콩 재배 포장의 생육환경 진단을 위한 토양 수분함량 예측 부분최소제곱회귀(Partial Least Squares Regression) 모델 개발
Prediction of Soil Moisture Content for Growth Environment Diagnosis of Soybean Cultivation Field using Partial Least Squares Regression (PLSR) Model
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초록
콩과 같은 밭작물은 주로 토양으로부터 수분을 공급받으며 토양 수분 조건에 따라 생육 반응이 민감하게 반응한다. 작물의 생육과 재배 지역의토양 조건, 기상 등에 따라 적정 토양 수분을 유지하는 것은 작물 생산량의 증가를 위해 중요하다. 따라서, 본 연구에서는 머신러닝 기법을 이용하여토양 수분 함량 예측 모델을 개발하였다. 깊이에 따른 토양 수분과 외기, 강수량 등 기상 변수와의 상관 관계를 구명하고, 깊이별 토양 수분예측을위한 부분최소제곱회귀(PLSR) 모델을 알고리즘을 개발하였다. 콩 재배포장의 10cm, 20cm, 30cm 깊이의 토양수분은 FDR 방식의 센서로 측정하였고, 콩 작물 주변 환경인자(재배환경의 기온, 상대습도, 풍속, 일사량, 일조시간)는 주변의 기상관측소에서 측정된 데이터를 이용하였다. 이를 이용하여깊이별 미래의 토양수분함량 예측 모델을 개발한 결과, 10cm와 20cm깊이에서 주요 인자는 현재 토양수분함량과 기온이었으며, 30cm 깊이에서의주요 인자는 현재 토양수분함량과 기온, 풍속으로 나타났다. 토양 깊이가 깊어짐에 따라 토양수분함량 예측 정확도가 향상되었으며, 이는 표면에가까울수록 토양수분함량이 변화가 크기 때문으로 예상된다. 또한 미래의 토양 수분함량예측시 1시간 후 예측 정확도가 가장 우수하였으며, 이때의Rv 2와 RMSEV가 10cm 깊이에서 0.993와 1.069%, 20cm 깊이에서 0.994와 0.821% 였으며, 30cm 깊이에서 0.999와 0.149% 였다. 본 연구결과는 콩 생육환경 진단을 위해 재배 포장의 토양수분함량을 토양층별로 미래의 토양수분함량도 예측이 가능함을 보여준다.
Abstract
Field crops such as soybeans are mainly supplied with moisture from the soil, and growth responses are sensitive to soil moistureconditions. Maintaining adequate soil moisture content depending on the soil conditions and weather in the cultivation field is importantfor increasing crop yield. Therefore, this study aimed to develop soil moisture content prediction models using a machine learning algorithm. The correlation between soil moisture and meteorological variables such as outdoor air and precipitation according to depth was investigated, and the partial least squares regression (PLSR) model algorithm was developed to predict soil moisture by depth. The soil moistureof 10cm, 20cm, and 30cm depth in the soybean cultivation field was measured using FDR sensors, and the environmental factors surroundingthe soybean crop (temperature, relative humidity, wind speed, solar radiation, and solar radiation) were measured by the nearby weatherstations. As a result of developing a prediction model for the future soil moisture content by depth, the main factors at 10cm and 20cmdepth were the present soil moisture content and temperature, and the main factors at 30cm depth were the present soil moisture content,temperature, and wind speed. As soil depth increased, the accuracy of predicting the soil moisture content is improved, which is expectedto be due to the change in the soil moisture content as it is closer to the surface. In addition, the prediction accuracy of soil moisturecontent after 1 hour was the best, Rv2 and RMSEV were 0.993 and 1.069% for 10cm depth, 0.994 and 0.821% for 20cm depth, and0.999 and 0.149% for 30cm depth. The results of this study indicate that the future soil moisture content of the cultivation field canbe predicted by soil depth to diagnose the soybean growth environment.
참고문헌 (16)
- Ahmad S, "Estimating soil moisture using remote sensing data : A machine learning approach", Advances in Water Resources, 33(1), pp.69~80 (2010)
- Bao Y, "Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model", International J. of Applied Earth Observation and Geoinformation, 72, pp.76~85 (2018)
- Boursianis AD, "Smart irrigation system for precision agriculture-The AREThOU5A IoT Platform", IEEE Sensors J, 21(16), pp.17539~17547 (2021)
- Cáceres, G, "Smart farm irrigation : Model predictive control for economic optimal irrigation in agriculture", Agronomy, 11(9), pp.1~18 (2021)
- Choi J, "AI Technology analysis using partial least square regression", J. of the Korea Society of Computer and Information, 25(3), pp.109~115 (2020)
- Choi SK, "Uniformity assessment of soil moisture redistribution for drip irrigation", J. of The Korean Society of Agricultural Engineers, 54(3), pp.19~28 (2012)
- Fang K, "The value of SMAP for long-term soil moisture estimation with the help of deep learning", IEEE Transactions on Geoscience and Remote Sensing, 57(4), pp.2221~2233 (2019)
- Huang X, "Soil moisture dynamics within soil profiles and associated environmental controls", Catena, 136, pp.189~196 (2016)
- Kim MY, "A study on appropriate irrigation technology for the construction of smart farm in the field-Appropriate soil moisture management through sensing and modeling", Magazine of the Korean Society of Agricultural Engineers, 58(4), pp.30~37 (2016)
- Lee S, "Evaluation of the behavior and quality in soil moisture data : A case study of Yongdam study watershed", J. Korea Water Resour. Assoc, 52(12), pp.951~962 (2019)
- Liu M, "Research and prediction of yellow soil moisture content in Guizhou province based on ARIMA model", Advanced Materials Research, 690, pp.3076~3081 (2013)
- Liu Y, "Generating high-resolution daily soil moisture by using spatial downscaling techniques : A comparison of six machine learning algorithms", Advances in Water Resources, 141, pp.103601~ (2020)
- Natsagdorj E, "Spatial distribution of soil moisture in mongolia using smap and modis satellite data : A time series model(2010-2025)", Remote Sensing, 13(3), pp.1~22 (2021)
- Peng J, "A review of spatial downscaling of satellite remotely sensed soil moisture", Reviews of Geophysics, 55(2), pp.341~366 (2017)
- Qu Y, "Soil moisture investigation utilizing machine learning approach based experimental data and Landsat5-TM images : A case study in the Mega City Beijing", Water(Switzerland), 10(4), pp.423~ (2018)
- Seo MC, "The development of estimation model (AFKAE0.5) for water balance and soil water content using daily weather data", Korean J. of Soil Science and Fertilizer, 45(6), pp.1203~1210 (2012)