Tomato Stem and Leaf Segmentation and Phenotype Parameter Extraction Based on Improved Red Billed Blue Magpie Optimization Algorithm
In response to the structural changes of tomato seedlings, traditional image techniques are difficult to accurately quantify key morphological parameters, such as leaf area, internode length, and mutual occlusion between organs. Therefore, this paper proposes a tomato point cloud stem and leaf segme...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/15/2/180 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832589485745897472 |
|---|---|
| author | Lina Zhang Ziyi Huang Zhiyin Yang Bo Yang Shengpeng Yu Shuai Zhao Xingrui Zhang Xinying Li Han Yang Yixing Lin Helong Yu |
| author_facet | Lina Zhang Ziyi Huang Zhiyin Yang Bo Yang Shengpeng Yu Shuai Zhao Xingrui Zhang Xinying Li Han Yang Yixing Lin Helong Yu |
| author_sort | Lina Zhang |
| collection | DOAJ |
| description | In response to the structural changes of tomato seedlings, traditional image techniques are difficult to accurately quantify key morphological parameters, such as leaf area, internode length, and mutual occlusion between organs. Therefore, this paper proposes a tomato point cloud stem and leaf segmentation framework based on Elite Strategy-based Improved Red-billed Blue Magpie Optimization (ES-RBMO) Algorithm. The framework uses a four-layer Convolutional Neural Network (CNN) for stem and leaf segmentation by incorporating an improved swarm intelligence algorithm with an accuracy of 0.965. Four key phenotypic parameters of the plant were extracted. The phenotypic parameters of plant height, stem thickness, leaf area and leaf inclination were analyzed by comparing the values extracted by manual measurements with the values extracted by the 3D point cloud technique. The results showed that the coefficients of determination (R<sup>2</sup>) for these parameters were 0.932, 0.741, 0.938 and 0.935, respectively, indicating high correlation. The root mean square error (RMSE) was 0.511, 0.135, 0.989 and 3.628, reflecting the level of error between the measured and extracted values. The absolute percentage errors (APE) were 1.970, 4.299, 4.365 and 5.531, which further quantified the measurement accuracy. In this study, an efficient and adaptive intelligent optimization framework was constructed, which is capable of optimizing data processing strategies to achieve efficient and accurate processing of tomato point cloud data. This study provides a new technical tool for plant phenotyping and helps to improve the intelligent management in agricultural production. |
| format | Article |
| id | doaj-art-94e89e3bd37b4ab297e006f84202f0e5 |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-94e89e3bd37b4ab297e006f84202f0e52025-01-24T13:16:02ZengMDPI AGAgriculture2077-04722025-01-0115218010.3390/agriculture15020180Tomato Stem and Leaf Segmentation and Phenotype Parameter Extraction Based on Improved Red Billed Blue Magpie Optimization AlgorithmLina Zhang0Ziyi Huang1Zhiyin Yang2Bo Yang3Shengpeng Yu4Shuai Zhao5Xingrui Zhang6Xinying Li7Han Yang8Yixing Lin9Helong Yu10College of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Engineering, Changchun University of Finance and Economics, Changchun 130217, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaIn response to the structural changes of tomato seedlings, traditional image techniques are difficult to accurately quantify key morphological parameters, such as leaf area, internode length, and mutual occlusion between organs. Therefore, this paper proposes a tomato point cloud stem and leaf segmentation framework based on Elite Strategy-based Improved Red-billed Blue Magpie Optimization (ES-RBMO) Algorithm. The framework uses a four-layer Convolutional Neural Network (CNN) for stem and leaf segmentation by incorporating an improved swarm intelligence algorithm with an accuracy of 0.965. Four key phenotypic parameters of the plant were extracted. The phenotypic parameters of plant height, stem thickness, leaf area and leaf inclination were analyzed by comparing the values extracted