ArticleFrontiers in plant science2022
Hyperspectral Technique Combined With Deep Learning Algorithm for Prediction of Phenotyping Traits in Lettuce.
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 7 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
7 citing papers in PubMed.
- Fault Diagnosis Method for Electric Vehicle In-Wheel Motor Bearings Based on Improved SVMD and ResNet-KAN.Sensors (Basel, Switzerland) · 2026Article
- KG-FT-Transformer: a knowledge-guided feature tokenizer transformer for hyperspectral prediction of lettuce quality trait and fingerprint analysis.Food chemistry: X · 2026Article
- Application of Laser-Induced Breakdown Spectroscopy (LIBS) technology in malignant tumor diagnosis.Discover oncology · 2026Review
- Generalizability and transferability of machine learning models using hyperspectral reflectance data for maize traits.Scientific reports · 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
- High-Precision Automated Soybean Phenotypic Feature Extraction Based on Deep Learning and Computer Vision.Plants (Basel, Switzerland) · 2024Article
- Hyperspectral imaging and artificial intelligence enhance remote phenotyping of grapevine rootstock influence on whole vine photosynthesis.Frontiers in plant science · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The currently available methods for evaluating most biochemical traits of plant phenotyping are destructive and have extremely low throughput. However, hyperspectral techniques can non-destructively obtain the spectral reflectance characteristics of plants, which can provide abundant biophysical and biochemical information. Therefore, plant spectra combined with machine learning algorithms can be used to predict plant phenotyping traits. However, the raw spectral reflectance characteristics contain noise and redundant information, thus can easily affect the robustness of the models developed
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.