ArticleHorticulture research2020
Growth monitoring of greenhouse lettuce based on a convolutional neural network.
Article in Horticulture research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Deep learning-based 3D morphological segmentation and quantitative growth analysis of field-grown cabbage across the full cycle.Plant phenomics (Washington, D.C.) · 2026Article
- Non-destructive monitoring of root biomass in hydroponically grown leafy vegetables: comparison between machine learning-based RGB and hyperspectral imaging.Plant methods · 2026Article
- From leaf to canopy: Inversion of lettuce pigment distribution using hyperspectral imaging technology combined with deep learning algorithms.Plant phenomics (Washington, D.C.) · 2025Article
- The blessing of Depth Anything: An almost unsupervised approach to crop segmentation with depth-informed pseudo labeling.Plant phenomics (Washington, D.C.) · 2025Article
- A method for phenotyping lettuce volume and structure from 3D images.Plant methods · 2025Article
- Predictive modelling employing machine learning, convolutional neural networks (CNNs), and smartphone RGB images for non-destructive biomass estimation of pearl millet (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
- Research Progress of Spectral Imaging Techniques in Plant Phenotype Studies.Plants (Basel, Switzerland) · 2024Review
- Development of an Automated Low-Cost Multispectral Imaging System to Quantify Canopy Size and Pigmentation.Sensors (Basel, Switzerland) · 2024Article
- Low-Cost Imaging to Quantify Germination Rate and Seedling Vigor across Lettuce Cultivars.Sensors (Basel, Switzerland) · 2024Article
- A hyperspectral deep learning attention model for predicting lettuce chlorophyll content.Plant methods · 2024Article
- Development of a machine vision-based weight prediction system of butterhead lettuce (Frontiers in plant science · 2024Article
- Image-Based High-Throughput Phenotyping in Horticultural Crops.Plants (Basel, Switzerland) · 2023Review
- Estimation of rice seedling growth traits with an end-to-end multi-objective deep learning framework.Frontiers in plant science · 2023Article
- Application of artificial intelligence for automatic cataract staging based on anterior segment images: comparing automatic segmentation approaches to manual segmentation.Frontiers in neuroscience · 2023Article
- Fruit Volume and Leaf-Area Determination of Cabbage by a Neural-Network-Based Instance Segmentation for Different Growth Stages.Sensors (Basel, Switzerland) · 2022Article
- Non-Destructive Monitoring of Crop Fresh Weight and Leaf Area with a Simple Formula and a Convolutional Neural Network.Sensors (Basel, Switzerland) · 2022Article
- Estimation of Greenhouse Lettuce Growth Indices Based on a Two-Stage CNN Using RGB-D Images.Sensors (Basel, Switzerland) · 2022Article
- Quantitative phenotyping and evaluation for lettuce leaves of multiple semantic components.Plant methods · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Growth-related traits, such as aboveground biomass and leaf area, are critical indicators to characterize the growth of greenhouse lettuce. Currently, nondestructive methods for estimating growth-related traits are subject to limitations in that the methods are susceptible to noise and heavily rely on manually designed features. In this study, a method for monitoring the growth of greenhouse lettuce was proposed by using digital images and a convolutional neural network (CNN). Taking lettuce images as the input, a CNN model was trained to learn the relationship between images and the corresponding growth-related traits, i.e., leaf fresh weight (LFW), leaf dry weight (LDW), and leaf area (LA). To compare the results of the CNN model, widely adopted methods were also used. The results showed that the values estimated by CNN had good agreement with the actual measurements, with
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