Researchers developed a model called PRNet using deep learning and high-quality aerial images to estimate the panicle ratio in rice plants. By analyzing images from experimental plots, they achieved a high accuracy in predicting the panicle ratio, heading date, and tiller percentage. This method can replace manual measurements and accurately gather information about rice plants, like grains per panicle and flowering patterns, using drones and advanced technology.