YOLOv8 BASED FEEDING INTENSITY OF LITOPENAEUS VANNAMEI QUANTIFICATION AND CLASSIFICATION METHODS
To quantify and classify the feeding intensity of Litopenaeus vannamei and overcome the subjectivity of feeding,and optimize the feed utilization rate.The experiment used the YOLOv8 model to identify and detect L.vannamei and the YOLOv8-segment model to segment the shrimp heads.Based on the number of shrimp in the feeding area and the pixel area of the shrimp head,the feeding intensity of the shrimp was quantified.The feeding intensity of the shrimp was then classified using the Convnext model,dividing the images into strong feeding,moderate feeding,and low feeding categories.The experimental results showed that after 200 iterations,the mAP50 of the YOLOv8 model for detecting shrimp targets in the feeding area reached 99.5％,and the mAP50 of the YOLOv8-segment model for segmenting the shrimp head reached 92.1％,which demonstrated the strong performance of the YOLOv8 model.The experiment verified that there were significant differences in the number of shrimp in the feeding area and the pixel area of the shrimp head under different feeding intensities.The classification accuracy of the Convnext model for the three feeding intensity categories of L.vannamei was 98.8％.This method can objectively and effectively quantify and classify the feeding intensity of L.vannamei,providing a theoretical basis and technical support for precise feeding of the shrimp.
Litopenaeus vannameiquantification of feeding intensitycomputer visionYOLOv8
万方数据
维普
