Communicating the use of artificial intelligence in agricultural and environmental research
Abstract Transformative technologies such as artificial intelligence (AI) make difficult tasks more accessible and convenient. Since 2018, the use of AI in research has increased drastically, with annual publication rates of 3–5 times higher than pre‐2017. Currently, >100,000 manuscripts using AI...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Agricultural & Environmental Letters |
| Online Access: | https://doi.org/10.1002/ael2.20144 |
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| _version_ | 1850250089241509888 |
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| author | Aaron Lee M. Daigh Samira H. Daroub Peter M. Kyveryga Mark E. Sorrells Nithya Rajan James A. Ippolito Endy Kailer Christine S. Booth Umesh Acharya Deepak Ghimire Saurav Das Bijesh Maharjan Yufeng Ge |
| author_facet | Aaron Lee M. Daigh Samira H. Daroub Peter M. Kyveryga Mark E. Sorrells Nithya Rajan James A. Ippolito Endy Kailer Christine S. Booth Umesh Acharya Deepak Ghimire Saurav Das Bijesh Maharjan Yufeng Ge |
| author_sort | Aaron Lee M. Daigh |
| collection | DOAJ |
| description | Abstract Transformative technologies such as artificial intelligence (AI) make difficult tasks more accessible and convenient. Since 2018, the use of AI in research has increased drastically, with annual publication rates of 3–5 times higher than pre‐2017. Currently, >100,000 manuscripts using AI are published annually within science and engineering, and >20,000 of these belong to the agricultural and environmental fields. Given the magnitude of use, clear communication on how AI is used and how it helps advance scientific knowledge is essential. Clear communication is perhaps more necessary with AI than previous technologies due to its broad and flexible spectrum of uses, the “black‐box” nature of deep‐learning algorithms, and ongoing debates regarding AI's predictive power versus knowledge of first‐principles mechanistic and process‐based theories and models. In this commentary, we provide guidelines and discussion points to the scientific community to ensure transparent and effective communication of AI research in agricultural and environmental research publications. |
| format | Article |
| id | doaj-art-c21732a7bc334f20bc3c5fa7f0265707 |
| institution | OA Journals |
| issn | 2471-9625 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Agricultural & Environmental Letters |
| spelling | doaj-art-c21732a7bc334f20bc3c5fa7f02657072025-08-20T01:58:19ZengWileyAgricultural & Environmental Letters2471-96252024-12-0192n/an/a10.1002/ael2.20144Communicating the use of artificial intelligence in agricultural and environmental researchAaron Lee M. Daigh0Samira H. Daroub1Peter M. Kyveryga2Mark E. Sorrells3Nithya Rajan4James A. Ippolito5Endy Kailer6Christine S. Booth7Umesh Acharya8Deepak Ghimire9Saurav Das10Bijesh Maharjan11Yufeng Ge12Agronomy & Horticulture University of Nebraska Lincoln Nebraska USASoil, Water, and Ecosystem Sciences University of Florida Gainesville Florida USAJohn Deere, Science Agronomy Johnston Iowa USASchool of Integrative Plant Science Cornell University Ithaca New York USASoil and Crop Sciences Texas A&M University College Station Texas USASchool of Environment and Natural Resources The Ohio State University Columbus Ohio USADepartment of Agronomy Kansas State University Manhattan Kansas USACollege of Agricultural Sciences and Natural Resources University of Nebraska Lincoln Nebraska USAJohn Deere Technology Innovation Center Champaign Illinois USAAgronomy & Horticulture University of Nebraska Lincoln Nebraska USAAgronomy & Horticulture University of Nebraska Lincoln Nebraska USAAgronomy & Horticulture University of Nebraska Lincoln Nebraska USABiological Systems Engineering University of Nebraska Lincoln Nebraska USAAbstract Transformative technologies such as artificial intelligence (AI) make difficult tasks more accessible and convenient. Since 2018, the use of AI in research has increased drastically, with annual publication rates of 3–5 times higher than pre‐2017. Currently, >100,000 manuscripts using AI are published annually within science and engineering, and >20,000 of these belong to the agricultural and environmental fields. Given the magnitude of use, clear communication on how AI is used and how it helps advance scientific knowledge is essential. Clear communication is perhaps more necessary with AI than previous technologies due to its broad and flexible spectrum of uses, the “black‐box” nature of deep‐learning algorithms, and ongoing debates regarding AI's predictive power versus knowledge of first‐principles mechanistic and process‐based theories and models. In this commentary, we provide guidelines and discussion points to the scientific community to ensure transparent and effective communication of AI research in agricultural and environmental research publications.https://doi.org/10.1002/ael2.20144 |
| spellingShingle | Aaron Lee M. Daigh Samira H. Daroub Peter M. Kyveryga Mark E. Sorrells Nithya Rajan James A. Ippolito Endy Kailer Christine S. Booth Umesh Acharya Deepak Ghimire Saurav Das Bijesh Maharjan Yufeng Ge Communicating the use of artificial intelligence in agricultural and environmental research Agricultural & Environmental Letters |
| title | Communicating the use of artificial intelligence in agricultural and environmental research |
| title_full | Communicating the use of artificial intelligence in agricultural and environmental research |
| title_fullStr | Communicating the use of artificial intelligence in agricultural and environmental research |
| title_full_unstemmed | Communicating the use of artificial intelligence in agricultural and environmental research |
| title_short | Communicating the use of artificial intelligence in agricultural and environmental research |
| title_sort | communicating the use of artificial intelligence in agricultural and environmental research |
| url | https://doi.org/10.1002/ael2.20144 |
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