Evaluation of artificial intelligence algorithms with sensor data assimilation in estimating crop evapotranspiration and crop water stress index for irrigation water management

Abia Katimbo; Daran R. Rudnick; Jingwen Zhang; Yufeng Ge; Kendall C. DeJonge; Trenton E. Franz; Yeyin Shi; Wei-zhen Liang; Xin Qiao; Derek M. Heeren; Isa Kabenge; Hope Njuki Nakabuye; Jiaming Duan

Smart Agricultural Technology (Aug 2023)

Evaluation of artificial intelligence algorithms with sensor data assimilation in estimating crop evapotranspiration and crop water stress index for irrigation water management

Abia Katimbo,
Daran R. Rudnick,
Jingwen Zhang,
Yufeng Ge,
Kendall C. DeJonge,
Trenton E. Franz,
Yeyin Shi,
Wei-zhen Liang,
Xin Qiao,
Derek M. Heeren,
Isa Kabenge,
Hope Njuki Nakabuye,
Jiaming Duan

Affiliations

Abia Katimbo: Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA; West Central Research, Extension, and Education Center, University of Nebraska-Lincoln, North Platte, NE, USA
Daran R. Rudnick: Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA; West Central Research, Extension, and Education Center, University of Nebraska-Lincoln, North Platte, NE, USA; Corresponding author.
Jingwen Zhang: Department of Civil and Environmental Engineering, University of Illinois at Urban-Champaign, Urbana, IL, USA
Yufeng Ge: Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
Kendall C. DeJonge: Water Management Systems Research Unit, United States Department of Agriculture, Fort Collins, CO, USA
Trenton E. Franz: School of Natural Resources, University of Nebraska-Lincoln, NE, USA
Yeyin Shi: Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
Wei-zhen Liang: Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA; Panhandle Research, Extension, and Education Center, University of Nebraska-Lincoln, Scottsbluff, NE, USA
Xin Qiao: Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA; Panhandle Research, Extension, and Education Center, University of Nebraska-Lincoln, Scottsbluff, NE, USA
Derek M. Heeren: Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
Isa Kabenge: Department of Agricultural and Bio-Systems Engineering, Makerere University, P.O Box 7062, Kampala, Uganda
Hope Njuki Nakabuye: Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA; West Central Research, Extension, and Education Center, University of Nebraska-Lincoln, North Platte, NE, USA
Jiaming Duan: Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA

Journal volume & issue: Vol. 4
p. 100176

Abstract

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Irrigation water management using automated irrigation decision support system (IDSS) as a smart irrigation scheduling tool can improve water use efficiency and crop production, especially under circumstances of limited water supply. The current study evaluated the performance of different artificial intelligence (AI) algorithms and their ensembles in forecasting Crop Evapotranspiration (ETc) and Crop Water Stress Index (CWSI) against calculated single crop coefficient FAO56 ETc and Jackson's theoretical CWSI, respectively. Soil moisture, canopy temperatures (Tc) and Normalized Difference Vegetation Index (NDVI) were all measured from irrigated and non-irrigated maize plots in West Central Nebraska during 2020 and 2021 growing seasons. There were fifteen and twelve input combinations used for ETc and CWSI predictions, respectively, having input variables such as weather and soil moisture as well as ancillary variables, including NDVI, reference evapotranspiration (ETr), and cumulative growing degree days (CGDDs). While evaluating the models, four statistical performance indicators including coefficient of determination (r2), root mean square error (RMSE), mean absoluter error (MAE), and mean absolute percentage error (MAPE) were used. Furthermore, ranking scores were performed on statistical results to find the overall best model across all the input combinations. Based on total ranking scores, CatBoost (RMSE ranging between 0.06 – 0.09 unitless) was the best model in predicting CWSI, while Stacked Regression (RMSE ranging between 0.27 – 0.72 mm d−1) was the best model for ETc estimation. Future research will consider designing and evaluating an IDSS using identified best machine learning models to establish soil water and plant stress feedback for automated irrigation scheduling.

Published in Smart Agricultural Technology

ISSN: 2772-3755 (Online)
Publisher: Elsevier
Country of publisher: Netherlands
LCC subjects: Agriculture: Agriculture (General); Social Sciences: Industries. Land use. Labor: Special industries and trades: Agricultural industries
Website: https://www.journals.elsevier.com/smart-agricultural-technology

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