Innovative Cucumber Phenotyping: A Smartphone-Based and Data-Labeling-Free Model
Sustaining global food security amid a growing world population demands advanced breeding methods. Phenotyping, which observes and measures physical traits, is a vital component of agricultural research. However, its labor-intensive nature has long hindered progress. In response, we present an effic...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/23/4775 |
| _version_ | 1797400279453270016 |
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| author | Le Quan Nguyen Jihye Shin Sanghuyn Ryu L. Minh Dang Han Yong Park O New Lee Hyeonjoon Moon |
| author_facet | Le Quan Nguyen Jihye Shin Sanghuyn Ryu L. Minh Dang Han Yong Park O New Lee Hyeonjoon Moon |
| author_sort | Le Quan Nguyen |
| collection | DOAJ |
| description | Sustaining global food security amid a growing world population demands advanced breeding methods. Phenotyping, which observes and measures physical traits, is a vital component of agricultural research. However, its labor-intensive nature has long hindered progress. In response, we present an efficient phenotyping platform tailored specifically for cucumbers, harnessing smartphone cameras for both cost-effectiveness and accessibility. We employ state-of-the-art computer vision models for zero-shot cucumber phenotyping and introduce a B-spline curve as a medial axis to enhance measurement accuracy. Our proposed method excels in predicting sample lengths, achieving an impressive mean absolute percentage error (MAPE) of 2.20%, without the need for extensive data labeling or model training. |
| first_indexed | 2024-03-09T01:53:17Z |
| format | Article |
| id | doaj.art-d07096419c5847918dd935e55c986e2d |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T01:53:17Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-d07096419c5847918dd935e55c986e2d2023-12-08T15:13:59ZengMDPI AGElectronics2079-92922023-11-011223477510.3390/electronics12234775Innovative Cucumber Phenotyping: A Smartphone-Based and Data-Labeling-Free ModelLe Quan Nguyen0Jihye Shin1Sanghuyn Ryu2L. Minh Dang3Han Yong Park4O New Lee5Hyeonjoon Moon6Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of KoreaDepartment of Artificial Intelligence, Sejong University, Seoul 05006, Republic of KoreaDepartment of Artificial Intelligence, Sejong University, Seoul 05006, Republic of KoreaDepartment of Information and Communication Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of KoreaDepartment of Bioresource Engineering, Sejong University, Seoul 05006, Republic of KoreaDepartment of Bioresource Engineering, Sejong University, Seoul 05006, Republic of KoreaDepartment of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of KoreaSustaining global food security amid a growing world population demands advanced breeding methods. Phenotyping, which observes and measures physical traits, is a vital component of agricultural research. However, its labor-intensive nature has long hindered progress. In response, we present an efficient phenotyping platform tailored specifically for cucumbers, harnessing smartphone cameras for both cost-effectiveness and accessibility. We employ state-of-the-art computer vision models for zero-shot cucumber phenotyping and introduce a B-spline curve as a medial axis to enhance measurement accuracy. Our proposed method excels in predicting sample lengths, achieving an impressive mean absolute percentage error (MAPE) of 2.20%, without the need for extensive data labeling or model training.https://www.mdpi.com/2079-9292/12/23/4775plant phenotypingcucumbersegmentationzero-shot learningdeep learningtrait |
| spellingShingle | Le Quan Nguyen Jihye Shin Sanghuyn Ryu L. Minh Dang Han Yong Park O New Lee Hyeonjoon Moon Innovative Cucumber Phenotyping: A Smartphone-Based and Data-Labeling-Free Model Electronics plant phenotyping cucumber segmentation zero-shot learning deep learning trait |
| title | Innovative Cucumber Phenotyping: A Smartphone-Based and Data-Labeling-Free Model |
| title_full | Innovative Cucumber Phenotyping: A Smartphone-Based and Data-Labeling-Free Model |
| title_fullStr | Innovative Cucumber Phenotyping: A Smartphone-Based and Data-Labeling-Free Model |
| title_full_unstemmed | Innovative Cucumber Phenotyping: A Smartphone-Based and Data-Labeling-Free Model |
| title_short | Innovative Cucumber Phenotyping: A Smartphone-Based and Data-Labeling-Free Model |
| title_sort | innovative cucumber phenotyping a smartphone based and data labeling free model |
| topic | plant phenotyping cucumber segmentation zero-shot learning deep learning trait |
| url | https://www.mdpi.com/2079-9292/12/23/4775 |
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