| Title: | OS23-2 Automated Classification of High-Grade Dried Shiitake Mushrooms Using Machine Learning |
|---|---|
| Publication: | ICAROB2025 |
| Volume: | 30 |
| Pages: | 613-617 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2025.OS23-2 |
| Author(s): | Leona Kimura, Ota Hamasuna, Kaoru Ohe, Satoshi Ikeda, Kenji Aoki, Amane Takei, Akihiro Kudo, Kazuhide Sugimoto, Makoto Sakamoto |
| Publication Date: | February 13, 2025 |
| Keywords: | Autoencoders (AE), Convolutional Neural Networks (CNN), Model Optimization, Shiitake Mushrooms |
| Abstract: | This study aims to automate shiitake mushroom sorting using an anomaly detection system with Autoencoders (AE) trained on acceptable product data. Initial experiments using CNN approaches highlighted challenges in achieving high accuracy for acceptable product classification, necessitating improvement. The AE-based approach showed progress in detecting defective products via data cleansing, augmentation, and training optimization. However, misclassification of acceptable products with features like darker areas or complex textures remains an issue. This presentation outlines current findings and strategies, including data expansion and model improvements, to address these challenges. |
| PDF File: | https://alife-robotics.co.jp/members2025/icarob/data/html/data/OS/OS23/OS23-2.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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