Molecular dissection of rose and Botrytis cinerea pathosystems affected by ethylene

Ha, Suong Tuyet Thi; Kim, Yong‐Tae; Yeam, Inhwa; Choi, Hyong Woo; In, Byung-Chun

doi:10.1016/j.postharvbio.2022.112104

Cited by 8 publications

(19 citation statements)

References 51 publications

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“…Postharvest conditions, such as dry transport, ethylene or high density of mold spores have been observed to decrease the longevity of the cut roses ( Harkema et al., 2013 ; Ha et al., 2021 ; Ha et al., 2022 ). In this study, dry transport, ethylene exposure, and increased B. cinerea spore (induced by ethylene exposure and fungal conidial inoculation during transport simulation) significantly reduced vase life and positive water balance of cut roses.…”

Section: Discussionmentioning

confidence: 99%

“…Ethylene exposure accelerates the senescence process of cut roses, leading to premature wilting, petal abscission, and overall reduced vase life ( Macnish et al., 2010 ; In et al., 2017 ). During transportation, contact with B. cinerea spores or storage in conditions conductive to fungal growth can lead to infection, resulting in necrotic lesions and decay, ultimately reducing vase life of cut flowers ( Ha et al., 2022 ).…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the vase life of cut roses was considered to end when cut flowers showed severe GMD symptoms in the petals. The progression of GMD by B. cinerea was evaluated based on the disease index as described in the previous study ( Ha et al., 2022 ).…”

Section: Methodsmentioning

confidence: 99%

“…The primer sequences used for quantitative real-time PCR (qRT-PCR) are listed in Table 1 . The qRT-PCR reaction setting and conditions for gene expression analyses have been indicated previously ( Ha et al., 2022 ).…”

Section: Methodsmentioning

confidence: 99%

“…The GMD development speed was accelerated by B. cinerea inoculation and ethylene treatment and also increased in ‘White Beauty’ compared to ‘All For Love’ ( Supplementary Figure 3 ). This evaluation was based on the previous findings showing that ethylene and water stress influenced the progression of GMD in cut roses during transport ( Harkema et al., 2013 ; Ha et al., 2022 ).…”

Section: Methodsmentioning

confidence: 99%

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

Kim,

Ha,

2024

Front. Plant Sci.

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IntroductionHyperspectral 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.MethodsCut 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 characteristic light wavelengths that are closely correlated with plant physiological states based on HSI. The flower bud of cut roses was selected for HSI measurement and the development of a vase life prediction model utilizing YOLOv5.Results and discussionThe HSI results revealed that spectral reflectance between 470 to 680 nm was strongly correlated with gray mold disease (GMD), whereas those between 700 to 900 nm were strongly correlated with flower wilting or vase life. To develop a YOLOv5 prediction model that can be used to anticipate flower longevity, the vase life of cut roses was classed into two categories as over 5 d (+5D) and under 5 d (-5D), based on scoring a grading standard on the flower quality. A total of 3000 images from HSI were forwarded to the YOLOv5 model for training and prediction of GMD and vase life of cut flowers. Validation of the prediction model using independent data confirmed its high predictive accuracy in evaluating the vase life of both ‘All For Love’ (r2 = 0.86) and ‘White Beauty’ (r2 = 0.83) cut flowers. The YOLOv5 model also accurately detected and classified GMD in the cut rose flowers based on the image data. Our results demonstrate that the combination of HSI and deep learning is a reliable method for detecting early GMD infection and evaluating the longevity of cut roses.

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Section: Discussionmentioning

confidence: 99%