by manual measurements with the values extracted by the 3D point cloud technique. The results showed that the coefficients of determination (R<sup>2</sup>) for these parameters were 0.932, 0.741, 0.938 and 0.935, respectively, indicating high correlation. The root mean square error (RMSE) was 0.511, 0.135, 0.989 and 3.628, reflecting the level of error between the measured and extracted values. The absolute percentage errors (APE) were 1.970, 4.299, 4.365 and 5.531, which further quantified the measurement accuracy. In this study, an efficient and adaptive intelligent optimization framework was constructed, which is capable of optimizing data processing strategies to achieve efficient and accurate processing of tomato point cloud data. This study provides a new technical tool for plant phenotyping and helps to improve the intelligent management in agricultural production.https://www.mdpi.com/2077-0472/15/2/180tomato plantsred-billed blue magpie optimization algorithmelite strategypoint cloud segmentationconvolutional neural networkphenotype extraction analysis |
| spellingShingle | Lina Zhang Ziyi Huang Zhiyin Yang Bo Yang Shengpeng Yu Shuai Zhao Xingrui Zhang Xinying Li Han Yang Yixing Lin Helong Yu Tomato Stem and Leaf Segmentation and Phenotype Parameter Extraction Based on Improved Red Billed Blue Magpie Optimization Algorithm Agriculture tomato plants red-billed blue magpie optimization algorithm elite strategy point cloud segmentation convolutional neural network phenotype extraction analysis |
| title | Tomato Stem and Leaf Segmentation and Phenotype Parameter Extraction Based on Improved Red Billed Blue Magpie Optimization Algorithm |
| title_full | Tomato Stem and Leaf Segmentation and Phenotype Parameter Extraction Based on Improved Red Billed Blue Magpie Optimization Algorithm |
| title_fullStr | Tomato Stem and Leaf Segmentation and Phenotype Parameter Extraction Based on Improved Red Billed Blue Magpie Optimization Algorithm |
| title_full_unstemmed | Tomato Stem and Leaf Segmentation and Phenotype Parameter Extraction Based on Improved Red Billed Blue Magpie Optimization Algorithm |
| title_short | Tomato Stem and Leaf Segmentation and Phenotype Parameter Extraction Based on Improved Red Billed Blue Magpie Optimization Algorithm |
| title_sort | tomato stem and leaf segmentation and phenotype parameter extraction based on improved red billed blue magpie optimization algorithm |
| topic | tomato plants red-billed blue magpie optimization algorithm elite strategy point cloud segmentation convolutional neural network phenotype extraction analysis |
| url | https://www.mdpi.com/2077-0472/15/2/180 |
| work_keys_str_mv | AT linazhang tomatostemandleafsegmentationandphenotypeparameterextractionbasedonimprovedredbilledbluemagpieoptimizationalgorithm AT ziyihuang tomatostemandleafsegmentationandphenotypeparameterextractionbasedonimprovedredbilledbluemagpieoptimizationalgorithm AT zhiyinyang tomatostemandleafsegmentationandphenotypeparameterextractionbasedonimprovedredbilledbluemagpieoptimizationalgorithm AT boyang tomatostemandleafsegmentationandphenotypeparameterextractionbasedonimprovedredbilledbluemagpieoptimizationalgorithm AT shengpengyu tomatostemandleafsegmentationandphenotypeparameterextractionbasedonimprovedredbilledbluemagpieoptimizationalgorithm AT shuaizhao tomatostemandleafsegmentationandphenotypeparameterextractionbasedonimprovedredbilledbluemagpieoptimizationalgorithm AT xingruizhang tomatostemandleafsegmentationandphenotypeparameterextractionbasedonimprovedredbilledbluemagpieoptimizationalgorithm AT xinyingli tomatostemandleafsegmentationandphenotypeparameterextractionbasedonimprovedredbilledbluemagpieoptimizationalgorithm AT hanyang tomatostemandleafsegmentationandphenotypeparameterextractionbasedonimprovedredbilledbluemagpieoptimizationalgorithm AT yixinglin tomatostemandleafsegmentationandphenotypeparameterextractionbasedonimprovedredbilledbluemagpieoptimizationalgorithm AT helongyu tomatostemandleafsegmentationandphenotypeparameterextractionbasedonimprovedredbilledbluemagpieoptimizationalgorithm |