The New Lens on Seed Testing: AI, ML and Image Analysis - LAKSHMIPRASANNA KATA; APARNA KUNA; PALLAVI MANDALAPU; BHARATHI YERAS; SUJATHA PATTA; ZUBEDA SOHAN (Seed Research, 2025)
Seed testing is undergoing a paradigm shift with the integration of advanced digital technologiesthat augment conventional methodologies by offering greater speed, accuracy, and scalability. This review examinesthe transformative impact of innovations such as automation, artificial intelligence (AI), and advanced imageanalysis on contemporary seed quality assessment. AI-based approaches, particularly machine learning anddeep learning models, are increasingly being employed to automate complex and labour-intensive tasks—includingseed sorting, germination prediction, and defect detection—that were traditionally reliant on expert visual evaluation.Image analysis techniques, notably hyperspectral and multispectral imaging, enable rapid and non-destructiveassessment of both external and internal seed attributes, thereby substantially improving diagnostic precision. Inparallel, the adoption of edge computing, Internet of Things (IoT) sensors, and cloud-based platforms has enabledreal-time and decentralized seed testing, extending analytical capabilities from centralized laboratories to fieldlevel applications. These digital frameworks enhance data integration, traceability, and decision-making efficiencyacross seed production systems, breeding programmes, and quality control laboratories. Moreover, high-throughputdigital phenotyping tools are accelerating plant breeding by generating detailed, high-resolution trait data, therebyreducing selection cycles and improving the efficiency of crop improvement programmes. Despite theseadvancements, the widespread adoption of digital seed testing technologies necessitates the development ofstandardized protocols, robust validation frameworks, and appropriate regulatory oversight to ensure data reliability,interoperability, and broader applicability. Collectively, these emerging innovations are redefining seed testingpractices and positioning the discipline as a critical enabler of sustainable, precision-oriented, and data-drivenagriculture.