High Throughput Phenotyping

High-throughput phenotyping aims to rapidly and efficiently measure plant and other biological traits, automating processes previously reliant on manual labor. Current research focuses on developing and validating computer vision techniques, leveraging deep learning models like Mask R-CNN, transformers (e.g., LETR), and Neural Radiance Fields (NeRFs), along with large language models (LLMs) for analyzing textual data like clinical notes. This automation significantly accelerates research in areas such as plant breeding, precision agriculture, and precision medicine, enabling large-scale data analysis and more efficient identification of desirable traits or disease markers.