Deep Learning-Driven Seed Detection Technology: Research Advances and Future Challenges

As a core component of modern agricultural breeding, germplasm resource management, and food quality control, seed detection holds significant importance for global food security. Deep learning technology, with its powerful feature learning and pattern recognition capabilities, is driving the transformation of seed detection toward intelligent and precise paradigms. This paper systematically reviews the technological evolution of deep learning in seed detection, focusing on research advances in critical application areas such as variety identification, defect classification, viability assessment, and gene-phenotype association. Studies demonstrate that deep learning significantly enhances detection accuracy and efficiency through multimodal data fusion, lightweight model design, and self-supervised learning. However, current technologies still face challenges such as the lack of data standardization, insufficient model generalizability, and computational resource constraints. Future efforts should focus on interdisciplinary integration, edge-cloud collaborative computing, and breakthroughs in trustworthy AI technologies to establish efficient and robust intelligent seed detection systems, thereby providing technological support for the revitalization of the seed industry.