Vol. 40 No. 1 (2026): AHS 150 years
Articles

Non-destructive detection and quantification of embryo presence in Acer monspessulanum seeds using neural networks

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  • S. Karimpour,
  • M. Ahmadi Sarcheshme,
  • S. Karimpour
S. Karimpour
Department of Horticultural Science and Landscape Design, Shirvan Faculty of Agriculture, University of Bojnord, Bojnord, Iran.
M. Ahmadi Sarcheshme
Department of Horticultural Science and Landscape Design, Shirvan Faculty of Agriculture, University of Bojnord, Bojnord, Iran.
S. Karimpour
Instituto Politécnico Nacional, CIC, UPALM-Zacatenco, 07320 Mexico City, Mexico.
DOI: https://doi.org/10.36253/ahsc-18597
Categories

Published 2026-04-07

Keywords

  • Embryo detection,
  • Montpellier maple,
  • seed classification,
  • seed phenotyping,
  • stepwise regression

How to Cite

Karimpour, S., Ahmadi Sarcheshme, M., & Karimpour, S. (2026). Non-destructive detection and quantification of embryo presence in Acer monspessulanum seeds using neural networks. Advances in Horticultural Science, 40(1), 85–97. https://doi.org/10.36253/ahsc-18597

Copyright (c) 2025 Sajede Karimpour

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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Abstract

This study aimed to develop a non-destructive method for predicting embryo presence and quantifying embryo biomass in Acer monspessulanum seeds using morphological and physical traits, combined with machine learning and statistical modeling approaches. Seeds were divided into two groups based on the presence or absence of embryos, and 26 morphological and physical traits were measured. Welch’s t-tests were used to identify traits significantly differed between seeds with and without embryos. A feedforward neural network classifier was trained on these traits to predict embryo presence. Additionally, forward stepwise regression models were constructed to identify key predictors of embryo fresh weight, dry weight, and water content in embryo‐containingseeds. Statistically significant differences (p<0.05) were observed between embryo- containing and empty seeds in traits such as seed weight, length, roundness, Hue, and floating behavior. The neural network classifier achieved 91.03% accuracy, with strong precision (0.92) and recall (0.94) for identifying embryo‐containing seeds. Regression models explained up to 56% of the variation in embryo dry weight and 50% in fresh weight, with seed weight, color parameters, and floatation traits emerging as primary predictors. Embryo water content was primarily predicted by the b* seed coat color parameter (31.6% variance), with additional contributions from buoyancy, perimeter, and post- scarification color traits, yielding a model explaining 45.5% of total variation. The integration of seed phenotyping with neural networks and multiple linear analysis provides a robust, non-destructive method for classifying embryo‐containing seed and predicting embryo development in Acer monspessulanum. This approach offers valuable applications in reforestation, seed banking, and ecological restoration initiatives.

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