Evaluation of artificial intelligence algorithms with sensor data assimilation in estimating crop evapotranspiration and crop water stress index for irrigation water management Article Swipe
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
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.atech.2023.100176
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.atech.2023.100176
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.
Related Topics
Concepts
Evapotranspiration
Crop coefficient
Irrigation scheduling
Mean squared error
Irrigation
Deficit irrigation
Coefficient of determination
Normalized Difference Vegetation Index
Mathematics
Environmental science
Water content
Irrigation management
Statistics
Machine learning
Leaf area index
Soil water
Computer science
Soil science
Agronomy
Engineering
Geotechnical engineering
Ecology
Biology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.atech.2023.100176
- OA Status
- gold
- Cited By
- 51
- References
- 61
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4317581912
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4317581912Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.atech.2023.100176Digital Object Identifier
- Title
-
Evaluation of artificial intelligence algorithms with sensor data assimilation in estimating crop evapotranspiration and crop water stress index for irrigation water managementWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-20Full publication date if available
- Authors
-
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 DuanList of authors in order
- Landing page
-
https://doi.org/10.1016/j.atech.2023.100176Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.atech.2023.100176Direct OA link when available
- Concepts
-
Evapotranspiration, Crop coefficient, Irrigation scheduling, Mean squared error, Irrigation, Deficit irrigation, Coefficient of determination, Normalized Difference Vegetation Index, Mathematics, Environmental science, Water content, Irrigation management, Statistics, Machine learning, Leaf area index, Soil water, Computer science, Soil science, Agronomy, Engineering, Geotechnical engineering, Ecology, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
51Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 20, 2024: 21, 2023: 10Per-year citation counts (last 5 years)
- References (count)
-
61Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.between | 195, 212 |
| abstract_inverted_index.current | 32 |
| abstract_inverted_index.fifteen | 101 |
| abstract_inverted_index.growing | 97, 134 |
| abstract_inverted_index.improve | 17 |
| abstract_inverted_index.limited | 28 |
| abstract_inverted_index.machine | 237 |
| abstract_inverted_index.models, | 141 |
| abstract_inverted_index.overall | 179 |
| abstract_inverted_index.ranging | 194, 211 |
| abstract_inverted_index.ranking | 169, 190 |
| abstract_inverted_index.results | 175 |
| abstract_inverted_index.scores, | 191 |
| abstract_inverted_index.supply. | 30 |
| abstract_inverted_index.support | 7 |
| abstract_inverted_index.weather | 118 |
| abstract_inverted_index.(CGDDs). | 137 |
| abstract_inverted_index.CatBoost | 192 |
| abstract_inverted_index.Nebraska | 92 |
| abstract_inverted_index.absolute | 162 |
| abstract_inverted_index.consider | 228 |
| abstract_inverted_index.decision | 6 |
| abstract_inverted_index.feedback | 247 |
| abstract_inverted_index.learning | 238 |
| abstract_inverted_index.measured | 82 |
| abstract_inverted_index.moisture | 121 |
| abstract_inverted_index.research | 226 |
| abstract_inverted_index.seasons. | 98 |
| abstract_inverted_index.absoluter | 157 |
| abstract_inverted_index.ancillary | 125 |
| abstract_inverted_index.automated | 4, 249 |
| abstract_inverted_index.designing | 229 |
| abstract_inverted_index.different | 38 |
| abstract_inverted_index.ensembles | 45 |
| abstract_inverted_index.establish | 241 |
| abstract_inverted_index.evaluated | 34 |
| abstract_inverted_index.including | 127, 146 |
| abstract_inverted_index.irrigated | 84 |
| abstract_inverted_index.moisture, | 70 |
| abstract_inverted_index.performed | 172 |
| abstract_inverted_index.reference | 129 |
| abstract_inverted_index.unitless) | 199 |
| abstract_inverted_index.variables | 115 |
| abstract_inverted_index.Difference | 76 |
| abstract_inverted_index.Irrigation | 0 |
| abstract_inverted_index.Normalized | 75 |
| abstract_inverted_index.Regression | 209 |
| abstract_inverted_index.Vegetation | 77 |
| abstract_inverted_index.algorithms | 42 |
| abstract_inverted_index.artificial | 39 |
| abstract_inverted_index.calculated | 58 |
| abstract_inverted_index.cumulative | 133 |
| abstract_inverted_index.efficiency | 20 |
| abstract_inverted_index.especially | 24 |
| abstract_inverted_index.evaluating | 139, 231 |
| abstract_inverted_index.identified | 235 |
| abstract_inverted_index.indicators | 145 |
| abstract_inverted_index.irrigation | 5, 13, 250 |
| abstract_inverted_index.management | 2 |
| abstract_inverted_index.percentage | 163 |
| abstract_inverted_index.predicting | 205 |
| abstract_inverted_index.scheduling | 14 |
| abstract_inverted_index.variables, | 126 |
| abstract_inverted_index.Jackson’s | 65 |
| abstract_inverted_index.coefficient | 61, 147 |
| abstract_inverted_index.estimation. | 224 |
| abstract_inverted_index.forecasting | 47 |
| abstract_inverted_index.performance | 36, 144 |
| abstract_inverted_index.production, | 23 |
| abstract_inverted_index.scheduling. | 251 |
| abstract_inverted_index.statistical | 143, 174 |
| abstract_inverted_index.theoretical | 66 |
| abstract_inverted_index.Furthermore, | 168 |
| abstract_inverted_index.combinations | 105 |
| abstract_inverted_index.intelligence | 40 |
| abstract_inverted_index.predictions, | 111 |
| abstract_inverted_index.temperatures | 72 |
| abstract_inverted_index.circumstances | 26 |
| abstract_inverted_index.combinations. | 186 |
| abstract_inverted_index.determination | 149 |
| abstract_inverted_index.non-irrigated | 86 |
| abstract_inverted_index.respectively, | 112 |
| abstract_inverted_index.respectively. | 68 |
| abstract_inverted_index.Evapotranspiration | 49 |
| abstract_inverted_index.evapotranspiration | 130 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| corresponding_author_ids | https://openalex.org/A5008475752 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 13 |
| corresponding_institution_ids | https://openalex.org/I114395901 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/6 |
| sustainable_development_goals[0].score | 0.8299999833106995 |
| sustainable_development_goals[0].display_name | Clean water and sanitation |
| citation_normalized_percentile.value | 0.98808675 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |