Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Arthi, Gunasekaran; Loganathan, Murugesan; Abidharini, Jothi Dheivasikamani; Balamuralikrishnan, Balasubramanian; Arun, Meyyazhagan; Manikantan, Pappuswamy; Prabhu, Jeyabal Philomenathan Antony; Anand, Arumugam Vijaya Title A name given to the resource Bioinformatics Tools and Deep Learning for Plant High-Throughput Phenotyping and Phenomics Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Plant High-Throughput Phenotyping and Functional Phenomics;pp.1-18 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003505631-1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003505631-1</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105004612782?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105004612782?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Arthi G., Department of Human Genetics and Molecular Genetics, Bharathiar University Coimbatore, Tamil Nadu, India; Loganathan M., Department of Human Genetics and Molecular Genetics, Bharathiar University Coimbatore, Tamil Nadu, India; Abidharini J.D., Department of Human Genetics and Molecular Genetics, Bharathiar University Coimbatore, Tamil Nadu, India; Balamuralikrishnan B., Department of Food Science and Biotechnology, Sejong University, Seoul, South Korea; Arun M., Department of Life Science, CHRIST University, Karnataka, Bangalore, India; Manikantan P., Department of Life Science, CHRIST University, Karnataka, Bangalore, India; Prabhu J.P.A., Department of Human Genetics and Molecular Genetics, Bharathiar University Coimbatore, Tamil Nadu, India; Anand A.V., Department of Human Genetics and Molecular Genetics, Bharathiar University Coimbatore, Tamil Nadu, India Description An account of the resource High-throughput phenotyping and phenomics are essential for advancing plant research and improving crop performance. The integration of bioinformatics tools and deep learning methodologies has transformed the way data is processed and analyzed in these fields. Bioinformatics tools facilitate the management and interpretation of large-scale genomic and phenotypic data, enabling researchers to extract valuable insights. Deep learning algorithms, particularly convolutional neural networks, have shown significant promise in automating the analysis of complex plant images and enhancing trait identification and prediction. This synergy between bioinformatics and deep learning accelerates the identification of key traits, improves the precision of phenotypic assessments, and supports the development of more resilient and productive crops. This chapter highlights how these advanced technologies contribute to more effective and scalable plant phenotyping and phenomics efforts. 2025 selection and editorial matter, Jen-Tsung Chen; individual chapters, the contributors. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104036036-1; 978-103282181-8; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online