ReviewJournal of advanced research2024
Digital techniques and trends for seed phenotyping using optical sensors.
Review in Journal of advanced research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
The trial behind it
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Who cites it
6 citing papers in PubMed, 23 citations in OpenAlex.
- Identification of Unsound Soybean Seeds Based on Hyperspectral Imaging and a Dual-Channel Residual-Squeeze-and-Excitation Network With Gramian Angular Field Fusion.Journal of food science · 2026Article
- GMFNet: A GADF-Mamba Fusion Network for Soybean Seed Hyperspectral Classification.Foods (Basel, Switzerland) · 2026Article
- Non-destructive detection strategy of maize seed vigor based on seed phenotyping and the potential for accelerating breeding.Journal of advanced research · 2025Article
- Olive Variety Classification and Prediction From 3D Morphology of Fruit and Stone: A Study Case on Five South Italy Autochthone Cultivars.Food science & nutrition · 2025Article
- MRI-Seed-Wizard: combining deep learning algorithms with magnetic resonance imaging enables advanced seed phenotyping.Journal of experimental botany · 2025Article
- Leveraging sensor technologies for seed phenotyping by genebanks.Frontiers in plant science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe breeding of high-quality, high-yield, and disease-resistant varieties is closely related to food security. The investigation of breeding results relies on the evaluation of seed phenotype, which is a key step in the process of breeding. In the global digitalization trend, digital technology based on optical sensors can perform the digitization of seed phenotype in a non-contact, high throughput way, thus significantly improving breeding efficiency. AIM OF REVIEW: This paper provides a comprehensive overview of the principles, characteristics, data processing methods, and bottlenecks associated with three digital technique types based on optical sensors: spectroscopy, digital imaging, and three-dimensional (3D) reconstruction techniques. In addition, the applicability and adaptability of digital techniques based on the optical sensors of maize seed phenotype traits, namely external visible phenotype (EVP) and internal invisible phenotype (IIP), are investigated. Furthermore, trends in future equipment, platform, phenotype data, and processing algorithms are discussed. This review offers conceptual and practical support for seed phenotype digitization based on optical sensors, which will provide reference and guidance for future research. KEY SCIENTIFIC CONCEPTS OF REVIEW: The digital techniques based on optical sensors can perform non-contact and high-throughput seed phenotype evaluation. Due to the distinct characteristics of optical sensors, matching suitable digital techniques according to seed phenotype traits can greatly reduce resource loss, and promote the efficiency of seed evaluation as well as breeding decision-making. Future research in phenotype equipment and platform, phenotype data, and processing algorithms will make digital techniques better meet the demands of seed phenotype evaluation, and promote automatic, integrated, and intelligent evaluation of seed phenotype, further helping to lessen the gap between digital techniques and seed phenotyping.
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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.