RGB and Point Cloud-Based Intelligent Grading of Pepper Plug Seedlings
As an emerging vegetable cultivation technology, plug seedling cultivation significantly improves seedling production efficiency and reduces costs through standardization. Grading and transplanting, as the final step before the sale of plug seedlings, categorizes seedlings into different grades to e...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/7/1568 |
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| author | Fengwei Yuan Guoning Ma Qinghao Zeng Jinghong Liu Zhang Xiao Zhenhong Zou Xiangjiang Wang |
| author_facet | Fengwei Yuan Guoning Ma Qinghao Zeng Jinghong Liu Zhang Xiao Zhenhong Zou Xiangjiang Wang |
| author_sort | Fengwei Yuan |
| collection | DOAJ |
| description | As an emerging vegetable cultivation technology, plug seedling cultivation significantly improves seedling production efficiency and reduces costs through standardization. Grading and transplanting, as the final step before the sale of plug seedlings, categorizes seedlings into different grades to ensure consistent quality. However, most current grading methods can only detect seedling emergence but cannot classify the emerged seedlings. Therefore, this study proposes an intelligent grading method for pepper plug seedlings based on RGB and point cloud images, enabling precise grading using both RGB and 3D point cloud data. The proposed method involves the following steps: First, RGB and point cloud images of the seedlings are acquired using 2D and 3D cameras. The point cloud data is then converted into a 2D representation and aligned with the RGB images. Next, a deep learning-based object detection algorithm identifies the positions of individual seedlings in the RGB images. Using these positions, the seedlings are segmented from both the RGB and 2D point cloud images. Subsequently, a deep learning-based leaf recognition algorithm processes the segmented RGB images to determine leaf count, while another deep learning-based algorithm segments the leaves in the 2D point cloud images to extract their spatial information. Their surface area is measured using 3D reconstruction method to calculate leaf area. Additionally, plant height is derived from the point cloud’s height data. Finally, a classification model is trained using these extracted features to establish a grading system. Experimental results demonstrate that this automated grading method achieves a success rate of 97%, and compared with manual methods, this method has higher production efficiency. Meanwhile, it can grade different tray seedlings by training different models and provide reliable technical support for the quality evaluation of seedlings in industrialized transplanting production. |
| format | Article |
| id | doaj-art-b1014d2eb5d14b2daf4f8670533532e9 |
| institution | DOAJ |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-b1014d2eb5d14b2daf4f8670533532e92025-08-20T03:13:41ZengMDPI AGAgronomy2073-43952025-06-01157156810.3390/agronomy15071568RGB and Point Cloud-Based Intelligent Grading of Pepper Plug SeedlingsFengwei Yuan0Guoning Ma1Qinghao Zeng2Jinghong Liu3Zhang Xiao4Zhenhong Zou5Xiangjiang Wang6College of Mechanical Engineering, University of South China, Hengyang 421001, ChinaCollege of Mechanical Engineering, University of South China, Hengyang 421001, ChinaCollege of Mechanical Engineering, University of South China, Hengyang 421001, ChinaCollege of Mechanical Engineering, University of South China, Hengyang 421001, ChinaCollege of Mechanical Engineering, University of South China, Hengyang 421001, ChinaHengyang Vegetable Seeds Co., Hengyang 421001, ChinaCollege of Mechanical Engineering, University of South China, Hengyang 421001, ChinaAs an emerging vegetable cultivation technology, plug seedling cultivation significantly improves seedling production efficiency and reduces costs through standardization. Grading and transplanting, as the final step before the sale of plug seedlings, categorizes seedlings into different grades to ensure consistent quality. However, most current grading methods can only detect seedling emergence but cannot classify the emerged seedlings. Therefore, this study proposes an intelligent grading method for pepper plug seedlings based on RGB and point cloud images, enabling precise grading using both RGB and 3D point cloud data. The proposed method involves the following steps: First, RGB and point cloud images of the seedlings are acquired using 2D and 3D cameras. The point cloud data is then converted into a 2D representation and aligned with the RGB images. Next, a deep learning-based object detection algorithm identifies the positions of individual seedlings in the RGB images. Using these positions, the seedlings are segmented from both the RGB and 2D point cloud images. Subsequently, a deep learning-based leaf recognition algorithm processes the segmented RGB images to determine leaf count, while another deep learning-based algorithm segments the leaves in the 2D point cloud images to extract their spatial information. Their surface area is measured using 3D reconstruction method to calculate leaf area. Additionally, plant height is derived from the point cloud’s height data. Finally, a classification model is trained using these extracted features to establish a grading system. Experimental results demonstrate that this automated grading method achieves a success rate of 97%, and compared with manual methods, this method has higher production efficiency. Meanwhile, it can grade different tray seedlings by training different models and provide reliable technical support for the quality evaluation of seedlings in industrialized transplanting production.https://www.mdpi.com/2073-4395/15/7/1568plug seedlingmachine learningdeep learningfeature extractionclassification method |
| spellingShingle | Fengwei Yuan Guoning Ma Qinghao Zeng Jinghong Liu Zhang Xiao Zhenhong Zou Xiangjiang Wang RGB and Point Cloud-Based Intelligent Grading of Pepper Plug Seedlings Agronomy plug seedling machine learning deep learning feature extraction classification method |
| title | RGB and Point Cloud-Based Intelligent Grading of Pepper Plug Seedlings |
| title_full | RGB and Point Cloud-Based Intelligent Grading of Pepper Plug Seedlings |
| title_fullStr | RGB and Point Cloud-Based Intelligent Grading of Pepper Plug Seedlings |
| title_full_unstemmed | RGB and Point Cloud-Based Intelligent Grading of Pepper Plug Seedlings |
| title_short | RGB and Point Cloud-Based Intelligent Grading of Pepper Plug Seedlings |
| title_sort | rgb and point cloud based intelligent grading of pepper plug seedlings |
| topic | plug seedling machine learning deep learning feature extraction classification method |
| url | https://www.mdpi.com/2073-4395/15/7/1568 |
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