- 【Updated on May 12, 2025】 Integration of CiNii Dissertations and CiNii Books into CiNii Research
- Trial version of CiNii Research Automatic Translation feature is available on CiNii Labs
- Suspension and deletion of data provided by Nikkei BP
- Regarding the recording of “Research Data” and “Evidence Data”
Estimation of Tiller Number in Rice Using a Field Robot and Deep Learning
-
- SINGH Dhirendranath
- Department of Agricultural and Environmental Engineering, Biotic Environmental Science, The United Graduate School of Agriculture Sciences, Iwate University
-
- MORI Tomohiro
- Department of Agricultural and Environmental Engineering, Biotic Environmental Science, The United Graduate School of Agriculture Sciences, Iwate University
-
- ICHIURA Shigeru
- Department of Agricultural and Environmental Engineering, Biotic Environmental Science, The United Graduate School of Agriculture Sciences, Iwate University
-
- NGUYEN Thanh Tung
- Department of Food, Life and Environment, Faculty of Agriculture, Yamagata University
-
- SASAKI Yuka
- Department of Food, Life and Environment, Faculty of Agriculture, Yamagata University
-
- KATAHIRA Mitsuhiko
- Department of Food, Life and Environment, Faculty of Agriculture, Yamagata University
Bibliographic Information
- Other Title
-
- ─Investigating Effects of Dataset Composition on Tiller Estimation Accuracy─
Search this article
Description
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 %.
Journal
-
- Engineering in Agriculture, Environment and Food
-
Engineering in Agriculture, Environment and Food 15 (2), 47-60, 2022
Asian Agricultural and Biological Engineering Association
- Tweet
Details 詳細情報について
-
- CRID
- 1390294330152444032
-
- ISSN
- 18818366
-
- Text Lang
- en
-
- Data Source
-
- JaLC
- Crossref
- OpenAIRE
-
- Abstract License Flag
- Disallowed