ArticleSensors (Basel, Switzerland)2018
Automatic Leaf Segmentation for Estimating Leaf Area and Leaf Inclination Angle in 3D Plant Images.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- TomatoPGT: A 3D point cloud dataset of tomato plants for segmentation and plant-trait extraction.Data in brief · 2026Article
- Projecting 2D top-view of PSII efficiency onto 3D plant models to quantify PSII efficiency across canopy layers.Plant methods · 2026Article
- Robust estimation of rice flag leaf inclination angle from SfM-MVS point clouds via ensemble skeleton extraction: validation in field and pot experiments.Plant methods · 2026Article
- Article
- Completing the picture of field-grown cereal crops: a new method for detailed leaf surface models in wheat.Plant methods · 2024Article
- LeTra: a leaf tracking workflow based on convolutional neural networks and intersection over union.Plant methods · 2024Article
- A 3D printed plant model for accurate and reliable 3D plant phenotyping.GigaScience · 2024Article
- Deep learning for automated segmentation and counting of hypocotyl and cotyledon regions in matureFrontiers in plant science · 2024Article
- A Point-Cloud Segmentation Network Based on SqueezeNet and Time Series for Plants.Journal of imaging · 2023Article
- FF-Net: Feature-Fusion-Based Network for Semantic Segmentation of 3D Plant Point Cloud.Plants (Basel, Switzerland) · 2023Article
- 3D data-augmentation methods for semantic segmentation of tomato plant parts.Frontiers in plant science · 2023Article
- Enhancing Green Fraction Estimation in Rice and Wheat Crops: A Self-Supervised Deep Learning Semantic Segmentation Approach.Plant phenomics (Washington, D.C.) · 2023Article
- Fruit Volume and Leaf-Area Determination of Cabbage by a Neural-Network-Based Instance Segmentation for Different Growth Stages.Sensors (Basel, Switzerland) · 2022Article
- ATAC-seq exposes differences in chromatin accessibility leading to distinct leaf shapes in mulberry.Plant direct · 2022Article
- MIX-NET: Deep Learning-Based Point Cloud Processing Method for Segmentation and Occlusion Leaf Restoration of Seedlings.Plants (Basel, Switzerland) · 2022Article
- Accelerating leaf area measurement using a volumetric approach.Plant methods · 2022Article
- PSegNet: Simultaneous Semantic and Instance Segmentation for Point Clouds of Plants.Plant phenomics (Washington, D.C.) · 2022Article
- Article
- Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping.Sensors (Basel, Switzerland) · 2020Article
- A Plant Leaf Geometric Parameter Measurement System Based on the Android Platform.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automatic and efficient plant monitoring offers accurate plant management. Construction of three-dimensional (3D) models of plants and acquisition of their spatial information is an effective method for obtaining plant structural parameters. Here, 3D images of leaves constructed with multiple scenes taken from different positions were segmented automatically for the automatic retrieval of leaf areas and inclination angles. First, for the initial segmentation, leave images were viewed from the top, then leaves in the top-view images were segmented using distance transform and the watershed algorithm. Next, the images of leaves after the initial segmentation were reduced by 90%, and the seed regions for each leaf were produced. The seed region was re-projected onto the 3D images, and each leaf was segmented by expanding the seed region with the 3D information. After leaf segmentation, the leaf area of each leaf and its inclination angle were estimated accurately via a voxel-based calculation. As a result, leaf area and leaf inclination angle were estimated accurately after automatic leaf segmentation. This method for automatic plant structure analysis allows accurate and efficient plant breeding and growth management.
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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.