AI-Driven Plant Science: Advancing Crop Performance Through Omics Integration and Physiology

Name: AI-Driven Plant Science: Advancing Crop Performance Through Omics Integration and Physiology
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Bol AI-Driven Plant Science traces the convergence between plant biology and artificial intelligence, connecting molecular data to breeding decisions and breeding decisions to sustainable outcomes. It opens with the genomic and epigenetic foundations of crop improvement, looking at how AI-based sequencing tools speed up genome annotation and how predictive models help identify which breeding lines are worth pursuing for climate resilience. The narrative then moves into the layers of gene expression and function, following the flow of biological information through transcriptomics, proteomics, and metabolomics, supported by bioinformatics pipelines built to handle that scale of data, and showing how integrative approaches are beginning to explain why two plants with near-identical genomes can respond so differently under the same stress. From the lab, the book moves to the field where AI supports disease and pest management, high-throughput phenotyping, and sustainable agricultural practice, translating molecular insight into decisions that affect real crops under real environmental pressures. A closing section looks ahead to synthetic biology's role in plant biotechnology, alongside the ethical, regulatory, and data-security questions that accompany these technologies' expanding presence in agricultural research. With contributions from specialist groups across Asia, the Middle East, and Europe, the volume offers a coherent perspective for researchers, industry professionals, and students seeking to understand how computational methods are changing the questions plant science can ask, and the speed at which it can answer them.

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AI-Driven Plant Science traces the convergence between plant biology and artificial intelligence, connecting molecular data to breeding decisions and breeding decisions to sustainable outcomes. It opens with the genomic and epigenetic foundations of crop improvement, looking at how AI-based sequencing tools speed up genome annotation and how predictive models help identify which breeding lines are worth pursuing for climate resilience. The narrative then moves into the layers of gene expression and function, following the flow of biological information through transcriptomics, proteomics, and metabolomics, supported by bioinformatics pipelines built to handle that scale of data, and showing how integrative approaches are beginning to explain why two plants with near-identical genomes can respond so differently under the same stress. From the lab, the book moves to the field where AI supports disease and pest management, high-throughput phenotyping, and sustainable agricultural practice, translating molecular insight into decisions that affect real crops under real environmental pressures. A closing section looks ahead to synthetic biology's role in plant biotechnology, alongside the ethical, regulatory, and data-security questions that accompany these technologies' expanding presence in agricultural research. With contributions from specialist groups across Asia, the Middle East, and Europe, the volume offers a coherent perspective for researchers, industry professionals, and students seeking to understand how computational methods are changing the questions plant science can ask, and the speed at which it can answer them.

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