Transforming rice phenotyping: Advanced deep learning models enhance panicle analysis and nitrogen impact studies

Rice is crucial for global food security, providing sustenance for half of the world's population. Its production, particularly influenced by environmental factors during the heading-flowering stage, affects crucial growth traits. Traditional phenotyping methods are inefficient for large-scale analysis, necessitating advanced, accurate monitoring solutions.