Development of a longevity prediction model for cut roses using hyperspectral imaging and a convolutional neural network.

ABSTRACT:

Introduction

Hyperspectral imaging (HSI) and deep learning techniques have been widely applied to predict postharvest quality and shelf life in multiple horticultural crops such as vegetables, mushrooms, and fruits; however, few studies show the application of these techniques to evaluate the quality issues of cut flowers. Therefore, in this study, we developed a non-contact and rapid detection technique for the emergence of gray mold disease (GMD) and the potential longevity of cut roses using deep learning techniques based on HSI data.

Methods

Cut flowers of two rose cultivars ('All For Love' and 'White Beauty') underwent either dry transport (thus impaired cut flower hydration), ethylene exposure, or Botrytis cinerea inoculation, in order to identify the characteri

SUBMITTER: Kim YT 

PROVIDER: S-EPMC10809400 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature