with PL filters, and two image capture boards. Two images were acquired at a time and RGB color component images were converted into HSI images. Using colors on the HSI images, main stems, peduncles, and fruits were discriminate and an end-effector grasping point on the main stem was recognized based on physical properties of the tomato plant. Since difficulty to recognize the grasping point depended on exposure of plant parts and on robot access angle, acquired images were classified into three groups; Group A was images in which the fruit cluster, the stem, and the peduncle were isolated from the other plant parts. Group B was images in which they existed with adjacent plant parts. Group C was images in which some of them were occluded. From an experiment, results showed that 73% of grasping points on main stems were successfully recognized excluding Group C which was not able to be recognized also by human eyes.