A Plug Seedling Growth-Point Detection Method Based on Differential Evolution Extra-Green Algorithm
To produce plug seedlings with uniform growth and which are suitable for high-speed transplanting operations, it is essential to sow seeds precisely at the center of each plug-tray hole. For accurately determining the position of the seed covered by the substrate within individual plug-tray holes, a...
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MDPI AG
2025-01-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/2/375 |
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| author | Hongmei Xia Shicheng Zhu Teng Yang Runxin Huang Jianhua Ou Lingjin Dong Dewen Tao Wenbin Zhen |
| author_facet | Hongmei Xia Shicheng Zhu Teng Yang Runxin Huang Jianhua Ou Lingjin Dong Dewen Tao Wenbin Zhen |
| author_sort | Hongmei Xia |
| collection | DOAJ |
| description | To produce plug seedlings with uniform growth and which are suitable for high-speed transplanting operations, it is essential to sow seeds precisely at the center of each plug-tray hole. For accurately determining the position of the seed covered by the substrate within individual plug-tray holes, a novel method for detecting the growth points of plug seedlings has been proposed. It employs an adaptive grayscale processing algorithm based on the differential evolution extra-green algorithm to extract the contour features of seedlings during the early stages of cotyledon emergence. The pixel overlay curve peak points within the binary image of the plug-tray’s background are utilized to delineate the boundaries of the plug-tray holes. Each plug-tray hole containing a single seedling is identified by analyzing the area and perimeter of the seedling’s contour connectivity domains. The midpoint of the shortest line between these domains is designated as the growth point of the individual seedling. For laboratory-grown plug seedlings of tomato, pepper, and Chinese kale, the highest detection accuracy was achieved on the third-, fourth-, and second-days’ post-cotyledon emergence, respectively. The identification rate of missing seedlings and single seedlings exceeded 97.57% and 99.25%, respectively, with a growth-point detection error of less than 0.98 mm. For tomato and broccoli plug seedlings cultivated in a nursery greenhouse three days after cotyledon emergence, the detection accuracy for missing seedlings and single seedlings was greater than 95.78%, with a growth-point detection error of less than 2.06 mm. These results validated the high detection accuracy and broad applicability of the proposed method for various seedling types at the appropriate growth stages. |
| format | Article |
| id | doaj-art-6fc9abf47dcc4d5cb1567ff1a63b827e |
| institution | DOAJ |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-6fc9abf47dcc4d5cb1567ff1a63b827e2025-08-20T02:44:36ZengMDPI AGAgronomy2073-43952025-01-0115237510.3390/agronomy15020375A Plug Seedling Growth-Point Detection Method Based on Differential Evolution Extra-Green AlgorithmHongmei Xia0Shicheng Zhu1Teng Yang2Runxin Huang3Jianhua Ou4Lingjin Dong5Dewen Tao6Wenbin Zhen7College of Engineering, South China Agricultural University, 483 Wushan Road, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, 483 Wushan Road, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, 483 Wushan Road, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, 483 Wushan Road, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, 483 Wushan Road, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, 483 Wushan Road, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, 483 Wushan Road, Guangzhou 510642, ChinaCollege of Engineering, South China Agricultural University, 483 Wushan Road, Guangzhou 510642, ChinaTo produce plug seedlings with uniform growth and which are suitable for high-speed transplanting operations, it is essential to sow seeds precisely at the center of each plug-tray hole. For accurately determining the position of the seed covered by the substrate within individual plug-tray holes, a novel method for detecting the growth points of plug seedlings has been proposed. It employs an adaptive grayscale processing algorithm based on the differential evolution extra-green algorithm to extract the contour features of seedlings during the early stages of cotyledon emergence. The pixel overlay curve peak points within the binary image of the plug-tray’s background are utilized to delineate the boundaries of the plug-tray holes. Each plug-tray hole containing a single seedling is identified by analyzing the area and perimeter of the seedling’s contour connectivity domains. The midpoint of the shortest line between these domains is designated as the growth point of the individual seedling. For laboratory-grown plug seedlings of tomato, pepper, and Chinese kale, the highest detection accuracy was achieved on the third-, fourth-, and second-days’ post-cotyledon emergence, respectively. The identification rate of missing seedlings and single seedlings exceeded 97.57% and 99.25%, respectively, with a growth-point detection error of less than 0.98 mm. For tomato and broccoli plug seedlings cultivated in a nursery greenhouse three days after cotyledon emergence, the detection accuracy for missing seedlings and single seedlings was greater than 95.78%, with a growth-point detection error of less than 2.06 mm. These results validated the high detection accuracy and broad applicability of the proposed method for various seedling types at the appropriate growth stages.https://www.mdpi.com/2073-4395/15/2/375differential evolution algorithmadaptive grayscale processgrowth-point detectionplug seedling identificationimage processing |
| spellingShingle | Hongmei Xia Shicheng Zhu Teng Yang Runxin Huang Jianhua Ou Lingjin Dong Dewen Tao Wenbin Zhen A Plug Seedling Growth-Point Detection Method Based on Differential Evolution Extra-Green Algorithm Agronomy differential evolution algorithm adaptive grayscale process growth-point detection plug seedling identification image processing |
| title | A Plug Seedling Growth-Point Detection Method Based on Differential Evolution Extra-Green Algorithm |
| title_full | A Plug Seedling Growth-Point Detection Method Based on Differential Evolution Extra-Green Algorithm |
| title_fullStr | A Plug Seedling Growth-Point Detection Method Based on Differential Evolution Extra-Green Algorithm |
| title_full_unstemmed | A Plug Seedling Growth-Point Detection Method Based on Differential Evolution Extra-Green Algorithm |
| title_short | A Plug Seedling Growth-Point Detection Method Based on Differential Evolution Extra-Green Algorithm |
| title_sort | plug seedling growth point detection method based on differential evolution extra green algorithm |
| topic | differential evolution algorithm adaptive grayscale process growth-point detection plug seedling identification image processing |
| url | https://www.mdpi.com/2073-4395/15/2/375 |
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