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- SINGH Dhirendranath
- Department of Agricultural and Environmental Engineering, Biotic Environmental Science, The United Graduate School of Agriculture Sciences, Iwate University
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- MORI Tomohiro
- Department of Agricultural and Environmental Engineering, Biotic Environmental Science, The United Graduate School of Agriculture Sciences, Iwate University
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- ICHIURA Shigeru
- Department of Agricultural and Environmental Engineering, Biotic Environmental Science, The United Graduate School of Agriculture Sciences, Iwate University
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- NGUYEN Thanh Tung
- Department of Food, Life and Environment, Faculty of Agriculture, Yamagata University
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- SASAKI Yuka
- Department of Food, Life and Environment, Faculty of Agriculture, Yamagata University
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- KATAHIRA Mitsuhiko
- Department of Food, Life and Environment, Faculty of Agriculture, Yamagata University
書誌事項
- タイトル別名
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- ─Investigating Effects of Dataset Composition on Tiller Estimation Accuracy─
この論文をさがす
説明
Tiller number, an important growth parameter for rice cultivation, is still being assessed manually. This work investigated the influence of dataset composition on performance of deep learning models for tiller number estimation in rice. Four datasets were constructed for early tillering, active tillering, and maximum tillering by applying the concepts of mixed varieties, class balance, and data augmentation. YOLOv4 models were trained to estimate tiller numbers using each constructed dataset. Then their performance was evaluated. Results demonstrated that the models trained with datasets created using a combination of mixed variety, class balance, and augmentation showed the best performance for estimating the tiller number at the three tillering stages with a mAP range of 68.8–86.4 %.
収録刊行物
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- Engineering in Agriculture, Environment and Food
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Engineering in Agriculture, Environment and Food 15 (2), 47-60, 2022
Asian Agricultural and Biological Engineering Association
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詳細情報 詳細情報について
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- CRID
- 1390294330152444032
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- ISSN
- 18818366
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- OpenAIRE
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- 抄録ライセンスフラグ
- 使用不可