Mitsutaka Kurita

A Machine Vision for Tomato Cluster Harvesting Robot

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.

Identification of Fluorescent Substance in Mandarin Orange Skin for Machine Vision System to Detect Rotten Citrus Fruits

As basic research to develop a machine vision system to detect rotten mandarin orange, the extraction and identification of fluorescent substances contained in rotten parts of mandarin orange were conducted, and the excitation and fluorescence wavelengths of the substance were determined. Although it has been reported that damaged orange fruit skins are often fluoresced by UV light, it was suggested that fluorescent substances exist not only in the rotten parts of skins but also the normal parts of skins from this research. The fluorescent substances were extracted from 1kg of mandarin peel, and NMR analysis and mass spectrometry were conducted. From this experiment, it was found that the fluorescent substance was quite possibly heptamethylflavone and that the excitation and fluorescent wavelengths of one of the substances were 360 to 375nm and 530 to 550nm, respectively.

n End-Effector and Manipulator Control for Tomato Cluster Harvesting Robot

An end-effector and a control method for tomato-cluster harvesting manipulator are proposed in this study. When fruit cluster harvesting is conducted, peduncle direction is necessary to cut, but is not easy to detect because peduncles are often occluded by leaves, stems and fruits. The end-effector needs to grasp the peduncle without its direction information. An end-effector which can surround main stem and can grasp and cut the peduncle by fingers was made as a trial. When a tomato cluster is transported into a container with the manipulator, both its transportation speed and vibration damping are required. Such a control problem is generally called a motion and vibration control (MOVIC). An input shaping method is one of the representative control methods for the MOVIC. It requires accurate natural frequencies of the manipulating target fruit cluster to damp the flexible vibration when the robot is accelerating the target. The tomato clusters, however, have individual variation with natural frequencies; hence, it is not easy to apply the input shaping method directly. To overcome this problem, identification method of natural frequency was combined with the input shaping method in the proposed method. This identification was based on real time sensing data from a machine vision and a force sensor and database of physical properties of the tomato clusters. Usefulness of the proposed method was verified through both numerical simulations and hardware experiments.

Development of a Mobile Grading Machine for Citrus Fruit

A mobile grading machine for citrus fruits has been developed to collect crop information such as fruit yield, diameter, and sugar content of fruits of each tree part. It consists of a mobile mechanism, a differential global positioning system, a fruit conveyer system, a color camera for machine vision, a NIR spectrometer, and a personal computer for control and database. Preliminary field tests were conducted to investigate basic performance of this machine in a mandarin orange orchard. Using the collecting data of fruit yield and diameter and sugar content of fruit, crop information maps of each tree part was made. These maps indicated that each tree part has large variability of yield, size, and sugar content of fruit.

Machine Vision Algorithm for Robots to Harvest Strawberries in Tabletop Culture Greenhouses

A strawberry harvesting robot consisting of a four DOF manipulator, an end-effector with suction pad, a three camera vision system and a rail type traveling device was developed as a trial to conduct experiments in a tabletop culture greenhouse. In order to harvest the strawberries with curved or inclined peduncles, a wrist joint which can rotate 15 degrees to the left or right from its base position was added. On the algorithm side, peduncle inclination angle was measured by the center camera. Harvesting experiments show that it was possible to precisely harvest more than 75% of fruits which were not occluded by other fruits with the developed robot. Experimental data also show that peduncle length, color and inclination pattern change with the seasons. Complex situations often exist in the real field conditions such as limited visibility of back end strawberries, occluded fruits, obstructions and complex peduncle patterns. Further studies are desirable to automate the harvesting task using a robot.

Development of an End-Effector for a Tomato Cluster Harvesting Robot

Abstract An end-effector was developed for a tomato cluster harvesting robot. This end-effector can harvest not individual fruits but a whole fruit cluster to improve the robots harvest efficiency. Experiments for harvesting tomato clusters were conducted in a high-density plant training system. According to a harvesting algorithm, the end-effector was able to perform well, even when target peduncle orientations were not given. Although the success rate of harvesting tomato clusters was 50 %, it is considered that this rate would improve if an end-effector is used for the high-wire tomato plant training systems in Dutch systems where the node lengths of plants are long enough to loosely hold the main stems.

A Machine Vision System for Tomato Cluster Harvesting Robot

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.

Strawberry harvesting robot for fruits grown on table top culture

We have been developing strawberry harvesting robot for harvesting in a table top culture greenhouse. A strawberry harvesting robot consisted of a 3 DOF manipulator, an end-effecter, machine vision system and a traveling device. An end-effecter, which consisted of a suction head connected to a blower and tow fingers could hold on fruit by suction head and cut and grasp peduncle by two fingers rotate according to peduncle inclination. Machine vision system, which consisted of three identical color cameras (stereo vision system and center camera) and five LED lighting device could calculate fruit’s location of 3 dimension and recognize target fruit and peduncle details. From harvesting experiments at May 2007, it was observed that 38% of fruits were harvested. Corresponding problems were miss-stereo matching and false recognition of fruit and peduncle.

Machine Vision System for Detecting Fluorescent Area of Citrus Using Fluorescence Image

Abstract This research is carried out to develop a machine vision system which could identify the fluorescence area on injured or defective citrus surfaces. The target objects whose surfaces were injured by needle insertions were acquired by a camera VGA using UV lamps (radiating Blacklight and UV-B wave-lengths) and white LEDs. Because damaged citrus peel includes fluorescent substances, it was easy to discriminate fluorescence parts from healthy parts. The results showed that the blacklighting system is practical and feasible, and that the proposed algorithm of fluorescence area detection is effective for some varieties of citrus.

Path Planning of Tomato Cluster Harvesting Robot for Realizing Low Vibration and Speedy Transportation

Abstract A manipulator control method was developed to realize the harvesting of operation for tomato clusters at high speeds. In robot harvesting, both speedy transportation and vibration damping are required when a tomato cluster is transported into a box or a basket by using a manipulator. An input shaping method (ISM) is one of the typical control methods for such control problems. Although the ISM requires accurate natural frequencies of the controlled object, the natural frequencies are different for each tomato cluster. Then, the identification of the natural frequency was combined with the ISM in our method. This identification is based on the data obtained in real time using a machine vision system, a force sensor, and a database on the physical properties of tomato clusters. A numerical simulation study and experiments were conducted and it was verified that the proposed method was applicable to the tomato harvesting robot motion.