Machine Vision Based on Optical Properties of Biomaterials for Fruit Grading System

Kondo, Naoshi

doi:10.2525/ecb.44.151

Cited by 14 publications

(3 citation statements)

References 20 publications

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“…A number of papers report successful results for inspection and grading of fruits and vegetables (Brosnan and Sun, 2002;Chen et al, 2002;Kondo, 2006;Chong, 2008). There are also many reports on machine vision based application for size sorting, color grading, shape classification, and defect detection.…”

Section: Introductionmentioning

confidence: 97%

Extracting External Features of Sweet Peppers Using Machine Vision System on Mobile Fruits Grading Robot

Qiao¹,

Sasao²,

Shibusawa³

et al. 2012

International Journal of Food Engineering

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Algorithms to extract the external features of sweet peppers were developed using the machine vision system. The objectives were to sort color, estimate size, classify shape, detect bruises and predict mass of sweet peppers. A test was performed using 372 samples of sweet pepper variety "TosahikariD". The results showed that the three main unacceptable colors were recognized and their percentages were calculated by the distribution of hue by saturation. The size of each sample was estimated by three parameters: Feret's V, maximum diameter and equivalent diameter, and they ranged from 55.3mm to 116.7 mm, 23.2mm to 61.6mm and 21.8mm to 73.1mm, respectively. Five parameters were selected and a neural network model was developed to classify the shape of sweet peppers. Test results indicated that the agreement rate for Shapes A, B, and total samples was 95.12, 100 and 95.70%, respectively. All 11 samples with bruises were successfully detected. The fruit mass was predicted using projection areas; the determination and correlation coefficient was 0.93 and 0.96, respectively.

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Section: Introductionmentioning

confidence: 97%

Extracting External Features of Sweet Peppers Using Machine Vision System on Mobile Fruits Grading Robot

Qiao¹,

Sasao²,

Shibusawa³

et al. 2012

International Journal of Food Engineering

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“…Studies on strawberry-harvesting robots for soil culture and an elevated substrate culture were started in Japanese universities [12][13][14], while the Bio-oriented Technology Research Advancement Institution (BRAIN) and Shibuya Seiki created a prototype commercial model robot. Moreover, many types of fruits have been graded and packed by robots in packing facilities [15][16][17]. Several grading systems for strawberries have also been designed [18][19][20]; however, strawberries are too delicate to be handled by mechanical hands.…”

Section: Introductionmentioning

confidence: 99%

Automation technologies for strawberry harvesting and packing operations in Japan1

Hayashi

Yamamoto

Tsubota

et al. 2014

JBR

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This paper describes a strawberry-harvesting robot, a packing robot, and a movable bench system. The harvesting and packing operations in strawberry production require harder, more time-consuming work compared to other operations such as transplanting and chemical spraying, making automation of these tasks desirable. Since harvesting and packing operation account for half of total working hours, automation of these tasks are strongly desired. First of all, based on the findings of many studies on strawberry-harvesting robots for soil culture and elevated substrate culture, our institute of the Bio-oriented Technology Research Advancement Institution and Shibuya Seiki developed a commercial model of a strawberry-harvesting robot, which is chiefly composed a cylindrical manipulator, machine vision, an end-effector, and traveling platform. The results showed an average 54.9% harvesting success rate, 8.6 s cycle time of picking operation, and 102.5 m/h work efficiency in hanging-type growing beds in an experimental greenhouse. Secondly, a prototype automatic packing robot consisting of a supply unit and a packing unit was developed. The supply unit picks up strawberries from a harvesting container, and the packing unit sucks each fruit from calyx side and locates its orientation into a tray. Performance testing showed that automatic packing had a task success rate of 97.3%, with a process time per fruit of 7.3 s. Thirdly, a movable bench system was developed, which makes planting beds rotate in longitudinal and lateral ways. This system brought high density production and labour saving operation at a fixed position, such as crop maintenance and harvesting. By setting up the main body of a strawberry-harvesting robot on working space, unmanned operation technique was developed and tested in an experimental greenhouse. Field experiments of these new automation technologies were conducted and gave a potential of practical use.

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“…In the cooperative facilities, fruit reception, preprocessing, grading and sorting, packing, storing, and shipping operations are conducted. Recently, robotics and mechatronics technologies have been developed, while machine vision and near infrared technologies have been also innovated [1]. There have been many reports on grading systems and inspection systems [2][3][4][5] and some of the new technologies are being introduced in the agricultural cooperative facilities.…”

Section: Introductionmentioning

confidence: 99%

Robotization in fruit grading system

Kondo

2008

Sens. & Instrumen. Food Qual.

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A grading robot system, which automatically provides fruits from containers and inspects all sides of the fruits was developed. The robot system consists of two fruit providing robots and a grading robot. Both robots had 3 DOF Cartesian coordinate manipulators, and 12 suction pads as end-effectors for transporting fruits. The grading robot had 12 color TV cameras, and 28 lighting devices as a machine vision system. The grading robot sucked 12 fruits up at a time and 12 bottom images of all the fruits were acquired during the manipulator moving from a halfway stage to carriers on a conveyor line. Before releasing the fruits to the carriers, 4 side images of each fruit were acquired by rotating the suction pads for 270°. The stroke of the manipulators was about 1.2 m and it took about 4.3 s to move back to the initial position that meant that this robot could grade three fruits per second. This grading robot system made following effects: (1) Labor substitution, (2) Objective grading operation without human subjective judgment, (3) Data accumulation on fruit grading for traceability and farming guidance to producer.

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Machine Vision Based on Optical Properties of Biomaterials for Fruit Grading System

Cited by 14 publications

References 20 publications

Extracting External Features of Sweet Peppers Using Machine Vision System on Mobile Fruits Grading Robot

Extracting External Features of Sweet Peppers Using Machine Vision System on Mobile Fruits Grading Robot

Automation technologies for strawberry harvesting and packing operations in Japan1

Robotization in fruit grading system

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