著者

内田 誠一 Seiichi Uchida

出版者

安田女子大学大学院

雑誌

安田女子大学大学院紀要 = The journal of the Graduate School, Yasuda Women's University (ISSN:24323772)

巻号頁・発行日

no.24, pp.91-98, 2019-03-31

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久邇宮朝彦親王が広島御謫居中に揮毫された新出の和歌短冊について紹介し、短冊の内容について分析・考察する。和歌の内容から、親王が広島にまず御到着になられたのが宮島であったこと、宮島から浅野家の別邸である古江の翠江園にお入りになった可能性が高いことを論じた。また、京都御還住以後に揮毫された、これまた新出の和歌短冊を紹介し、通行の親王歌集と文字の異同があることを指摘した。

著者

Maria Suzuki Kanae Masuda Hideaki Asakuma Kouki Takeshita Kohei Baba Yasutaka Kubo Koichiro Ushijima Seiichi Uchida Takashi Akagi

出版者

The Japanese Society for Horticultural Science

雑誌

The Horticulture Journal (ISSN:21890102)

巻号頁・発行日

pp.UTD-323, (Released:2022-05-25)

被引用文献数

6

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In contrast to the progress in the research on physiological disorders relating to shelf life in fruit crops, it has been difficult to non-destructively predict their occurrence. Recent high-tech instruments have gradually enabled non-destructive predictions for various disorders in some crops, while there are still issues in terms of efficiency and costs. Here, we propose application of a deep neural network (or simply deep learning) to simple RGB images to predict a severe fruit disorder in persimmon, rapid over-softening. With 1,080 RGB images of ‘Soshu’ persimmon fruits, three convolutional neural networks (CNN) were examined to predict rapid over-softened fruits with a binary classification and the date to fruit softening. All of the examined CNN models worked successfully for binary classification of the rapid over-softened fruits and the controls with > 80% accuracy using multiple criteria. Furthermore, the prediction values (or confidence) in the binary classification were correlated to the date to fruit softening. Although the features for classification by deep learning have been thought to be in a black box by conventional standards, recent feature visualization methods (or “explainable” deep learning) has allowed identification of the relevant regions in the original images. We applied Grad-CAM, Guided backpropagation, and layer-wise relevance propagation (LRP), to find early symptoms for CNNs classification of rapid over-softened fruits. The focus on the relevant regions tended to be on color unevenness on the surface of the fruit, especially in the peripheral regions. These results suggest that deep learning frameworks could potentially provide new insights into early physiological symptoms of which researchers are unaware.

著者

内田 誠一 Seiichi Uchida

出版者

安田女子大学

雑誌

安田女子大学紀要 = Journal of Yasuda Women's University (ISSN:02896494)

巻号頁・発行日

vol.44, pp.21-29, 2015-11-20

著者

内田 誠一 Seiichi Uchida

出版者

安田女子大学

雑誌

安田女子大学紀要 = Journal of Yasuda Women's University (ISSN:02896494)

巻号頁・発行日

no.45, pp.210-218, 2017-02-20