• Journal of Bio-Environment Control
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• v.31 no.3
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• pp.237-245
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• 2022

Peach is a typical summer fruit which can be used for various food, processed food, and fragrance ingredients in Korea. Peach is also known as one of serious allergens which make difficulty for farm workers during peach sorting operations. After peach harvesting, it moves to the sorting operations for removing cover material, removing fuzz on peach surface, sorting by size, and packing. The air-samplers and optical particle counters were used to analyze the characteristics of fine dust generation by location and operation characteristics in the experimental peach farms. During removing peach fuzz, the dust concentrations were increased by 6.89 times on total suspended particulate (TSP), 2.13 times on PM-10 (particulate matter), and 1.30 times on PM-2.5 compared to non-working periods, respectively. During removing peach covering materials, the dust concentrations were increased by 3.14 times on TSP, 1.91 times on PM-10, and 1.43 times on PM-2.5 compared to non-working periods, respectively. This represents peach fuzz can be affected to farm workers during peach sorting operations.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1267-1267
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• 2001

The existing fruit sorter has the method of tilting tray and extracting fruits by the action of solenoid or springs. In peaches, the most sort processing is supported by man because the sorter make fatal damage to peaches. In order to sustain commodity and quality of peach non-destructive, non-contact and real time based sorter was needed. This study was performed to develop peach sorter using near-infrared spectroscopy in real time and nondestructively. The prototype was developed to decrease internal and external damage of peach caused by the sorter, which had a way of extracting tray with it. To decrease positioning error of measuring sugar contents in peaches, fiber optic with two direction diverged was developed and attached to the prototype. The program for sorting and operating the prototype was developed using visual basic 6.0 language to measure several quality index such as chlorophyll, some defect, sugar contents. The all sorting result was saved to return farmers for being index of good quality production. Using the prototype, program and MLR(multiple linear regression) model, it was possible to estimate sugar content of peaches with the determination coefficient of 0.71 and SEC of 0.42bx using 16 wavelengths. The developed MLR model had determination coefficient of 0.69, and SEP of 0.49bx, it was better result than single point measurement of 1999's. The peach sweetness grading system based on NIR reflectance method, which consists of photodiode-array sensor, quartz-halogen lamp and fiber optic diverged two bundles for transmitting the light and detecting the reflected light, was developed and evaluated. It was possible to predict the soluble solid contents of peaches in real time and nondestructively using the system which had the accuracy of 91 percentage and the capacity of 7,200 peaches per an hour for grading 2 classes by sugar contents. Draining is one of important factors for production peaches having good qualities. The reason why one farm's product belows others could be estimated for bad draining, over-much nitrogen fertilizer, soil characteristics, etc. After this, the report saved by the peach grading system will have to be good materials to farmers for production high quality peaches. They could share the result or compare with others and diagnose their cultural practice.

• Information Systems Review
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• v.22 no.1
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• pp.113-124
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• 2020

In the operation of farms, it is very important to evaluate the quality of harvested crops and to classify defective products. However, farmers have difficulty coping with the cost and time required for quality assessment due to insufficient capital and manpower. This study thus aims to detect defects by analyzing the epidermis of fruit using deep learning algorithm. We developed a model that can analyze the epidermis by applying YOLOv3 algorithm based on Region Convolutional Neural Network to video images of peach. A total of four classes were selected and trained. Through 97,600 epochs, a high performance detection model was obtained. The crop failure detection model proposed in this study can be used to automate the process of data collection, quality evaluation through analyzed data, and defect detection. In particular, we have developed an analytical model for peach, which is the most vulnerable to external wounds among crops, so it is expected to be applicable to other crops in farming.

• Korean Journal of Microbiology
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• v.53 no.3
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• pp.163-169
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• 2017

Strains D4 and D5 were isolated from peach-rotten soil during the peach harvest season. The isolates were identified based on morphological and biochemical characterization, and identification was determined by 16S rRNA gene sequencing. Results showed that D4 has high similarity to Lactobacillus plantarum ATCC $14917^T$ and Lactobacillus pentosus ATCC $8041^T$ at 99.05% and 98.98%, respectively. D5 was also similar to Lactobacillus pentosus ATCC $8041^T$ and Lactobacillus plantarum ATCC $14917^T$ at 98.71% and 98.64%, respectively. In contrast, isolates showed differences in carbohydrate utilization in comparison to Lactobacillus plantarum ATCC $14917^T$ and Lactobacillus pentosus ATCC $8041^T$. In view of this we performed VITEK MS matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) analysis, multiplex PCR fingerprinting, and random amplified polymorphic DNA (RAPD)-PCR to further confirm the identification of D4 and D5. The results of these analyses showed that both strains were most similar to Lactobacillus plantarum.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.270-278
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• 2022

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.

