Academic Journal
Simple and Affordable Vision-Based Detection of Seedling Deficiencies to Relieve Labor Shortages in Small-Scale Cruciferous Nurseries.
| Τίτλος: | Simple and Affordable Vision-Based Detection of Seedling Deficiencies to Relieve Labor Shortages in Small-Scale Cruciferous Nurseries. |
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| Συγγραφείς: | Su, Po-Jui, Chen, Tse-Min, Su, Jung-Jeng |
| Πηγή: | Agriculture; Basel; Nov2025, Vol. 15 Issue 21, p2227, 24p |
| Θεματικοί όροι: | Computer vision, Automation, Inspection & review, Modular design, Seedling quality, Image processing, Labor market, Plant nurseries |
| Περίληψη: | Labor shortages in seedling nurseries, particularly in manual inspection and replanting, hinder operational efficiency despite advancements in automation. This study aims to develop a cost-effective, GPU-free machine vision system to automate the detection of deficient seedlings in plug trays, specifically for small-scale nursery operations. The proposed Deficiency Detection and Replanting Positioning (DDRP) machine integrates low-cost components including an Intel RealSense Depth Camera D435, Raspberry Pi 4B, stepper motors, and a programmable logic controller (PLC). It utilizes OpenCV's Haar cascade algorithm, HSV color space conversion, and Otsu thresholding to enable real-time image processing without GPU acceleration. The proposed Deficiency Detection and Replanting Positioning (DDRP) machine integrates low-cost components including an Intel RealSense Depth Camera D435, Raspberry Pi 4B, stepper motors, and a programmable logic controller (PLC). It utilizes OpenCV's Haar cascade algorithm, HSV color space conversion, and Otsu thresholding to enable real-time image processing without GPU acceleration. Under controlled laboratory conditions, the DDRP-Machine achieved high detection accuracy (96.0–98.7%) and precision rates (82.14–83.78%). Benchmarking against deep-learning models such as YOLOv5x and Mask R-CNN showed comparable performance, while requiring only one-third to one-fifth of the cost and avoiding complex infrastructure. The Batch Detection (BD) mode significantly reduced processing time compared to Continuous Detection (CD), enhancing real-time applicability. The DDRP-Machine demonstrates strong potential to improve seedling inspection efficiency and reduce labor dependency in nursery operations. Its modular design and minimal hardware requirements make it a practical and scalable solution for resource-limited environments. This study offers a viable pathway for small-scale farms to adopt intelligent automation without the financial burden of high-end AI systems. Future enhancements, adaptive lighting and self-learning capabilities, will further improve field robustness and including broaden its applicability across diverse nursery conditions. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=20770472&ISBN=&volume=15&issue=21&date=20251101&spage=2227&pages=2227-2250&title=Agriculture; Basel&atitle=Simple%20and%20Affordable%20Vision-Based%20Detection%20of%20Seedling%20Deficiencies%20to%20Relieve%20Labor%20Shortages%20in%20Small-Scale%20Cruciferous%20Nurseries.&aulast=Su%2C%20Po-Jui&id=DOI:10.3390/agriculture15212227 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: Simple and Affordable Vision-Based Detection of Seedling Deficiencies to Relieve Labor Shortages in Small-Scale Cruciferous Nurseries. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Su%2C+Po-Jui%22">Su, Po-Jui</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Tse-Min%22">Chen, Tse-Min</searchLink><br /><searchLink fieldCode="AR" term="%22Su%2C+Jung-Jeng%22">Su, Jung-Jeng</searchLink> – Name: TitleSource Label: Source Group: Src Data: Agriculture; Basel; Nov2025, Vol. 15 Issue 21, p2227, 24p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Inspection+%26+review%22">Inspection & review</searchLink><br /><searchLink fieldCode="DE" term="%22Modular+design%22">Modular design</searchLink><br /><searchLink fieldCode="DE" term="%22Seedling+quality%22">Seedling quality</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Labor+market%22">Labor market</searchLink><br /><searchLink fieldCode="DE" term="%22Plant+nurseries%22">Plant nurseries</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Labor shortages in seedling nurseries, particularly in manual inspection and replanting, hinder operational efficiency despite advancements in automation. This study aims to develop a cost-effective, GPU-free machine vision system to automate the detection of deficient seedlings in plug trays, specifically for small-scale nursery operations. The proposed Deficiency Detection and Replanting Positioning (DDRP) machine integrates low-cost components including an Intel RealSense Depth Camera D435, Raspberry Pi 4B, stepper motors, and a programmable logic controller (PLC). It utilizes OpenCV's Haar cascade algorithm, HSV color space conversion, and Otsu thresholding to enable real-time image processing without GPU acceleration. The proposed Deficiency Detection and Replanting Positioning (DDRP) machine integrates low-cost components including an Intel RealSense Depth Camera D435, Raspberry Pi 4B, stepper motors, and a programmable logic controller (PLC). It utilizes OpenCV's Haar cascade algorithm, HSV color space conversion, and Otsu thresholding to enable real-time image processing without GPU acceleration. Under controlled laboratory conditions, the DDRP-Machine achieved high detection accuracy (96.0–98.7%) and precision rates (82.14–83.78%). Benchmarking against deep-learning models such as YOLOv5x and Mask R-CNN showed comparable performance, while requiring only one-third to one-fifth of the cost and avoiding complex infrastructure. The Batch Detection (BD) mode significantly reduced processing time compared to Continuous Detection (CD), enhancing real-time applicability. The DDRP-Machine demonstrates strong potential to improve seedling inspection efficiency and reduce labor dependency in nursery operations. Its modular design and minimal hardware requirements make it a practical and scalable solution for resource-limited environments. This study offers a viable pathway for small-scale farms to adopt intelligent automation without the financial burden of high-end AI systems. Future enhancements, adaptive lighting and self-learning capabilities, will further improve field robustness and including broaden its applicability across diverse nursery conditions. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Agriculture; Basel is the property of MDPI 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: 10.3390/agriculture15212227 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 2227 Subjects: – SubjectFull: Computer vision Type: general – SubjectFull: Automation Type: general – SubjectFull: Inspection & review Type: general – SubjectFull: Modular design Type: general – SubjectFull: Seedling quality Type: general – SubjectFull: Image processing Type: general – SubjectFull: Labor market Type: general – SubjectFull: Plant nurseries Type: general Titles: – TitleFull: Simple and Affordable Vision-Based Detection of Seedling Deficiencies to Relieve Labor Shortages in Small-Scale Cruciferous Nurseries. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Su, Po-Jui – PersonEntity: Name: NameFull: Chen, Tse-Min – PersonEntity: Name: NameFull: Su, Jung-Jeng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20770472 Numbering: – Type: volume Value: 15 – Type: issue Value: 21 Titles: – TitleFull: Agriculture; Basel Type: main |
| ResultId | 1 |