http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Wijethunga W.M. Upeksha Darshani (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
-
Wijethunga W.M. Upeksha Darshani,신미희,Jayasooriya L. Sugandhi Hirushika,김경호,박경미,천미건,최상우,김홍림,김진국 한국원예학회 2024 원예과학기술지 Vol.42 No.3
The biochemical changes in mangoes during ripening affect the fruit composition and quality, making these fruits vulnerable to rapid quality deterioration given their climacteric nature. This study examined the effects of an edible chitosan coating and modified atmosphere packaging (MAP) on room-temperature and cold storage conditions in an effort to enhance the quality of the ‘Irwin’ mangoes in two experiments. The 100 μL·L-1 and 500 μL·L-1 chitosan coating treatments used here were applied twice as preharvest treatments. Soon after harvest, mangoes were treated with 500 μL·L-1 chitosan postharvest treatment and 80,000 cc and 150,000 cc MAPs along with control and chitosan pre-treated fruits. The fruits in these cases were kept under room-temperature storage conditions in our first experiment. The control and 100 μL·L-1 and 500 μL·L-1 chitosan-preharvest-treated fruits were kept in cold storage at 7°C for 14 days and then exposed to room-temperature conditions as our second experiment. The physicochemical characteristics and respiration rate were subsequently analyzed. Remarkably, MAP types at room temperature were found to affect quality retention and weight maintenance positively compared to the chitosan treatments. However, the physicochemical characteristics were retained during cold storage, and the weight loss% changed moderately after exposure to room-temperature conditions. The soluble solids content, titratable acidity, and respiration rates were not significant, and firmness was decreased significantly only in the control treatment in the days after cold storage. In addition, apparent chromaticity variations could not be obtained from the front and back sides of the fruits. Notably, our first experiment pinpointed MAPs as ideal for room-temperature storage, while our second experiment highlighted the preharvest chitosan treatments as optimal for cold storage. These emerged as the foremost strategies, significantly enhancing the post-harvest quality and extending the shelf life of fully ripe ‘Irwin’ mangoes.
-
딥러닝 기술을 활용한 복숭아 ‘미황’의 성숙도 자동 분류
Lee Sang Jun,신미희,Jayasooriya L. Sugandhi Hirushika,Wijethunga W.M. Upeksha Darshani,Lee Seul Ki,Cho Jung Gun,Jang Si Hyeong,Cho Byoung-Kwan,김진국 한국원예학회 2024 원예과학기술지 Vol.42 No.1
소비자에게 전달되는 복숭아는 숙도에 따라서 품질이 달라지기 때문에 섭취하기에 적합한 숙도를 고려하여 유통하는 과정이 필요하다. 또한, 숙도는 복숭아의 상품성 및 저장성에 영향을 미칠 수 있어 적합한 수확 시기를 선정하는 작업이 요구되지만, 현재 노지 과수 작목의 숙도 판별에 대한 국내 연구는 미미한 실정이다. 그렇기 때문에 본 연구에서는 딥러닝 객체 탐지 분류모델을 활용하여 복숭아 ‘미황’에 대한 숙도 분류 모델을 개발하였다. 실험실 내부 및 야외에서 촬영된 각 2,800장의 이미지를활용하여 데이터 셋을 구축하였고, 수확 날짜 및 복숭아 과정부(apex)의 색도 a* 값을 기준으로 하는 두 개의 데이터 셋으로구성하여 각 셋의 구분 기준에 따라 미숙, 적숙 그리고 과숙 3개의 class로 분류하였다. Train : Validation : Test 데이터 셋은7 : 2 : 1의 비율로 분류하였고 데이터의 다양성 향상 및 unbalance를 해결하기 위해 augmentation을 실시하였다. 딥러닝 모델은 EfficientNet, YOLOv5 그리고 Vision Transformer를 활용하였으며 EfficientNet에서 가장 우수한 분류 모델 성능을 기록하였다. 날짜 기준 분류 모델은 분류 모델 성능 평가 지표 기준 최저 및 최대 100%의 정확도를 달성하였고, 색도 a* 값 기준 분류모델은 최저 94.7%, 최대 98.2%의 높은 정확도를 보였다. 본 연구에서 개발된 객체 탐지 기반 복숭아 숙도 분류 모델은 향후노지 과수 작목의 숙도 분류를 통한 기계수확 적기 판정 작업에 활용될 수 있을 것으로 판단된다. Peach must be delivered to market when at their proper ripeness, as its fruit quality declines quickly after harvest. Therefore, it is necessary to consider suitable ripeness for consumption and distribution. However, research on ripeness judgments for peaches in the orchard is scarce. This study used deep learning technology to develop a ripeness classification model for ‘Mihwang’ peaches. A dataset was prepared using 2,800 images, each taken from a peach orchard (outside dataset) and a laboratory (inside dataset) with the same fruit. The dataset was constructed based on the harvest date of the peaches and the peach apex’s skin color (a* value). It uses three classes, immature, ripe, and overripe, according to the classification criteria of the two datasets. The model was trained with a ratio of 7:2:1 of training data, validation data, and test data, and image data augmentation was carried out to improve the diversity of the data and to solve any imbalances. Among EfficientNet, YOLOv5, and Vision Transformer, the deep learning model recorded the best classification model performance on EfficientNet. Based on the classification model and performance evaluation index, the harvest-date-based classification model achieved the highest accuracy of 100%. The classification model based on the apex color a* value of peaches showed high accuracy with a minimum rate of 94.7% and a maximum rate of 98.2%. The peach ripeness classification model developed in this study can be used for determining the proper time for the mechanical harvesting of fruit from an orchard.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
