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Multimodal Data Fusion for Precise Lettuce Phenotype Estimation Using Deep Learning Algorithms
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DOI：10.3390/plants13223217.png)
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摘要
En 中文
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 R2 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.
Keyword:
deep learning
phenotype
lettuce
RGB-D
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.1
论文数:
2.2W
被引数:
6.4W
机构
引用论文
LettuceNet: A Novel Deep Learning Approach for Efficient Lettuce Localization and Counting.png)
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