AI Tool Used to Detect Seed Health and Quality

Seed plays a central role in agriculture, sustainability, and food security. A reservoir of nutrients and genetic diversity in many crops, the seed is the foundation for crop development, ensuring resilience and a productive agricultural system. The development of new technologies such as molecular biology, cryopreservation, DNA fingerprinting and high throughput sequencing, phenotyping and digital image formation has given new shape to seed science. Rapid and accurate identification of genetic traits, better germplasm conservation, and development of superior varieties. Seed health is the key component of agricultural productivity and sustainability, with seed-borne pathogens posing a major threat to crop yield and quality. The traditional seed testing methods, such as blotter paper, agar plate, etc., have the limitation of time and accuracy. Today, the approach of modern technology is in demand in the seed industry. By accepting these challenges, advances in computer vision, machine learning, remote sensing, and digital image processing are revolutionising seed pathology. These technologies have enhanced speed, reliability and precision to detect and classification of pathogens, non-destructive and real-time analysis. The artificial intelligence (AI) machine offers transformation potential to agriculture. AI tools can help to predict the germination potential, vigour, damage identification, sorting, seed storage analysis and breeding efficiency. The integration of AI tool with imaging, spectroscopy, and remote sensing can fill the gap of seed testing, high-quality seed production with low human error. Additionally, these powerful approaches improve the seed quality, stress tolerance and crop performance. Integration of decision support systems and spectral data modelling further supports disease monitoring and management in seed technology. The convergence of classical diagnostic methods with innovative digital tools is paving the way for more accurate, efficient, and scalable approaches to seed health assessment.