ArticleScientific reports2021
Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 42 citations in OpenAlex.
- A novel approach integrating multispectral imaging and machine learning to identify seed maturity and vigor in smooth bromegrass.Plant methods · 2025Article
- Leveraging sensor technologies for seed phenotyping by genebanks.Frontiers in plant science · 2025Article
- Soybean seed pest damage detection method based on spatial frequency domain imaging combined with RL-SVM.Plant methods · 2024Article
- Autofluorescence-spectral imaging for rapid and invasive characterization of soybean for pre-germination anaerobic stress tolerance.Frontiers in plant science · 2024Article
- Stress phenotyping analysis leveraging autofluorescence image sequences with machine learning.Frontiers in plant science · 2024Article
- Encapsulation of Carbon Dots in a Core-Shell Mesh through Coaxial Direct Ink Writing for Improved Crop Growth.ACS sustainable chemistry & engineering · 2023Article
- Vigour testing for the rice seed with computer vision-based techniques.Frontiers in plant science · 2023Article
- Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality.Frontiers in plant science · 2023Article
- Integrating optical imaging techniques for a novel approach to evaluate Siberian wild rye seed maturity.Frontiers in plant science · 2023Article
- Transcripts Expressed during GerminationPlants (Basel, Switzerland) · 2022Article
- An Approach Using Emerging Optical Technologies and Artificial Intelligence Brings New Markers to Evaluate Peanut Seed Quality.Frontiers in plant science · 2022Article
- Slight crack identification of cottonseed using air-coupled ultrasound with sound to image encoding.Frontiers in plant science · 2022Article
- A Reliable Method to Recognize Soybean Seed Maturation Stages Based on Autofluorescence-Spectral Imaging Combined With Machine Learning Algorithms.Frontiers in plant science · 2022Article
- Haematococcus pluvialis bionanoparticles boost maize seedling health, serving as a sustainable seed priming agent and biostimulant for agriculture.Physiologia plantarumArticle
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 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the agricultural industry, advances in optical imaging technologies based on rapid and non-destructive approaches have contributed to increase food production for the growing population. The present study employed autofluorescence-spectral imaging and machine learning algorithms to develop distinct models for classification of soybean seeds differing in physiological quality after artificial aging. Autofluorescence signals from the 365/400 nm excitation-emission combination (that exhibited a perfect correlation with the total phenols in the embryo) were efficiently able to segregate treatments. Furthermore, it was also possible to demonstrate a strong correlation between autofluorescence-spectral data and several quality indicators, such as early germination and seed tolerance to stressful conditions. The machine learning models developed based on artificial neural network, support vector machine or linear discriminant analysis showed high performance (0.99 accuracy) for classifying seeds with different quality levels. Taken together, our study shows that the physiological potential of soybean seeds is reduced accompanied by changes in the concentration and, probably in the structure of autofluorescent compounds. In addition, altering the autofluorescent properties in seeds impact the photosynthesis apparatus in seedlings. From the practical point of view, autofluorescence-based imaging can be used to check modifications in the optical properties of soybean seed tissues and to consistently discriminate high-and low-vigor seeds.
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.