Harnessing AI-Powered Genomic Research for Sustainable Crop Improvement
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, a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/14/12/2299 |
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| author | Elżbieta Wójcik-Gront Bartłomiej Zieniuk Magdalena Pawełkowicz |
| author_facet | Elżbieta Wójcik-Gront Bartłomiej Zieniuk Magdalena Pawełkowicz |
| author_sort | Elżbieta Wójcik-Gront |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d3f6dc986c174901ac6bd7ea92921e5e |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-d3f6dc986c174901ac6bd7ea92921e5e2024-12-27T14:03:18ZengMDPI AGAgriculture2077-04722024-12-011412229910.3390/agriculture14122299Harnessing AI-Powered Genomic Research for Sustainable Crop ImprovementElżbieta Wójcik-Gront0Bartłomiej Zieniuk1Magdalena Pawełkowicz2Department of Biometry, Institute of Agriculture, Warsaw University of Life Sciences-SGGW, 159 Nowoursynowska Str., 02-776 Warsaw, PolandDepartment of Chemistry, Institute of Food Sciences, Warsaw University of Life Sciences-SGGW, 159C Nowoursynowska Str., 02-776 Warsaw, PolandDepartment of Plant Genetics, Breeding and Biotechnology, Institute of Biology, Warsaw University of Life Sciences-SGGW, 159 Nowoursynowska Str., 02-776 Warsaw, PolandArtificial 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.https://www.mdpi.com/2077-0472/14/12/2299artificial intelligencemachine learningdeep learningcrop improvementgenomic studybig data |
| spellingShingle | Elżbieta Wójcik-Gront Bartłomiej Zieniuk Magdalena Pawełkowicz Harnessing AI-Powered Genomic Research for Sustainable Crop Improvement Agriculture artificial intelligence machine learning deep learning crop improvement genomic study big data |
| title | Harnessing AI-Powered Genomic Research for Sustainable Crop Improvement |
| title_full | Harnessing AI-Powered Genomic Research for Sustainable Crop Improvement |
| title_fullStr | Harnessing AI-Powered Genomic Research for Sustainable Crop Improvement |
| title_full_unstemmed | Harnessing AI-Powered Genomic Research for Sustainable Crop Improvement |
| title_short | Harnessing AI-Powered Genomic Research for Sustainable Crop Improvement |
| title_sort | harnessing ai powered genomic research for sustainable crop improvement |
| topic | artificial intelligence machine learning deep learning crop improvement genomic study big data |
| url | https://www.mdpi.com/2077-0472/14/12/2299 |
| work_keys_str_mv | AT elzbietawojcikgront harnessingaipoweredgenomicresearchforsustainablecropimprovement AT bartłomiejzieniuk harnessingaipoweredgenomicresearchforsustainablecropimprovement AT magdalenapawełkowicz harnessingaipoweredgenomicresearchforsustainablecropimprovement |