ArticleFrontiers in plant science2022
An Approach Using Emerging Optical Technologies and Artificial Intelligence Brings New Markers to Evaluate Peanut Seed Quality.
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 10 papers.
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Who cites it
10 citing papers in PubMed.
- Integrating AI in seed science: Toward an intelligent design paradigm.Plant communications · 2026Review
- Non-destructive detection strategy of maize seed vigor based on seed phenotyping and the potential for accelerating breeding.Journal of advanced research · 2025Article
- Towards resilience: Transcriptional insights on flavonoid biosynthesis during peanut seed maturation phases.PloS one · 2025Article
- Tropical peanut maturation scale for harvesting seeds with superior quality.Frontiers in plant science · 2024Article
- Vigour testing for the rice seed with computer vision-based techniques.Frontiers in plant science · 2023Article
- POMONA: a multiplatform software for modeling seed physiology.Frontiers in plant science · 2023Article
- Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality.Frontiers in plant science · 2023Article
- Transcripts Expressed during GerminationPlants (Basel, Switzerland) · 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
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Seeds of high physiological quality are defined by their superior germination capacity and uniform seedling establishment. Here, it was investigated whether multispectral images combined with machine learning models can efficiently categorize the quality of peanut seedlots. The seed quality from seven lots was assessed traditionally (seed weight, water content, germination, and vigor) and by multispectral images (area, length, width, brightness, chlorophyll fluorescence, anthocyanin, and reflectance: 365 to 970 nm). Seedlings from the seeds of each lot were evaluated for their photosynthetic capacity (fluorescence and chlorophyll index, F
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