A Fast and Real‐Time Machine Vision Evaluation System for Size Grading of Agaricus bisporus Based on Video.
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| Title: | A Fast and Real‐Time Machine Vision Evaluation System for Size Grading of Agaricus bisporus Based on Video. |
|---|---|
| Authors: | Shui, Qiyang1,2 (AUTHOR), Miao, Fajun1,2 (AUTHOR), Liu, Senping1,2 (AUTHOR), Cao, Liang3 (AUTHOR) 380854554@163.com, Jiang, Huanyu4 (AUTHOR) hyjiang@zju.edu.cn, Lu, Jinzhu1,5 (AUTHOR) lujingzhu1103@163.com |
| Source: | Food Science & Nutrition. Jan2026, Vol. 14 Issue 1, p1-17. 17p. |
| Subject Terms: | Cultivated mushroom, Industrial efficiency, Computer vision, Video processing, Quality assurance |
| Abstract: | Automatic grading in factory production is a key component for improving production efficiency and ensuring product quality. As one of the most widely consumed edible mushrooms in the world, the cap diameter of Agaricus bisporus is a key factor affecting the market selling price. In addition to the high labor cost, manual grading is subjective and prone to human assessment errors. Therefore, this study proposes a video‐based fast real‐time machine vision evaluation system for size grading of Agaricus bisporus mushrooms. The designed grading system consists of a conveyor belt to achieve transportation of Agaricus bisporus and an overhead color camera to perform size quality assessment. Considering deployment on edge devices, we have optimized the YOLOv5 model for lightweight performance by adopting ShuffleNetV2 as the backbone network, significantly improving the model's lightweight performance and processing speed. At the same time, we investigated the balance between transport speed and recognition accuracy to ensure that the system maintains a high recognition rate while producing efficiently. The results show that the size of the improved model is reduced by 86.1%, FPS is increased by 9.8%, and mAP is 97.1%. The experimental field results show that the grading can still be performed at a high speed with a transmission speed of 130.17 mm/s. The grading speed can reach 1066.67 mushrooms/min with an accuracy of 97.87%, and it also has strong adaptability and stability in low resolution and low light conditions. The video‐based model can detect Agaricus bisporus mushrooms that are always in motion, which is more suitable for practical detection than a single image‐based detection model. [ABSTRACT FROM AUTHOR] |
| Copyright of Food Science & Nutrition is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 191183799 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Fast and Real‐Time Machine Vision Evaluation System for Size Grading of Agaricus bisporus Based on Video. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Shui%2C+Qiyang%22">Shui, Qiyang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Miao%2C+Fajun%22">Miao, Fajun</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Senping%22">Liu, Senping</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cao%2C+Liang%22">Cao, Liang</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> 380854554@163.com</i><br /><searchLink fieldCode="AR" term="%22Jiang%2C+Huanyu%22">Jiang, Huanyu</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> hyjiang@zju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Lu%2C+Jinzhu%22">Lu, Jinzhu</searchLink><relatesTo>1,5</relatesTo> (AUTHOR)<i> lujingzhu1103@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Food+Science+%26+Nutrition%22">Food Science & Nutrition</searchLink>. Jan2026, Vol. 14 Issue 1, p1-17. 17p. – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Cultivated+mushroom%22">Cultivated mushroom</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+efficiency%22">Industrial efficiency</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Video+processing%22">Video processing</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+assurance%22">Quality assurance</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Automatic grading in factory production is a key component for improving production efficiency and ensuring product quality. As one of the most widely consumed edible mushrooms in the world, the cap diameter of Agaricus bisporus is a key factor affecting the market selling price. In addition to the high labor cost, manual grading is subjective and prone to human assessment errors. Therefore, this study proposes a video‐based fast real‐time machine vision evaluation system for size grading of Agaricus bisporus mushrooms. The designed grading system consists of a conveyor belt to achieve transportation of Agaricus bisporus and an overhead color camera to perform size quality assessment. Considering deployment on edge devices, we have optimized the YOLOv5 model for lightweight performance by adopting ShuffleNetV2 as the backbone network, significantly improving the model's lightweight performance and processing speed. At the same time, we investigated the balance between transport speed and recognition accuracy to ensure that the system maintains a high recognition rate while producing efficiently. The results show that the size of the improved model is reduced by 86.1%, FPS is increased by 9.8%, and mAP is 97.1%. The experimental field results show that the grading can still be performed at a high speed with a transmission speed of 130.17 mm/s. The grading speed can reach 1066.67 mushrooms/min with an accuracy of 97.87%, and it also has strong adaptability and stability in low resolution and low light conditions. The video‐based model can detect Agaricus bisporus mushrooms that are always in motion, which is more suitable for practical detection than a single image‐based detection model. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Food Science & Nutrition is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/fsn3.71328 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1 Subjects: – SubjectFull: Cultivated mushroom Type: general – SubjectFull: Industrial efficiency Type: general – SubjectFull: Computer vision Type: general – SubjectFull: Video processing Type: general – SubjectFull: Quality assurance Type: general Titles: – TitleFull: A Fast and Real‐Time Machine Vision Evaluation System for Size Grading of Agaricus bisporus Based on Video. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Shui, Qiyang – PersonEntity: Name: NameFull: Miao, Fajun – PersonEntity: Name: NameFull: Liu, Senping – PersonEntity: Name: NameFull: Cao, Liang – PersonEntity: Name: NameFull: Jiang, Huanyu – PersonEntity: Name: NameFull: Lu, Jinzhu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20487177 Numbering: – Type: volume Value: 14 – Type: issue Value: 1 Titles: – TitleFull: Food Science & Nutrition Type: main |
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