Study on classification system for seedless white grape based on YOLOv7 model
The uneven quality of seedless white grapes leads to poor market competitiveness,while manual grading has low effi-ciency and subjective differences.Therefore,the objective of this paper is to propose a seedless white grape classification system based on the YOLOv7 model.The image of the seedless white grape fruit was used as input to train and adjust the YOLOv7 model,resulting in an average accuracy of 96.86%and an average grading speed of 1.9 images per second.The grading model proposed in this article was compared with traditional manual grading methods to verify its advantages in grading seedless white grape fruits of different qualities.The experimental results show that the YOLOv7 model can distinguish different levels of seedless white grapes in real time and accurately,and is superior to traditional manual grading methods in terms of grading accuracy and speed.This classification system provides a valuable reference for research of fruit grading in seedless white grapes.
YOLOv7Seedless white grapesClassification detection
NETL
NSTL
万方数据
