外文摘要：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.
外文关键词：YOLOv5;Vision transformer;EfficientNet;fruit firmness;robot harvester
作者：Cho, Byoung-Kwan;Lee, Sang Jun;Shin, Mi Hee;Jayasooriya, L Sugandhi Hirushika;Wijethunga, W M Upeksha Darshani;Lee, Seul Ki;Cho, Jung Gun;Jang, Si Hyeong;Kim, Jin Gook
作者单位：Gyeongsang Natl Univ;Chungnam Natl Univ;Natl Inst Hort & Herbal Sci
期刊名称：HORTICULTURAL SCIENCE & TECHNOLOGY
期刊影响因子：0.0
出版年份：2024
出版刊次：42（1）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台