ArticleFrontiers in plant science2022
Automatic monitoring of lettuce fresh weight by multi-modal fusion based deep learning.
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 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Enhancing autonomous agriculture control systems in greenhouses for sustainable resource usage using deep learning techniques.PloS one · 2026Article
- Precise and Continuous Biomass Measurement for Plant Growth Using a Low-Cost Sensor Setup.Sensors (Basel, Switzerland) · 2025Article
- Autonomous Greenhouse Cultivation of Dwarf Tomato: Performance Evaluation of Intelligent Algorithms for Multiple-Sensor Feedback.Sensors (Basel, Switzerland) · 2025Article
- Vase-Life Monitoring System for Cut Flowers Using Deep Learning and Multiple Cameras.Plants (Basel, Switzerland) · 2025Article
- 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
- Zero-shot instance segmentation for plant phenotyping in vertical farming with foundation models and VC-NMS.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
- Lettuce Production in Intelligent Greenhouses-3D Imaging and Computer Vision for Plant Spacing Decisions.Sensors (Basel, Switzerland) · 2023Article
- Estimation of rice seedling growth traits with an end-to-end multi-objective deep learning framework.Frontiers in plant science · 2023Article
- Real-time dense small object detection algorithm based on multi-modal tea shoots.Frontiers in plant science · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fresh weight is a widely used growth indicator for quantifying crop growth. Traditional fresh weight measurement methods are time-consuming, laborious, and destructive. Non-destructive measurement of crop fresh weight is urgently needed in plant factories with high environment controllability. In this study, we proposed a multi-modal fusion based deep learning model for automatic estimation of lettuce shoot fresh weight by utilizing RGB-D images. The model combined geometric traits from empirical feature extraction and deep neural features from CNN. A lettuce leaf segmentation network based on U-Net was trained for extracting leaf boundary and geometric traits. A multi-branch regression network was performed to estimate fresh weight by fusing color, depth, and geometric features. The leaf segmentation model reported a reliable performance with a mIoU of 0.982 and an accuracy of 0.998. A total of 10 geometric traits were defined to describe the structure of the lettuce canopy from segmented images. The fresh weight estimation results showed that the proposed multi-modal fusion model significantly improved the accuracy of lettuce shoot fresh weight in different growth periods compared with baseline models. The model yielded a root mean square error (RMSE) of 25.3 g and a coefficient of determination (
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.