ArticleJournal of imaging2023
A Point-Cloud Segmentation Network Based on SqueezeNet and Time Series for Plants.
Article in Journal of imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- DBGCN: Dual-branch Graph Convolutional Network for organ instance inference on sparsely labeled 3D plant data.Plant phenomics (Washington, D.C.) · 2026Article
- SOY3DSEG: A high-precision universal point cloud segmentation model for soybean full growth period based on improved point transformer.Plant phenomics (Washington, D.C.) · 2026Article
- A scalable framework for UAV-based point cloud reconstruction and organ segmentation of field-grown cotton in complex field environments.Frontiers in plant science · 2026Article
- Deep learning for three-dimensional (3D) plant phenomics.Plant phenomics (Washington, D.C.) · 2025Review
- Automatic 3D Plant Organ Instance Segmentation Method Based on PointNeXt and Quickshift+.Plant phenomics (Washington, D.C.) · 2025Article
- FishSegSSL: A Semi-Supervised Semantic Segmentation Framework for Fish-Eye Images.Journal of imaging · 2024Article
- Cucumber Seedling Segmentation Network Based on a Multiview Geometric Graph Encoder from 3D Point Clouds.Plant phenomics (Washington, D.C.) · 2024Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The phenotyping of plant growth enriches our understanding of intricate genetic characteristics, paving the way for advancements in modern breeding and precision agriculture. Within the domain of phenotyping, segmenting 3D point clouds of plant organs is the basis of extracting plant phenotypic parameters. In this study, we introduce a novel method for point-cloud downsampling that adeptly mitigates the challenges posed by sample imbalances. In subsequent developments, we architect a deep learning framework founded on the principles of SqueezeNet for the segmentation of plant point clouds. In addition, we also use the time series as input variables, which effectively improves the segmentation accuracy of the network. Based on semantic segmentation, the MeanShift algorithm is employed to execute instance segmentation on the point-cloud data of crops. In semantic segmentation, the average Precision, Recall, F1-score, and IoU of maize reached 99.35%, 99.26%, 99.30%, and 98.61%, and the average Precision, Recall, F1-score, and IoU of tomato reached 97.98%, 97.92%, 97.95%, and 95.98%. In instance segmentation, the accuracy of maize and tomato reached 98.45% and 96.12%. This research holds the potential to advance the fields of plant phenotypic extraction, ideotype selection, and precision agriculture.
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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.