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

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.Recent advancements in computer vision and machine learning, especially deep learning, have improved plant phenotyping, with methods like the scale-invariant feature transform (SIFT) algorithm and convolutional neural networks aiding in rice panicle analysis. However, these techniques face limitations in capturing the dynamic growth of rice panicles over time. Addressing this gap requires combining field cameras with deep learning for detailed, real-time monitoring.In June 2023, Plant Phenomics published a research article titled "Analyzing Nitrogen Effects on Rice Panicle Development by Panicle Detection and Time-Series ...