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 technologies that augment conventional methodologies by offering greater speed, accuracy, and scalability. This review examines the transformative impact of innovations such as automation, artificial intelligence (AI), and advanced image analysis on contemporary seed quality assessment. AI-based approaches, particularly machine learning and deep learning models, are increasingly being employed to automate complex and labour-intensive tasks—including seed 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-destructive assessment of both external and internal seed attributes, thereby substantially improving diagnostic precision. In parallel, the adoption of edge computing, Internet of Things (IoT) sensors, and cloud-based platforms has enabled real-time and decentralized seed testing, extending analytical capabilities from centralized laboratories to fieldlevel applications. These digital frameworks enhance data integration, traceability, and decision-making efficiency across seed production systems, breeding programmes, and quality control laboratories. Moreover, high-throughput digital phenotyping tools are accelerating plant breeding by generating detailed, high-resolution trait data, thereby reducing selection cycles and improving the efficiency of crop improvement programmes. Despite these advancements, the widespread adoption of digital seed testing technologies necessitates the development of standardized protocols, robust validation frameworks, and appropriate regulatory oversight to ensure data reliability, interoperability, and broader applicability. Collectively, these emerging innovations are redefining seed testing practices and positioning the discipline as a critical enabler of sustainable, precision-oriented, and data-driven agriculture.