The application provides a high-
throughput tracking measurement method for growth phenotypes of individual
Scophthalmus maximus, a medium, a device and an application thereof, and belongs to the technical field of biological identification. The method uses an
image acquisition system to obtain complete images of the ventral surface and the dorsal surface of the
Scophthalmus maximus, and performs pretreatment, and uses a
machine learning
algorithm to identify fish body ventral images that are easy to distinguish; then, a
fish fin image of the
Scophthalmus maximus is extracted from the dorsal image of an un-identified individual by using an
image segmentation model, a
deep learning algorithm is used to extract features, and a high-complexity fin image is identified; thereby, all Scophthalmus maximus individuals are identified; then, a data generation model is trained for the dorsal image of the Scophthalmus maximus individual, growth
phenotype key point detection is completed, and growth traits of the Scophthalmus maximus are calculated according to key
point data. The application also provides a medium, a device and an application for running the method. The method can track growth
phenotype information of the whole
growth cycle of the Scophthalmus maximus individual, automatically complete growth index detection, and has high precision.