Computer vision recognition of stem and calyx in apples using near-infrared linear-array structured light and 3D reconstruction

Zhang, Baohua; Huang, Wenqian; Wang, Chaopeng; Gong, Liang; Zhao, Chunjiang; Liu, Chengliang; Huang, Danfeng

doi:10.1016/j.biosystemseng.2015.07.011

Cited by 34 publications

(20 citation statements)

References 20 publications

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“…Yin et al (2009a,b) used a laser active visual sensor to measure the turned angles of robot arm and distance between target tomato and end-effector. Zhang et al (2015) developed a novel apple stem recognition system using the 3D reconstruction technique combined with near-infrared and linear-array structured lighting. The author reported that 97.5% overall recognition accuracy for the 100 samples was obtained by the proposed system and method.…”

Section: Laser Active Visual Schemementioning

confidence: 99%

A review of key techniques of vision-based control for harvesting robot

Zhao

Gong

Huang

et al. 2016

Computers and Electronics in Agriculture

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301

126

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Section: Laser Active Visual Schemementioning

confidence: 99%

A review of key techniques of vision-based control for harvesting robot

Zhao

Gong

Huang

et al. 2016

Computers and Electronics in Agriculture

Self Cite

301

126

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“…1(a). Thevision system used in our study issameas the system described in [2]. It mainly consists of a 2CCD camera (JAI AD-080GE), a near-infrared linear-array structured lighting (800nm, 200mw), a lighting system (LED light), a computer, a conveyor belt driven by a stepper motor.…”

Section: 2computer Vision Systemmentioning

confidence: 99%

“…Consumers care more and more information about the products they bought, and wellinformed high-quality products are easier to arouse consumers' interest and stimulate their purchasing desire [2]. For fruits and vegetables, appearance is one of the most important sensory quality attributes, it would not only influences the packaging and retail price, but also affects consumers' preferences and choice.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of an Automatic Grading System of Diced Potatoes Based on Machine Vision

Wang

Huang

Zhang

et al. 2016

Computer and Computing Technologies in Agriculture IX

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Potato is one of the most important crops in the world. In recent years, potato and its processed products have gradually become important trade goods. As an important semi-manufactured product, diced potatoes need to be graded according to their three-dimensional (3D) size and shape before trading. 3D information inspection manually is a time-consuming and labor intensive work. A novel automatic grading system based on computer vision and near-infrared linear-array structured lighting was proposed in this paper. Two-dimensional size and shape information were extracted from RGB images, and height information was measured in NIR images combined with structured lighting. Then, a pair of pseudo-color and gray level height map images fusing with 3D size and shape information was constructed. Finally, diced potatoes were classified into either regular or irregular class according to their 3D information and criteria required by the industry. The grading system and proposed algorithm were testified by a total of 400 diced potatoes with different size and shapes. The test results showed that the detection error was in the range of about 1 mm, and the classification accuracy was 98%. The results indicated that the system and algorithm was efficient and suitable for the 3D characteristic inspection of diced potatoes.

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“…Many studies have developed grading machines based on normal image to detect the physical appearance of apples [1][2][3][4], or applied methods of laser image, hyperspectral image, near-infrared light, Raman spectrum, and weak light to detect the internal quality of the apples [5][6][7][8][9][10][11]. In this paper, a mass detection method based on particle area analysis is proposed, the appearance of apples can be detected to rapidly determine the surface characteristics, like color, texture, and surface defects, since apples from the same region, orchard, and of the same variety have similar internal quality, so as to allow the grading of apples after picking and before storage.…”

Section: Introductionmentioning

confidence: 99%

A Novel Apple Size and Surface Quality Detection and Grading System

Liu¹,

Qiao²,

Shi³

et al. 2019

I2M

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This paper attempts to design an automatic grading system that can accurately and efficiently determine the size and surface quality of apples. For this purpose, apple images were processed by the LabVIEW toolkit (including IMAQ Vision), and the particle area analysis was performed to measure the size and evaluate the surface quality of apples, creating an apple size and surface quality detection and grading system. The proposed system can detect the size and surface defects of each apple, grade the surface quality, and transfer the apple to the storage point for apples in that grade. With clear principles, simple processes and good portability, the system can be applied independently or together with other detection methods to evaluate the internal quality of apples, such as sugar content, maturity and pesticide residue. The research findings make it possible to make a comprehensive evaluation of apple quality.

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Computer vision recognition of stem and calyx in apples using near-infrared linear-array structured light and 3D reconstruction

Cited by 34 publications

References 20 publications

A review of key techniques of vision-based control for harvesting robot

A review of key techniques of vision-based control for harvesting robot

Design and Implementation of an Automatic Grading System of Diced Potatoes Based on Machine Vision

A Novel Apple Size and Surface Quality Detection and Grading System

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