VGG-MFO-orange for sweetness prediction of Linhai mandarin oranges

Abstract

Abstract Mandarin orange is a popular fruit in China and known worldwide for its unique flavor and nutritional benefits. As consumer demand for fruit quality increases, the fine assessment and grading of fruit sweetness—especially through non-destructive testing techniques—are becoming increasingly important in agriculture and commerce. In this paper, a new Attention for Orange (AO) attention mechanism and Multiscale Feature Optimization (MFO) feature extraction module are designed and combined with VGG13 convolutional neural network (CNN), innovatively proposed VGG-MFO-Orange CNN model for accurately classifying mandarin oranges with different sweetness. First, a sample of Linhai mandarin oranges was collected, and a sweetness triple classification dataset with 5022 images was formed, utilizing image acquisition and sugar detection. The proposed model was then trained against six influential classical CNN models: DenseNet121, MobileNet_v2, ResNet50, ShuffleNet, VGG13, and VGG13_bn. The experimental results showed that our model achieved an accuracy of 86.8% on the validation set, which was significantly better than the other six models. It also demonstrated excellent generalization ability and effectiveness in predicting the sweetness of Linhai mandarin oranges. Therefore, our model can provide an efficient means of fruit grading for agricultural production, contribute to agricultural modernization, and enhance the competitiveness of agricultural products in the market.

Keywords

• Linhai mandarin orange
• Sugar testing
• Deep learning
• Convolutional neural networks
• Quality classification