Research on Strawberry Quality Grading Based on Object Detection and Stacking Fusion Model

Abstract

Strawberry quality grading helps producers to better manage inventory, transportation and sales, and improve product market competitiveness. Currently, this work is mostly carried out by manual grading, which is not only inefficient, but also relies on the experience of the grader, which is easy to cause grading errors. Aiming at the complex background information present in strawberry images, a grading fusion model is proposed, which adopts YOLOv5 target detection algorithm in the first level to recognize the strawberry in its natural state and crop its background information; In the second level, a stacking fusion model is adopted, which combines the neural network classification model and the machine learning classifier to realize the lossless quality grading of strawberry; The loss function is improved by introducing the Label Smoothing method, which makes the model more suitable for strawberry grading this kind of non-standard product classification task; Comparison experimental results show that the accuracy of the strawberry grading fusion model based on complex background images proposed in this paper reaches 85.4%, which improves the classification accuracy by 13% comparing with that of the single-stage model.

Keywords

• Strawberries graded
• stacking
• YOLOv5
• transfer learning
• label smoothing