Sorghum Panicle

Sorghum panicle research focuses on developing efficient and accurate methods for phenotyping this crucial component of the plant, impacting yield prediction and breeding programs. Current efforts leverage deep learning, employing object detection models and generative adversarial networks (GANs) to analyze images from various sources, including UAVs and smartphones, often incorporating semi-supervised learning techniques to reduce data labeling needs. These advancements enable high-throughput 3D reconstruction of panicles, facilitating precise measurements of traits like seed count and volume, ultimately improving the efficiency and accuracy of sorghum breeding and yield estimation.