<?xml version="1.0" encoding="UTF-8"?>
<?xml-stylesheet type="text/xsl" href="https://www.peertechzpublications.org/assets/xsl/oaitohtml.xsl"?>
<OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:mml="http://www.w3.org/1998/Math/MathML" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd">
										<responseDate>2026-05-13T15:59:40Z</responseDate>
										<request verb="GetRecord" metadataPrefix="oai_dc" identifier="oai:www.peertechzpublications.org:10.17352/2455-488X.000091">https://www.peertechzpublications.org/oai-pmh</request><GetRecord><record>
								<header>
									<identifier>oai:www.peertechzpublications.org:10.17352/2455-488X.000091</identifier>
									<datestamp>2025-04-10</datestamp>
									<setSpec>PTZ.JCEES:VOL11</setSpec>
								</header>
								<metadata>
									<oai_dc:dc xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
										<dc:title>
										High-throughput Screening and Trait Dissection for Seed Quality Enhancement
										</dc:title><dc:creator>Rashmi Jha</dc:creator><dc:creator> Sk Asraful Ali</dc:creator><dc:creator> V Manonmani</dc:creator><dc:creator> Ramanjit Kaur</dc:creator><dc:creator> Sudhir Kumar</dc:creator><dc:creator> Rajkumari Jyotika</dc:creator><dc:creator> Megha Kumari</dc:creator><dc:creator> Dileep Meena</dc:creator><dc:creator> Rohit Bapurao Borate</dc:creator><dc:creator> Sunil Kumar Prajapati</dc:creator><dc:creator> Nilutpal Saikia</dc:creator><dc:creator> Unti Miiri Ezing</dc:creator><dc:creator>Bipasa Baur</dc:creator><dc:description>&lt;p&gt;High-throughput phenotyping (HTP) has transformed seed testing, quality evaluation, storage, and stress response assessment by enabling rapid, non-destructive, and high-resolution analysis of seed traits. Traditional seed evaluation methods are labour-intensive and time-consuming, whereas HTP employs advanced imaging, sensor technologies, and machine learning algorithms to assess seed morphology, physiological traits, and biochemical properties efficiently. In seed testing, HTP accelerates germination studies, vigour assessments, and stress tolerance evaluations, facilitating the identification of high-quality and resilient seed varieties. It also enhances seed storage practices by providing real-time monitoring of seed viability, detecting deterioration factors, and optimizing storage conditions. Furthermore, HTP significantly contributes to understanding seed responses to biotic and abiotic stresses. By characterizing genetic and physiological factors associated with disease resistance and environmental stress tolerance, HTP aids in breeding stress-resilient crops and optimizing seed treatments. The integration of HTP with artificial intelligence further refines predictive modelling and precision agriculture strategies, supporting climate-resilient farming and sustainable agricultural practices. This paper highlights the multifaceted role of HTP in advancing seed science, from quality assurance to stress management, underscoring its impact on agricultural productivity and genetic resource conservation.&amp;nbsp;&lt;/p&gt;</dc:description>
										<dc:publisher>Journal of Civil Engineering and Environmental Sciences - Peertechz Publications</dc:publisher>
										<dc:date>2025-04-10</dc:date>
										<dc:type>Review Article</dc:type>
										<dc:identifier>https://doi.org/10.17352/2455-488X.000091</dc:identifier>
										<dc:language>en</dc:language>
										<dc:rights>Copyright © Rashmi Jha et al.</dc:rights>
									</oai_dc:dc>
								</metadata>
							</record></GetRecord>
						</OAI-PMH>
