Scalable machine learning framework for adaptive irrigation management of maize and soybean in the U.S. Midwest Article Swipe

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Precious N. Egbuikwem , Derek M. Heeren , Yeyin Shi , Eric Wilkening , Ivo Zution Gonçalves , Guillermo R. Balboa , Daran R. Rudnick , Abia Katimbo , Randall S. Ritzema ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1016/j.compag.2025.110710

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Irrigation

Agricultural Engineering

Agronomy

Computer Science

Environmental Science

Artificial Intelligence

Machine Learning

Engineering

Biology

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Irrigation Agricultural engineering Scalability Agronomy Computer science Environmental science Artificial intelligence Machine learning Engineering Biology Operating system

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Type: article
Language: en
Landing Page: https://doi.org/10.1016/j.compag.2025.110710
OA Status: hybrid
References: 58
Related Works: 10
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OpenAlex ID: https://openalex.org/W4412455174

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DOI: https://doi.org/10.1016/j.compag.2025.110710

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Title: Scalable machine learning framework for adaptive irrigation management of maize and soybean in the U.S. Midwest

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Type: article

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Language: en

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Publication year: 2025

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Publication date: 2025-07-15

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Authors: Precious N. Egbuikwem, Derek M. Heeren, Yeyin Shi, Eric Wilkening, Ivo Zution Gonçalves, Guillermo R. Balboa, Daran R. Rudnick, Abia Katimbo, Randall S. Ritzema

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Landing page: https://doi.org/10.1016/j.compag.2025.110710

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Open access: Yes

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OA status: hybrid

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OA URL: https://doi.org/10.1016/j.compag.2025.110710

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Concepts: Irrigation, Agricultural engineering, Scalability, Agronomy, Computer science, Environmental science, Artificial intelligence, Machine learning, Engineering, Biology, Operating system

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Cited by: 0

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