Panicle-Cloud: An Open and AI-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice

Rice (Oryza sativa) is an essential stable food for many rice consumption nations in the world and, thus, the importance to improve its yield production under global climate changes. To evaluate different rice varieties’ yield performance, key yield-related traits such as panicle number per unit area (PNpM(2)) are key indicators, which have attracted much attention by many plant research groups. Nevertheless, it is still challenging to conduct large-scale screening of rice panicles to quantify the PNpM(2) trait due to complex field conditions, a large variation of rice cultivars, and their panicle morphological features. Here, we present Panicle-Cloud, an open and artificial intelligence (AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected imagery. To facilitate the development of AI-powered detection models, we first established an open diverse rice panicle detection dataset that was annotated by a group of rice specialists; then, we integrated several state-of-the-art deep learning models (including a preferred model called Panicle-AI) into the Panicle-Cloud platform, so that nonexpert users could select a pretrained model to detect rice panicles from their own aerial images. We trialed the AI models with images collected at different attitudes and growth stages, through which the right timing and preferred image resolutions for phenotyping rice panicles in the field were identified. Then, we applied the platform in a 2-season rice breeding trial to valid its biological relevance and classified yield production using the platform-derived PNpM(2) trait from hundreds of rice varieties. Through correlation analysis between computational analysis and manual scoring, we found that the platform could quantify the PNpM(2) trait reliably, based on which yield production was classified with high accuracy. Hence, we trust that our work demonstrates a valuable advance in phenotyping the PNpM(2) trait in rice, which provides a useful toolkit to enable rice breeders to screen and select desired rice varieties under field conditions.