Data-driven estimation of actual evapotranspiration to support irrigation management: Testing two novel methods based on an unoccupied aerial vehicle and an artificial neural network Article Swipe
Offer Rozenstein
,
Lior Fine
,
Nitzan Malachy
,
Antoine Richard
,
Cédric Pradalier
,
Josef Tanny
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.agwat.2023.108317
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.agwat.2023.108317
Recent advances in remote sensing and machine learning show potential for improving irrigation use efficiency. In this study, two independent methods to determine the irrigation dose in processing tomatoes were calibrated, validated, and tested in an irrigation experiment. The first method used multispectral imagery acquired from an unoccupied aerial vehicle (UAV) to estimate the FAO-56 crop coefficient, Kc. The second method used an artificial neural network (ANN) trained on eddy covariance measurements of latent heat flux and meteorological variables from a nearby meteorological station. An irrigation experiment was conducted, where the farmer was instructed through a mobile application with updated irrigation recommendations. Evapotranspiration estimated by the new methods was set as the irrigation dose for the UAV and ANN treatments. The best-practice irrigation, commonly used by the regional farmers, was set as the control treatment (100%), guided by an irrigation expert and soil sensors for feedback. Derivatives of this treatment at 50%, 75%, and 125% of the control irrigation dose were tested. Yield, water use efficiency (WUE), and Brix level were measured and analyzed. Results show that both methods, UAV and ANN, estimated evapotranspiration to derive the irrigation dose at a near-perfect agreement with best-practice irrigation, both in the total amount and irrigation rate. Furthermore, there were no significant differences between the best practice and the experimental treatments in yield (117 ton/ha), water-use efficiency (31.7 kg/m3), and Brix (4.5°Bx). These results demonstrate the potential of advanced machine learning techniques and aerial remote sensing to quantify crop water requirements and support irrigation management.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.agwat.2023.108317
- OA Status
- gold
- Cited By
- 22
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4366300649
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4366300649Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.agwat.2023.108317Digital Object Identifier
- Title
-
Data-driven estimation of actual evapotranspiration to support irrigation management: Testing two novel methods based on an unoccupied aerial vehicle and an artificial neural networkWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-04-18Full publication date if available
- Authors
-
Offer Rozenstein, Lior Fine, Nitzan Malachy, Antoine Richard, Cédric Pradalier, Josef TannyList of authors in order
- Landing page
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https://doi.org/10.1016/j.agwat.2023.108317Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.agwat.2023.108317Direct OA link when available
- Concepts
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Evapotranspiration, Irrigation, Eddy covariance, Environmental science, Crop coefficient, Irrigation management, Artificial neural network, Agricultural engineering, Remote sensing, Computer science, Artificial intelligence, Engineering, Agronomy, Geography, Biology, Ecology, EcosystemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
22Total citation count in OpenAlex
- Citations by year (recent)
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2025: 9, 2024: 9, 2023: 4Per-year citation counts (last 5 years)
- References (count)
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39Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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