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| Bibliography | Contents |
|---|---|
| Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review Hwang, Sung-Wook; Sugiyama, Junji (2021) Plant Methods, 17 |  |
| RoseTracker: A system for automated rose growth monitoring Shinoda, Risa; Motoki, Ko; Hara, Kensho; Kataoka, Hirokatsu; Nakano, Ryohei; Nakazaki, Tetsuya; Noguchi, Ryozo (2023-10) Smart Agricultural Technology, 5 | |
| Potential of machine learning approaches for predicting mechanical properties of spruce wood in the transverse direction Chen, Shuoye; Shiina, Rei; Nakai, Kazushi; Awano, Tatsuya; Yoshinaga, Arata; Sugiyama, Junji (2023-06-25) Journal of Wood Science, 69 | |
| Cultivar discrimination of litchi fruit images using deep learning Osako, Yutaro; Yamane, Hisayo; Lin, Shu-Yen; Chen, Po-An; Tao, Ryutaro (2020-07-27) Scientia Horticulturae, 269 | |
| Implementation of deep-learning algorithm for obstacle detection and collision avoidance for robotic harvester Li, Yang; Iida, Michihisa; Suyama, Tomoya; Suguri, Masahiko; Masuda, Ryohei (2020-07) Computers and Electronics in Agriculture, 174 | |
| A Study of Deep Learning for Quantitative Analysis of Vitamin A in Cattle Blood Shibasaki, Mizuki; Suzuki, Tetsuhito; Fukushima, Moriyuki; Nagaoka, Shin-ichi; Ogawa, Yuichi; Kondo, Naoshi (2024) Journal of Computer Chemistry, Japan -International Edition, 10 | |
| Fundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learning Li, Nanding; Kondo, Naoshi; Ogawa, Yuichi; Shiraga, Keiichiro; Shibasaki, Mizuki; Pinna, Daniele; Fukushima, Moriyuki; Nagaoka, Shinichi; Fujiura, Tateshi; De, Xuehong; Suzuki, Tetsuhito (2025-02-03) Scientific Reports, 15 | |
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