ArticleFrontiers in plant science2022
TMSCNet: A three-stage multi-branch self-correcting trait estimation network for RGB and depth images of lettuce.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- A method for phenotyping lettuce volume and structure from 3D images.Plant methods · 2025Article
- Multi-objective RGB-D fusion network for non-destructive strawberry trait assessment.Frontiers in plant science · 2025Article
- Multimodal Data Fusion for Precise Lettuce Phenotype Estimation Using Deep Learning Algorithms.Plants (Basel, Switzerland) · 2024Article
- Development of a machine vision-based weight prediction system of butterhead lettuce (Frontiers in plant science · 2024Article
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Abstract
Growth traits, such as fresh weight, diameter, and leaf area, are pivotal indicators of growth status and the basis for the quality evaluation of lettuce. The time-consuming, laborious and inefficient method of manually measuring the traits of lettuce is still the mainstream. In this study, a three-stage multi-branch self-correcting trait estimation network (TMSCNet) for RGB and depth images of lettuce was proposed. The TMSCNet consisted of five models, of which two master models were used to preliminarily estimate the fresh weight (FW), dry weight (DW), height (H), diameter (D), and leaf area (LA) of lettuce, and three auxiliary models realized the automatic correction of the preliminary estimation results. To compare the performance, typical convolutional neural networks (CNNs) widely adopted in botany research were used. The results showed that the estimated values of the TMSCNet fitted the measurements well, with coefficient of determination (
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