Seed purity assessment by means of spectral imaging

Seed purity assessment by means of spectral imaging

In this work, we propose a technique for identifying impurity grains from spectral images using neural networks that is able to analyze a heap of seeds, grouping grains with similar spectral and morphological characteristics and optimizing the main stages of forming a training sample of a neural net...

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Bibliographic Details
Main Authors: G.V. Nesterov, A.V. Guryleva, A.A. Zolotukhina, D.S. Fomin, Y.K. Shashko, A.S. Machikhin
Format: Article
Language:English
Published: Samara National Research University 2025-06-01
Series:Компьютерная оптика
Subjects:
videospectrometry
hyperspectral imaging
digital image processing
spectral characteristics
machine learning
lstm neural network
seed material
agriculture
Online Access:https://computeroptics.ru/eng/KO/Annot/KO49-3/490312e.html
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