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
Kajian ini cuba meramalkan tarikh kemunculan telinga pokok padi, berdasarkan rekod tanaman sepanjang 25 tahun. Meramalkan kemunculan tanaman padi diketahui penting untuk mengamalkan kualiti penuaian yang baik, dan telah lama bergantung kepada petani tua yang memperoleh kemahiran ramalan intuitif berdasarkan pengalaman jangka panjang mereka. Berdepan dengan petani yang semakin tua, pendekatan berasaskan data untuk ramalan telah diteruskan. Namun begitu, ia tidak semestinya mencukupi dari segi penggunaan praktikal. Salah satu isu ialah menggunakan ramalan cuaca sebagai ciri supaya prestasi ramalan diubah mengikut ketepatan ramalan. Isu lain ialah prestasi dipelbagaikan mengikut rantau dan ciri serantau belum digunakan sebagai ciri untuk ramalan. Dengan latar belakang ini, kami mencadangkan kejuruteraan ciri untuk mengukur ciri serantau tersembunyi sebagai ciri untuk ramalan. Selanjutnya ciri ini direkayasa hanya berdasarkan data pemerhatian tanpa sebarang ramalan. Menggunakan cadangan kami pada data pada rekod pemangkasan menghasilkan prestasi ramalan yang mencukupi, ±2.69 hari RMSE.
Hiroshi UEHARA
Rissho University
Yasuhiro IUCHI
Akita Prefectural University
Yusuke FUKAZAWA
Waseda University
Yoshihiro KANETA
Akita Prefectural University
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Salinan
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
Salinan
@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},}
Salinan
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 -