The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 연구에서는 25년간의 농사 기록을 바탕으로 벼의 이삭 출현 날짜를 예측하려고 합니다. 벼의 이삭 출현 예측은 좋은 수확 품질을 구현하는 데 중요한 것으로 알려져 있으며, 오랜 경험을 바탕으로 직관적인 예측 기술을 습득하는 노년층 농부에게 오랫동안 의존해 왔습니다. 고령화 농민에 직면하여 예측을 위한 데이터 기반 접근 방식이 추구되었습니다. 그럼에도 불구하고, 실제 사용 측면에서 반드시 충분하지는 않습니다. 문제 중 하나는 일기예보를 기능으로 채택하여 예측의 정확도에 따라 예측 성능이 달라지는 것입니다. 또 다른 문제는 지역별로 성과가 다르고, 지역적 특성이 예측의 특징으로 활용되지 않았다는 점이다. 이러한 배경을 바탕으로 우리는 숨겨진 지역적 특성을 예측의 특징으로 정량화하는 특징공학을 제안합니다. 또한 이 기능은 예측 없이 관찰 데이터만을 기반으로 설계되었습니다. 우리의 제안을 자르기 기록 데이터에 적용하면 RMSE ±2.69일의 충분한 예측 성능을 얻을 수 있었습니다.
Hiroshi UEHARA
Rissho University
Yasuhiro IUCHI
Akita Prefectural University
Yusuke FUKAZAWA
Waseda University
Yoshihiro KANETA
Akita Prefectural University
전자농업, 작물 예측, 기능 엔지니어링, 은닉 마르코프 모델, 지식 습득
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부
Hiroshi UEHARA, Yasuhiro IUCHI, Yusuke FUKAZAWA, Yoshihiro KANETA, "Predicting A Growing Stage of Rice Plants Based on The Cropping Records over 25 Years — A Trial of Feature Engineering Incorporating Hidden Regional Characteristics —" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 5, pp. 955-963, May 2022, doi: 10.1587/transinf.2021DAP0013.
Abstract: This study tries to predict date of ear emergence of rice plants, based on cropping records over 25 years. Predicting ear emergence of rice plants is known to be crucial for practicing good harvesting quality, and has long been dependent upon old farmers who acquire skills of intuitive prediction based on their long term experiences. Facing with aging farmers, data driven approach for the prediction have been pursued. Nevertheless, they are not necessarily sufficient in terms of practical use. One of the issue is to adopt weather forecast as the feature so that the predictive performance is varied by the accuracy of the forecast. The other issue is that the performance is varied by region and the regional characteristics have not been used as the features for the prediction. With this background, we propose a feature engineering to quantify hidden regional characteristics as the feature for the prediction. Further the feature is engineered based only on observational data without any forecast. Applying our proposal to the data on the cropping records resulted in sufficient predictive performance, ±2.69days of RMSE.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021DAP0013/_p
부
@ARTICLE{e105-d_5_955,
author={Hiroshi UEHARA, Yasuhiro IUCHI, Yusuke FUKAZAWA, Yoshihiro KANETA, },
journal={IEICE TRANSACTIONS on Information},
title={Predicting A Growing Stage of Rice Plants Based on The Cropping Records over 25 Years — A Trial of Feature Engineering Incorporating Hidden Regional Characteristics —},
year={2022},
volume={E105-D},
number={5},
pages={955-963},
abstract={This study tries to predict date of ear emergence of rice plants, based on cropping records over 25 years. Predicting ear emergence of rice plants is known to be crucial for practicing good harvesting quality, and has long been dependent upon old farmers who acquire skills of intuitive prediction based on their long term experiences. Facing with aging farmers, data driven approach for the prediction have been pursued. Nevertheless, they are not necessarily sufficient in terms of practical use. One of the issue is to adopt weather forecast as the feature so that the predictive performance is varied by the accuracy of the forecast. The other issue is that the performance is varied by region and the regional characteristics have not been used as the features for the prediction. With this background, we propose a feature engineering to quantify hidden regional characteristics as the feature for the prediction. Further the feature is engineered based only on observational data without any forecast. Applying our proposal to the data on the cropping records resulted in sufficient predictive performance, ±2.69days of RMSE.},
keywords={},
doi={10.1587/transinf.2021DAP0013},
ISSN={1745-1361},
month={May},}
부
TY - JOUR
TI - Predicting A Growing Stage of Rice Plants Based on The Cropping Records over 25 Years — A Trial of Feature Engineering Incorporating Hidden Regional Characteristics —
T2 - IEICE TRANSACTIONS on Information
SP - 955
EP - 963
AU - Hiroshi UEHARA
AU - Yasuhiro IUCHI
AU - Yusuke FUKAZAWA
AU - Yoshihiro KANETA
PY - 2022
DO - 10.1587/transinf.2021DAP0013
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2022
AB - This study tries to predict date of ear emergence of rice plants, based on cropping records over 25 years. Predicting ear emergence of rice plants is known to be crucial for practicing good harvesting quality, and has long been dependent upon old farmers who acquire skills of intuitive prediction based on their long term experiences. Facing with aging farmers, data driven approach for the prediction have been pursued. Nevertheless, they are not necessarily sufficient in terms of practical use. One of the issue is to adopt weather forecast as the feature so that the predictive performance is varied by the accuracy of the forecast. The other issue is that the performance is varied by region and the regional characteristics have not been used as the features for the prediction. With this background, we propose a feature engineering to quantify hidden regional characteristics as the feature for the prediction. Further the feature is engineered based only on observational data without any forecast. Applying our proposal to the data on the cropping records resulted in sufficient predictive performance, ±2.69days of RMSE.
ER -