Decoupled Geometric Measurement and Machine Learning Classification for Automated Post-Harvest Quality Assessment of <i>Ruscus hypophyllum</i> Foliage

This paper presents a hybrid computer vision framework that explicitly decouples geometric stem measurement from visual foliage condition classification for automated post-harvest quality assessment of <i>Ruscus hypophyllum</i> ornamental foliage. The proposed approach addresses a gap in the literat...

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Hlavní autoři:	Fernando Ortega-Loza, Fernando Toapanta-Ramos, Diego Peña, Moad Hicham Safhi, Diego H. Peluffo-Ordóñez
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	MDPI AG 2026-07-01
Edice:	Horticulturae
Témata:	post-harvest quality assessment ornamental foliage <i>Ruscus hypophyllum</i> machine vision intelligent inspection geometric measurement
On-line přístup:	https://www.mdpi.com/2311-7524/12/8/935
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