Apple Stem and Calyx Identification with Machine Vision

Yang, Qingsheng

doi:10.1006/jaer.1996.0024

Cited by 43 publications

(10 citation statements)

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“…The simulation results show that high shape classification rates were obtained when the work cells and the centre of enlargement were located in the lower one-third section of the retina and at a point with coordinates (0,0,8), respectively, the value of the enlargement factor was two, and the retina had either 75 or 100 sensory cells. In addition, an increase in the number of work cells from four to eight did not significantly affect the shape classification performance.…”

Section: Discussionmentioning

confidence: 93%

Three-dimensional Shape Recognition using a Charge-Simulation Method to Process Primary Image Features

Kanali

Murase

Honami

1998

Journal of Agricultural Engineering Research

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Section: Discussionmentioning

confidence: 93%

Three-dimensional Shape Recognition using a Charge-Simulation Method to Process Primary Image Features

Kanali

Murase

Honami

1998

Journal of Agricultural Engineering Research

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“…When the 3D shape of the fruit is reconstructed, the problem of mistaking the stem cavity for a blemish disappears. Yang (1996) recovered the 3D shape of apples by projecting structured light on the fruit surface, the shape-from-shading technique to construct the 3D surface of apples from 2D NIR images; they report a 90.1% defect detection at a throughput of 1 fruit per second. Wen and Tao (2000) used a dual-camera system comprised of a visible-NIR camera and a mid-infrared (MIR) camera.…”

Section: External or Surface Defectsmentioning

confidence: 99%

Sensors for product characterization and quality of specialty crops—A review

Ruiz-Altisent

Ruiz-García

Moreda

et al. 2010

Computers and Electronics in Agriculture

192

112

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a b s t r a c tThis review covers developments in non-invasive techniques for quality analysis and inspection of specialty crops, mainly fresh fruits and vegetables, over the past decade up to the year 2010. Presented and discussed in this review are advanced sensing technologies including computer vision, spectroscopy, X-rays, magnetic resonance, mechanical contact, chemical sensing, wireless sensor networks and radiofrequency identification sensors. The current status of different sensing systems is described in the context of commercial application. The review also discusses future research needs and potentials of these sensing technologies. Emphases are placed on those technologies that have been proven effective or have shown great potential for agro-food applications. Despite significant progress in the development of non-invasive techniques for quality assessment of fruits and vegetables, the pace for adoption of these technologies by the specialty crop industry has been slow.

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“…The proposed algorithm was found to be effective in detecting various defects such as bruises, russet, scab, fungi or wounds. In similar studies Yang [94] assessed the feasibility of using computer vision for the identification of apple stems and calyxes which required automatic grading and coring. Back propagation neural networks were used to classify each patch as stem/calyx or patch-like blemish.…”

Section: Leemansmentioning

confidence: 99%

Quality Inspection and Grading of Agricultural and Food Products by Computer Vision-a Review

Narendra¹,

Hareesh²

2010

IJCA

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The paper presents the recent development and application of image analysis and computer vision system in quality evaluation of products in the field of agricultural and food. It is very much essential to through light on basic concepts and technologies associated with computer vision system, a tool used in image analysis and automated sorting and grading is highlighted.In India the ever-increasing population, losses in handling and processing and the increased expectation of food products of high quality and safety standards, there is a need for the growth of accurate, fast and objective quality determination of food and agricultural products. Computer vision is a rapid, economic, consistent and objective inspection technique, which has expanded into many diverse industries. Its speed and accuracy satisfy everincreasing production and quality requirements, hence aiding in the development of totally automated processes. This non-destructive method of inspection has found applications in the agricultural and food industry, including the inspection of quality and grading of fruit and vegetable. It has also been used successfully in the analysis of grain characteristics and in the evaluation of foods such as potato chips, meats, cheese and pizza. This paper reviews the progress of computer vision in the agricultural and food field then explores different possible areas of research having a wider scope to enhance the existing algorithms to meet the today"s challenges.

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Apple Stem and Calyx Identification with Machine Vision

Cited by 43 publications

References 1 publication

Three-dimensional Shape Recognition using a Charge-Simulation Method to Process Primary Image Features

Three-dimensional Shape Recognition using a Charge-Simulation Method to Process Primary Image Features

Sensors for product characterization and quality of specialty crops—A review

Quality Inspection and Grading of Agricultural and Food Products by Computer Vision-a Review

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