Extensive research has been performed on the in-field nondestructive evaluation (NDE) of the physicochemical properties of ‘Madoka’ peaches, such as chromaticity (a*), soluble solids content (SSC), firmness, and titratable acidity (TA) content. To accomplish this, a snapshot-based hyperspectral imaging (HSI) approach for filed application was conducted in the visible and near-infrared (Vis/NIR) region. The hyperspectral images of ‘Madoka’ samples were captured and combined with commercial HSI analysis software, and then the physicochemical properties of the ‘Madoka’ samples were predicted. To verify the performance of the field-based HSI application, a lab-based HSI application was also conducted, and their coefficient of determination values (R2) were compared. Finally, pixel-based chemical images were produced to interpret the dynamic changes of the physicochemical properties in ‘Madoka’ peach. Consequently, the a* values and SSC content shows statistically significant R2 values (0.84). On the other hand, the firmness and TA content shows relatively lower accuracy (R2 = 0.6 to 0.7). Then, the resultant chemical images of the a* values and SSC content were created and could represent their different levels using grey scale gradation. This indicates that the HSI system with integrated HSI software used in this work has promising potential as an in-field NDE for analyzing the physicochemical properties in ‘Madoka’ peaches.

This study was conducted using deep learning technology to classify for 'Mihwang' peach maturity with RGB images and fruit quality attributes during fruit development and maturation periods. The 730 images of peach were used in the training data set and validation data set at a ratio of 8:2. The remains of 170 images were used to test the deep learning models. In this study, among the fruit quality attributes, firmness, Hue value, and a* value were adapted to the index with maturity classification, such as immature, mature, and over mature fruit. This study used the CNN (Convolutional Neural Networks) models for image classification; VGG16 and InceptionV3 of GoogLeNet. The performance results show 87.1% and 83.6% with Hue left value in VGG16 and InceptionV3, respectively. In contrast, the performance results show 72.2% and 76.9% with firmness in VGG16 and InceptionV3, respectively. The loss rate shows 54.3% and 62.1% with firmness in VGG16 and InceptionV3, respectively. It considers increasing for adapting a field utilization with firmness index in peach.

Persimmons are one of the most important export fruits in South Korea, where several tons are exported across the globe each year. In this study, the quality attributes of ‘Wonmi’ persimmon fruits were evaluated during an export simulation at 0 °C, 10 °C, and 24 °C with a combination of 1-Methylcyclopropene (1-MCP) and modified atmosphere packaging (MAP) treatments. The relative humidity during the export simulation was greater at room temperature (75–92%) and 0 °C (85% to 93%) than at 10 °C (42% to 60%). The results show that the application of 1-MCP and MAP treatments during the export simulation were effective in delaying the ripening of ‘Wonmi’ persimmons by reducing respiration and ethylene production. The suppressed expression of ethylene synthesis genes and cell wall modification genes reduced the ethylene production and maintain the fruit firmness, respectively. In addition, 1-MCP and MAP treatments were effective in maintaining SSC and color of ‘Wonmi’ persimmon fruits during the export simulation. Thus, by adopting these treatments, the overall quality of persimmon exports from South Korea can significantly improve.

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