Korean Journal of Agricultural and Forest Meteorology (한국농림기상학회지)

Korean Society of Agricultural and Forest Meteorology (한국농림기상학회)
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Predicting the Pre-Harvest Sprouting Rate in Rice Using Machine Learning

기계학습을 이용한 벼 수발아율 예측

  • Ban, Ho-Young (Division of Crop Physiology and Production, National Institute of Crop Science, Rural development Administrarion) ;
  • Jeong, Jae-Hyeok (Division of Crop Physiology and Production, National Institute of Crop Science, Rural development Administrarion) ;
  • Hwang, Woon-Ha (Division of Crop Physiology and Production, National Institute of Crop Science, Rural development Administrarion) ;
  • Lee, Hyeon-Seok (Division of Crop Physiology and Production, National Institute of Crop Science, Rural development Administrarion) ;
  • Yang, Seo-Yeong (Division of Crop Physiology and Production, National Institute of Crop Science, Rural development Administrarion) ;
  • Choi, Myong-Goo (Division of Crop Physiology and Production, National Institute of Crop Science, Rural development Administrarion) ;
  • Lee, Chung-Keun (Division of Crop Physiology and Production, National Institute of Crop Science, Rural development Administrarion) ;
  • Lee, Ji-U (Grain Research Division, Gangwondo Agricultural Research and Extension Services) ;
  • Lee, Chae Young (Grain Research Division, Chungcheongbuk-do Agricultural Research and Extension Services) ;
  • Yun, Yeo-Tae (Grain Research Division, Chungcheongnam-do Agricultural Research & Extension Services) ;
  • Han, Chae Min (Grain Research Division, Gyeongsangbuk-do Agricultural Research & Extension Services) ;
  • Shin, Seo Ho (Food crop research center, Jeollanamdo Agricultural Research & Extension Services) ;
  • Lee, Seong-Tae (Grain Research Division, Gyeongsangnam-do Agricultural Research & Extension Services)
  • 반호영 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 정재혁 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 황운하 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 이현석 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 양서영 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 최명구 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 이충근 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 이지우 (강원도농업기술원 작물연구과) ;
  • 이채영 (충청북도농업기술원 작물연구과) ;
  • 윤여태 (충청남도농업기술원 작물연구과) ;
  • 한채민 (경상북도농업기술원 작물연구과) ;
  • 신서호 (전라남도농업기술원 식량작물연구소) ;
  • 이성태 (경상남도농업기술원 작물연구과)
  • Received : 2020.07.20
  • Accepted : 2020.11.09
  • Published : 2020.12.30
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Abstract

Rice flour varieties have been developed to replace wheat, and consumption of rice flour has been encouraged. damage related to pre-harvest sprouting was occurring due to a weather disaster during the ripening period. Thus, it is necessary to develop pre-harvest sprouting rate prediction system to minimize damage for pre-harvest sprouting. Rice cultivation experiments from 20 17 to 20 19 were conducted with three rice flour varieties at six regions in Gangwon-do, Chungcheongbuk-do, and Gyeongsangbuk-do. Survey components were the heading date and pre-harvest sprouting at the harvest date. The weather data were collected daily mean temperature, relative humidity, and rainfall using Automated Synoptic Observing System (ASOS) with the same region name. Gradient Boosting Machine (GBM) which is a machine learning model, was used to predict the pre-harvest sprouting rate, and the training input variables were mean temperature, relative humidity, and total rainfall. Also, the experiment for the period from days after the heading date (DAH) to the subsequent period (DA2H) was conducted to establish the period related to pre-harvest sprouting. The data were divided into training-set and vali-set for calibration of period related to pre-harvest sprouting, and test-set for validation. The result for training-set and vali-set showed the highest score for a period of 22 DAH and 24 DA2H. The result for test-set tended to overpredict pre-harvest sprouting rate on a section smaller than 3.0 %. However, the result showed a high prediction performance (R2=0.76). Therefore, it is expected that the pre-harvest sprouting rate could be able to easily predict with weather components for a specific period using machine learning.

본 연구는 자연 조건에서 쌀가루용 벼의 수발아율을 예측하기 위한 것으로 기계학습을 이용하여 기상요소들에 따른 수발아율을 간단히 예측할 수 있는 초기 시스템을 개발하기 위해 수행되었다. 이를 위하여 강원도, 충청북도, 경상북도에 위치한 6개 지역에서 쌀가루용 벼 3품종을 재배하였다. 수확 후 수발아율과 출수일을 조사하였으며, 각 지역의 종관기상대의 일평균 기온과 상대 습도, 그리고 강수량 정보를 이용하여 기계학습 모델 중 하나이며, 정확도가 높은 GBM 모델로 수발아율을 예측하였다. 2017년부터 2019년까지 강원과 충북, 그리고 경북의 6개 지역에서 쌀가루 용 벼 3품종에 대해 재배 실험을 수행하였다. 조사 항목은 출수일과 수발아율이었다. 기상자료는 동일한 지역명의 종관기상대를 이용하여 일 평균 기온 및 상대 습도, 그리고 강수량 자료를 수집하였다. 수발아율 예측을 위해 기계학습 모델인 Gradient Boosting Machine (GBM)을 이용하였으며, 학습 투입 변수로는 평균 기온과 상대 습도, 그리고 총 강수량이었다. 또한 수발아 피해 관련 기간을 설정하기 위해 출수 후 몇일 후부터 그 이후의 기간에 대한 실험도 수행하였다. 자료는 수발아 피해 관련 기간의 교정을 위한 training-set과 vali-set, 검증을 위한 test-set으로 구분하였다. training-set과 vali-set으로 교정한 결과, 출수 후 22일 후부터 24일동안에서 가장 높은 score를 나타내었다. test-set으로 검증한 결과는 3.0%보다 낮은 구간에서 수발아율을 약간 높게 예측한 경향이 있었지만, 높은 예측력을 보였다(R2=0.76). 따라서, 기계학습을 이용하여 특정기간동안의 기상요소들로 수발아율을 간단하게 예측할 수 있을 것으로 예상된다. 본 연구의 결과를 종합해 볼 때, 기계학습을 이용하여 특정 기간 동안에 평균 기온과 상대 습도, 그리고 총 강수량으로 높은 수발아율 예측 성능을 보였으며, 이 시스템을 이용하여 일반 농가들을 대상으로 수발아에 관한 피해를 예방할 수 있는 조기 수발아 예측 시스템으로 이용가능 할 것으로 판단된다. 하지만 품종마다 휴면 정도 차이로 인한 수발아 관련 기간에 차이가 있으므로, 다른 쌀가루용 벼 품종에 대해서도 추가로 조사하고, 개별 품종으로 세분화하여 분석한다면 좀 더 정확도 높은 예측 시스템을 개발할 수 있을 것으로 판단된다.

Keywords

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