The application discloses a rice
germplasm yield prediction method and
system for
panicle type classification and growth period alignment. Firstly,
time sequence images of a rice plot are acquired, and a ST-YOLO single
panicle detection model is used to identify the growth stage of the rice and extract single
panicle images. The single panicle images are input into a PP-LCNet multi-
label classification model to synchronously identify the panicle type and the growth period, filter mature period data, and process the data by panicle type, so that the growth period alignment and the panicle type classification are realized, and the temporal and spatial feature
noise introduced due to different phenology and morphology is eliminated. The geometric features of the detection frame are extracted at the single panicle scale, and the number, color, texture, spectrum, phenology and other features are extracted by using a Swin
Transformer segmentation model at the group scale.
Machine learning algorithms are used to respectively establish yield prediction models for different panicle types. The application effectively solves the problem of complex temporal and
spatial noise interference in large-scale breeding tests, and realizes high-
throughput, accurate and non-destructive rice yield prediction.