Multimodal Data Fusion for Precise Lettuce Phenotype Estimation Using Deep Learning Algorithms
Effective lettuce cultivation requires precise monitoring of growth characteristics, quality assessment, and optimal harvest timing. In a recent study, a deep learning model based on multimodal data fusion was developed to estimate lettuce phenotypic traits accurately. A dual-modal network combining...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Plants |
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| Online Access: | https://www.mdpi.com/2223-7747/13/22/3217 |
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| author | Lixin Hou Yuxia Zhu Mengke Wang Ning Wei Jiachi Dong Yaodong Tao Jing Zhou Jian Zhang |
| author_facet | Lixin Hou Yuxia Zhu Mengke Wang Ning Wei Jiachi Dong Yaodong Tao Jing Zhou Jian Zhang |
| author_sort | Lixin Hou |
| collection | DOAJ |
| description | Effective lettuce cultivation requires precise monitoring of growth characteristics, quality assessment, and optimal harvest timing. In a recent study, a deep learning model based on multimodal data fusion was developed to estimate lettuce phenotypic traits accurately. A dual-modal network combining RGB and depth images was designed using an open lettuce dataset. The network incorporated both a feature correction module and a feature fusion module, significantly enhancing the performance in object detection, segmentation, and trait estimation. The model demonstrated high accuracy in estimating key traits, including fresh weight (fw), dry weight (dw), plant height (h), canopy diameter (d), and leaf area (la), achieving an R<sup>2</sup> of 0.9732 for fresh weight. Robustness and accuracy were further validated through 5-fold cross-validation, offering a promising approach for future crop phenotyping. |
| format | Article |
| id | doaj-art-9f538d70de21454fbedbdd7ae4f97baa |
| institution | OA Journals |
| issn | 2223-7747 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj-art-9f538d70de21454fbedbdd7ae4f97baa2025-08-20T02:27:36ZengMDPI AGPlants2223-77472024-11-011322321710.3390/plants13223217Multimodal Data Fusion for Precise Lettuce Phenotype Estimation Using Deep Learning AlgorithmsLixin Hou0Yuxia Zhu1Mengke Wang2Ning Wei3Jiachi Dong4Yaodong Tao5Jing Zhou6Jian Zhang7College of Information and Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, ChinaSchool of Computer Science and Technology, Beijing Jiaotong University, Beijing 100044, ChinaCollege of Information and Technology, Jilin Agricultural University, Changchun 130118, ChinaFaculty of Agronomy, Jilin Agricultural University, Changchun 130118, ChinaEffective lettuce cultivation requires precise monitoring of growth characteristics, quality assessment, and optimal harvest timing. In a recent study, a deep learning model based on multimodal data fusion was developed to estimate lettuce phenotypic traits accurately. A dual-modal network combining RGB and depth images was designed using an open lettuce dataset. The network incorporated both a feature correction module and a feature fusion module, significantly enhancing the performance in object detection, segmentation, and trait estimation. The model demonstrated high accuracy in estimating key traits, including fresh weight (fw), dry weight (dw), plant height (h), canopy diameter (d), and leaf area (la), achieving an R<sup>2</sup> of 0.9732 for fresh weight. Robustness and accuracy were further validated through 5-fold cross-validation, offering a promising approach for future crop phenotyping.https://www.mdpi.com/2223-7747/13/22/3217deep learningphenotypelettuceRGB-D |
| spellingShingle | Lixin Hou Yuxia Zhu Mengke Wang Ning Wei Jiachi Dong Yaodong Tao Jing Zhou Jian Zhang Multimodal Data Fusion for Precise Lettuce Phenotype Estimation Using Deep Learning Algorithms Plants deep learning phenotype lettuce RGB-D |
| title | Multimodal Data Fusion for Precise Lettuce Phenotype Estimation Using Deep Learning Algorithms |
| title_full | Multimodal Data Fusion for Precise Lettuce Phenotype Estimation Using Deep Learning Algorithms |
| title_fullStr | Multimodal Data Fusion for Precise Lettuce Phenotype Estimation Using Deep Learning Algorithms |
| title_full_unstemmed | Multimodal Data Fusion for Precise Lettuce Phenotype Estimation Using Deep Learning Algorithms |
| title_short | Multimodal Data Fusion for Precise Lettuce Phenotype Estimation Using Deep Learning Algorithms |
| title_sort | multimodal data fusion for precise lettuce phenotype estimation using deep learning algorithms |
| topic | deep learning phenotype lettuce RGB-D |
| url | https://www.mdpi.com/2223-7747/13/22/3217 |
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