• Journal of Bio-Environment Control
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• v.22 no.1
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• pp.27-33
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• 2013

In order to provide basic data for the development of a controlled environment cultivation system and standardization of the structures, structural status and improvement methods were investigated for the fruit tree greenhouses of grape, pear, and peach. The greenhouses for citrus and grape cultivation are increasing while pear and persimmon greenhouses are gradually decreasing due to the advance of storage facilities. In the future, greenhouse cultivation will expand for the fruit trees which are more effective in cultivation under rain shelter and are low in storage capability. Fruit tree greenhouses were mostly complying with standards of farm supply type models except for a pear greenhouse and a large single-span peach greenhouse. It showed that there was no greenhouse specialized in each species of fruit tree. Frame members of the fruit tree greenhouses were mostly complying with standards of the farm supply type model or the disaster tolerance type model published by MIFAFF and RDA. In most cases, the concrete foundations were used. The pear greenhouse built with the column of larger cross section than the disaster tolerance type. The pear greenhouse had also a special type of foundation with the steel plate welded at the bottom of columns and buried in the ground. As the results of the structural safety analysis of the fruit tree greenhouses, the grape greenhouses in Gimcheon and Cheonan and the peach greenhouses in Namwon and Cheonan appeared to be vulnerable for snow load whereas the peach greenhouse in Namwon was not safe enough to withstand wind load. The peach greenhouse converted from a vegetable growing facility turned out to be unsafe for both snow and wind loads. Considering the shape, height and planting space of fruit tree, the appropriate size of greenhouses was suggested that the grape greenhouse be 7.0~8.0 m wide and 2.5~2.8 m high for eaves, while 6.0~7.0 m wide and 3.0~3.3 m of eaves height for the pear and peach greenhouses.

• Journal of Biosystems Engineering
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• v.30 no.5 s.112
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• pp.280-284
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• 2005

Front the production on the farm to the consumer, agricultural products are subject to various physical treatments involving mechanical techniques and devices. It is essential to understand the physical laws governing the response of these biological materials so that the machines, processes, and handling operations can be designed fur maximum efficiency and the highest quality of the end products. A compression test system was developed to test the physical properties of fruits including apple, pear, and peach which may lead to a better understanding of the physical laws. The test system consisted of a digital storage oscilloscope and simple mechanism which can apply quasi-static compression to fresh fruits. Rupture force, energy, and deformation were measured at the five levels of compression speed from 1.25 to 62.5 mm/min for each internal and external tissues. Rupture forces for apple and pear were in the range of 42.2 to 46.2 N and 38.8 to 41.2 N for external and infernal tissues, respectively. Rupture forces fir peach external tissues were in the range of 48.2 to 54.0 N.

• Korean Journal of Environmental Agriculture
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• v.28 no.4
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• pp.356-364
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• 2009

In 2006 and 2007, there was a big outbreak of the Ussur Brown Katydid, Paratlanticus ussurriensis in the central part of Korea attacking some orchard trees. Until 2000, the katydid had not been regarded as an agricultural pest because they were distributed widely in Korea with low population density and their habitats were confined mainly to hillsides of forested areas. The fact that katydid attacked orchard trees with a higher population density seemed to be related to a change in feeding environment. And the shift of their habitats from oak woodlands to commercial orchards was thought to be related to the nutritional contents of their feed. In an attempt to understand these relationships, we conducted an ecological study of the affected areas. When the katydids changed their habitats in early May of 2008 and 2009, they shifted their host plants from oak trees to peach trees. The habitat shift was closely related to the nitrogen (N) content of the host plant leaves. When katydid moved to the hillside adjacent to orchard farm, N content of oak tree leaves decreased dramatically from 5.3% to 2.2%. At that time N content of peach tree leaves were higher than the 2.2% of oak leaves, showing 3.5~5.0%. This range of N content of peach tree leaves has been consistent until late June. And feed preference analysis carried out in the laboratory showed that katydid prefered peach tree leaves to peach fruit to oak tree leaves.

• Journal of Biosystems Engineering
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• v.30 no.5 s.112
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• pp.274-279
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• 2005

Impact is one of the major cause of damage to fruits druing varios processes from the production on the farm to the consumer. The tissue of fruits are ruptured in a very short period time less than 10ms by impact loading. Mechanical behavior of fruits under impact loading can be analyzed better with high speed sampling data acquisition system and one of them is a digital storage oscilloscope. A impact test system was developed to test the physical properties of fruits including apple, pear, and peach which may lead to a better understanding of the physical laws. The test system consisted of a digital storage oscilloscope and simple mechanism which can apply impact force to fresh produce. Rupture force, energy, and deffrmation were measured at the five levels of drop heights from 4 to 24cm fur each internal and external tissues. Rupture forces for apple and pear were in the range of 72.9 to 87.7 N and 70.8 to 84.1 N for external and internal tissues, respectively. Rupture forces far peach external tissues were in the range of 43.4 to 65.0 N.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.3
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• pp.124-131
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• 2012

This study was carried out to evaluate a possible change in freeze risk for 'Changhowon Hwangdo' peach buds in three major peach growing areas under the future climate projected by RCP8.5 emission scenario. Mean values of the monthly temperature data for the present decade (2000s) and the future decades (2020s, 2050s, 2080s) were extracted for farm lands in Icheon, Chungju, and Yeongcheon-Gyeongsan region at 1km resolution and 30 sets of daily temperature data were generated randomly by a stochastic process for each decade. The daily data were used to calculate a thermal time-based dormancy depth index which is closely related to the cold tolerance of peach buds. Combined with daily minimum temperature, dormancy depth can be used to estimate the potential risk of freezing damage on peach buds. When the freeze risk was calculated daily for the winter period (from 1 November to 15 March) in the present decade, Icheon and Chungju regions had high values across the whole period, but Yeongcheon-Gyeongsan regions had low values from mid-December to the end of January. In the future decades, the frequency of freezing damage would be reduced in all 3 regions and the reduction rate could be as high as 75 to 90% by 2080's. However, the severe class risk (over 80% damage) will not disappear in the future and most occurrences will be limited to December to early January according to the calculation. This phenomenon might be explained by shortened cold hardiness period caused by winter warming as well as sudden cold waves resulting from the higher inter-annual climate variability projected by the RCP8.5 scenario.