ArticlePlant phenomics (Washington, D.C.)2020
Semantic Segmentation of Sorghum Using Hyperspectral Data Identifies Genetic Associations.
Article in Plant phenomics (Washington, D.C.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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.
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.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- Meta-analysis identifies pleiotropic loci controlling phenotypic trade-offs in sorghum.Genetics · 2021Pooled it
- A configurable five-step workflow for high-throughput hyperspectral image analysis and vegetation index mapping.MethodsX · 2026Article
- FLDO-LKNet: An Efficient Method for Segmenting Stone Cells in Rubber Tree Bark with Robustness to Staining Differences and Morphological and Scale Changes.Plants (Basel, Switzerland) · 2026Article
- Overcoming difficulties in segmentation of hyperspectral plant images with small projection areas using machine learning.Scientific reports · 2026Article
- A Mycelium Dataset with Edge-Precise Annotation for Semantic Segmentation.Scientific data · 2025Article
- MOSSNet: multiscale and oriented sorghum spike detection and counting in UAV images.Frontiers in plant science · 2025Article
- Integrating Genomic Selection and a Genome-Wide Association Study to Improve Days Open in Thai Dairy Holstein Cattle: A Comprehensive Genetic Analysis.Animals : an open access journal from MDPI · 2024Article
- Cotton leaf segmentation with composite backbone architecture combining convolution and attention.Frontiers in plant science · 2023Article
- High-throughput field crop phenotyping: current status and challenges.Breeding science · 2022Article
- SegVeg: Segmenting RGB Images into Green and Senescent Vegetation by Combining Deep and Shallow Methods.Plant phenomics (Washington, D.C.) · 2022Article
- Plant leaf veins coupling feature representation and measurement method based on DeepLabV3.Frontiers in plant science · 2022Article
- Deterministic and stochastic modelling of impacts from genomic selection and phenomics on genetic gain for perennial ryegrass dry matter yield.Scientific reports · 2021Article
- Panicle-3D: Efficient Phenotyping Tool for Precise Semantic Segmentation of Rice Panicle Point Cloud.Plant phenomics (Washington, D.C.) · 2021Article
- Voxel carving-based 3D reconstruction of sorghum identifies genetic determinants of light interception efficiency.Plant direct · 2020Article
- Increased Power and Accuracy of Causal Locus Identification in Time Series Genome-wide Association in Sorghum.Plant physiology · 2020Article
- High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field.Frontiers in bioengineering and biotechnology · 2020Review
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 at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study describes the evaluation of a range of approaches to semantic segmentation of hyperspectral images of sorghum plants, classifying each pixel as either nonplant or belonging to one of the three organ types (leaf, stalk, panicle). While many current methods for segmentation focus on separating plant pixels from background, organ-specific segmentation makes it feasible to measure a wider range of plant properties. Manually scored training data for a set of hyperspectral images collected from a sorghum association population was used to train and evaluate a set of supervised classification models. Many algorithms show acceptable accuracy for this classification task. Algorithms trained on sorghum data are able to accurately classify maize leaves and stalks, but fail to accurately classify maize reproductive organs which are not directly equivalent to sorghum panicles. Trait measurements extracted from semantic segmentation of sorghum organs can be used to identify both genes known to be controlling variation in a previously measured phenotypes (e.g., panicle size and plant height) as well as identify signals for genes controlling traits not previously quantified in this population (e.g., stalk/leaf ratio). Organ level semantic segmentation provides opportunities to identify genes controlling variation in a wide range of morphological phenotypes in sorghum, maize, and other related grain crops.
Identifiers
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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.