The New Lens on Seed Testing: AI, ML and Image Analysis
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Authors
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LAKSHMIPRASANNA KATA
Dept. of Seed Science & Technology, SRTC campus, Professor Jayashankar Telangana Agricultural University, Rajendranagar, Hyderabad, Telangana-500030, India
Author
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APARNA KUNA
MFPI-Quality Control Laboratory, EEI campus, Professor Jayashankar Telangana Agricultural University, Rajendranagar, Hyderabad, Telangana-500030, India
Author
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PALLAVI MANDALAPU
Dept. of Seed Science & Technology, SRTC campus, Professor Jayashankar Telangana Agricultural University, Rajendranagar, Hyderabad, Telangana-500030, India
Author
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BHARATHI YERAS
Agricultural Research Station, Tandur, Professor Jayashankar Telangana Agricultural University, Rajendranagar, Hyderabad, Telangana-500030, India
Author
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SUJATHA PATTA
Dept. of Genetics and Plant Breeding, Agricultural College, Polasa, Jagtial, Professor Jayashankar Telangana Agricultural University, Rajendranagar, Hyderabad, Telangana-500030, India
Author
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ZUBEDA SOHAN
MFPI-Quality Control Laboratory, EEI campus, Professor Jayashankar Telangana Agricultural University, Rajendranagar, Hyderabad, Telangana-500030, India
Author
Keywords:
Digital seed testing, artificial intelligence, image analysis, seed quality, hyperspectral imaging, machine learningAbstract
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.
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