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

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 inoculation, in order to identify the characteristic light wavelengths that are closely correlated with plant physiological states based on HSI.

Early Detection of Infection in Cut Roses Using Thermal Imaging.

() causes gray mold disease (GMD), which results in physiological disorders in plants that decrease the longevity and economic value of horticultural crops. To prevent the spread of GMD during distribution, a rapid, early detection technique is necessary. Thermal imaging has been used for GMD detection in various plants, including potted roses; however, its application to cut roses, which have a high global demand, has not been established.

Regulation of Infection and Gene Expression in Cut Roses by Using Nano Silver and Salicylic Acid.

() is one of the necrotrophic pathogens resulting in the heaviest commercial losses in cut rose flowers, and the severity of gray mold disease partly depends on the presence of ethylene during the storage and transport. The effectiveness of nano silver (NS) and salicylic acid (SA) was assessed as a novel control agent in protecting the cut rose flowers against infection and ethylene damages. The efficacy of NS and SA was compared with an inoculated control (CON).