A Review of Machine Learning Techniques in Agroclimatic Studies

Dania Tamayo-Vera, Xiuquan Wang () and Morteza Mesbah
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Dania Tamayo-Vera: School of Mathematical and Computational Sciences, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada
Xiuquan Wang: Canadian Center for Climate Change and Adaptation, University of Prince Edward Island, St. Peters Bay, PE C0A 2A0, Canada
Morteza Mesbah: Charlottetown Research and Development Centre, Agriculture and Agri-Food Canada, Charlottetown, PE C1A 4N6, Canada

Agriculture, 2024, vol. 14, issue 3, 1-19

Abstract: The interplay of machine learning (ML) and deep learning (DL) within the agroclimatic domain is pivotal for addressing the multifaceted challenges posed by climate change on agriculture. This paper embarks on a systematic review to dissect the current utilization of ML and DL in agricultural research, with a pronounced emphasis on agroclimatic impacts and adaptation strategies. Our investigation reveals a dominant reliance on conventional ML models and uncovers a critical gap in the documentation of methodologies. This constrains the replicability, scalability, and adaptability of these technologies in agroclimatic research. In response to these challenges, we advocate for a strategic pivot toward Automated Machine Learning (AutoML) frameworks. AutoML not only simplifies and standardizes the model development process but also democratizes ML expertise, thereby catalyzing the advancement in agroclimatic research. The incorporation of AutoML stands to significantly enhance research scalability, adaptability, and overall performance, ushering in a new era of innovation in agricultural practices tailored to mitigate and adapt to climate change. This paper underscores the untapped potential of AutoML in revolutionizing agroclimatic research, propelling forward the development of sustainable and efficient agricultural solutions that are responsive to the evolving climate dynamics.

Keywords: machine learning; agricultural research; deep learning; agricultural data; data processing; AutoML; crop management; pet diseases; smart farming; soil assessment (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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