Vision-Based White Radish Phenotypic Trait Measurement with Smartphone Imagery
White radish is a nutritious and delectable vegetable that is enjoyed globally. Conventional techniques for monitoring radish growth are arduous and time-consuming, encouraging the development of novel methods for quicker measurements and greater sampling density. This research introduces a mathemat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/13/6/1630 |
| _version_ | 1797596483693838336 |
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| author | L. Minh Dang Kyungbok Min Tan N. Nguyen Han Yong Park O New Lee Hyoung-Kyu Song Hyeonjoon Moon |
| author_facet | L. Minh Dang Kyungbok Min Tan N. Nguyen Han Yong Park O New Lee Hyoung-Kyu Song Hyeonjoon Moon |
| author_sort | L. Minh Dang |
| collection | DOAJ |
| description | White radish is a nutritious and delectable vegetable that is enjoyed globally. Conventional techniques for monitoring radish growth are arduous and time-consuming, encouraging the development of novel methods for quicker measurements and greater sampling density. This research introduces a mathematical model working on high-resolution images to measure radish’s biophysical properties automatically. A color calibration was performed on the dataset using a color checker panel to minimize the impact of varying light conditions on the RGB images. Subsequently, a Mask-RCNN model was trained to effectively segment different components of the radishes. The observations of the segmented results included leaf length, leaf width, root width, root length, leaf length to width, root length to width, root shoulder color, and root peel color. The automated real-life measurements of these observations were then conducted and compared with actual results. The validation results, based on a set of white radish samples, demonstrated the models’ effectiveness in utilizing images for quantifying phenotypic traits. The average accuracy of the automated method was confirmed to be 96.2% when compared to the manual method. |
| first_indexed | 2024-03-11T02:52:02Z |
| format | Article |
| id | doaj.art-ac8c3ac144854efa80f30e4c6c89ca52 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-11T02:52:02Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-ac8c3ac144854efa80f30e4c6c89ca522023-11-18T08:55:52ZengMDPI AGAgronomy2073-43952023-06-01136163010.3390/agronomy13061630Vision-Based White Radish Phenotypic Trait Measurement with Smartphone ImageryL. Minh Dang0Kyungbok Min1Tan N. Nguyen2Han Yong Park3O New Lee4Hyoung-Kyu Song5Hyeonjoon Moon6Department of Information and Communication Engineering, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of KoreaDepartment of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of KoreaDepartment of Architectural Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, 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 Information and Communication Engineering, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of KoreaDepartment of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of KoreaWhite radish is a nutritious and delectable vegetable that is enjoyed globally. Conventional techniques for monitoring radish growth are arduous and time-consuming, encouraging the development of novel methods for quicker measurements and greater sampling density. This research introduces a mathematical model working on high-resolution images to measure radish’s biophysical properties automatically. A color calibration was performed on the dataset using a color checker panel to minimize the impact of varying light conditions on the RGB images. Subsequently, a Mask-RCNN model was trained to effectively segment different components of the radishes. The observations of the segmented results included leaf length, leaf width, root width, root length, leaf length to width, root length to width, root shoulder color, and root peel color. The automated real-life measurements of these observations were then conducted and compared with actual results. The validation results, based on a set of white radish samples, demonstrated the models’ effectiveness in utilizing images for quantifying phenotypic traits. The average accuracy of the automated method was confirmed to be 96.2% when compared to the manual method.https://www.mdpi.com/2073-4395/13/6/1630radishdeep learningmathematical modelingsegmentationphenotypic traits |
| spellingShingle | L. Minh Dang Kyungbok Min Tan N. Nguyen Han Yong Park O New Lee Hyoung-Kyu Song Hyeonjoon Moon Vision-Based White Radish Phenotypic Trait Measurement with Smartphone Imagery Agronomy radish deep learning mathematical modeling segmentation phenotypic traits |
| title | Vision-Based White Radish Phenotypic Trait Measurement with Smartphone Imagery |
| title_full | Vision-Based White Radish Phenotypic Trait Measurement with Smartphone Imagery |
| title_fullStr | Vision-Based White Radish Phenotypic Trait Measurement with Smartphone Imagery |
| title_full_unstemmed | Vision-Based White Radish Phenotypic Trait Measurement with Smartphone Imagery |
| title_short | Vision-Based White Radish Phenotypic Trait Measurement with Smartphone Imagery |
| title_sort | vision based white radish phenotypic trait measurement with smartphone imagery |
| topic | radish deep learning mathematical modeling segmentation phenotypic traits |
| url | https://www.mdpi.com/2073-4395/13/6/1630 |
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