Harnessing AI-Powered Genomic Research for Sustainable Crop Improvement

Elżbieta Wójcik-Gront, Bartłomiej Zieniuk and Magdalena Pawełkowicz ()
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Elżbieta Wójcik-Gront: Department of Biometry, Institute of Agriculture, Warsaw University of Life Sciences-SGGW, 159 Nowoursynowska Str., 02-776 Warsaw, Poland
Bartłomiej Zieniuk: Department of Chemistry, Institute of Food Sciences, Warsaw University of Life Sciences-SGGW, 159C Nowoursynowska Str., 02-776 Warsaw, Poland
Magdalena Pawełkowicz: Department of Plant Genetics, Breeding and Biotechnology, Institute of Biology, Warsaw University of Life Sciences-SGGW, 159 Nowoursynowska Str., 02-776 Warsaw, Poland

Agriculture, 2024, vol. 14, issue 12, 1-17

Abstract: Artificial intelligence (AI) can revolutionize agriculture by enhancing genomic research and promoting sustainable crop improvement. AI systems integrate machine learning (ML) and deep learning (DL) with big data to identify complex patterns and relationships by analyzing vast genomic, phenotypic, and environmental datasets. This capability accelerates breeding cycles, improves predictive accuracy, and supports the development of climate-resilient, high-yielding crop varieties. Applications such as precision agriculture, automated phenotyping, predictive analytics, and early pest and disease detection demonstrate AI’s ability to optimize agricultural practices while promoting sustainability. Despite these advancements, challenges remain, including fragmented data sources, variability in phenotyping protocols, and data ownership concerns. Addressing these issues through standardized data integration frameworks, advanced analytical tools, and ethical AI practices will be critical for realizing AI’s full agricultural potential. This review provides a comprehensive overview of AI-powered genomic research, highlights the role of big data in training robust AI models, and explores ethical and technological considerations for sustainable agricultural practices.

Keywords: artificial intelligence; machine learning; deep learning; crop improvement; genomic study; big data (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
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