Fang Chun, Shen Runhong, Yuan Meiling, Ye Wangyi, Dai Sheng, Wang Di
Zhejiang Provincial Key Laboratory for Cutting Tools, Research Institute of Motor and Intelligent Control Technology, Taizhou University, Jiaojiang, Taizhou, 318000, Zhejiang, China.
School of Light Industry, Harbin University of Commerce, Harbin, 150028, China.
Sci Rep. 2025 Apr 6;15(1):11781. doi: 10.1038/s41598-025-96297-9.
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.
蜜橘是中国一种受欢迎的水果，因其独特的风味和营养价值而闻名于世。随着消费者对水果品质需求的增加，水果甜度的精细评估和分级——尤其是通过无损检测技术——在农业和商业中变得越来越重要。本文设计了一种新的橘子注意力（AO）机制和多尺度特征优化（MFO）特征提取模块，并与VGG13卷积神经网络（CNN）相结合，创新性地提出了VGG-MFO-橘子CNN模型，用于准确分类不同甜度的蜜橘。首先，采集了临海蜜橘样本，利用图像采集和糖分检测，形成了一个包含5022张图像的甜度三重分类数据集。然后，将所提出的模型与六种有影响力的经典CNN模型进行对比训练：DenseNet121、MobileNet_v2、ResNet50、ShuffleNet、VGG13和VGG13_bn。实验结果表明，我们的模型在验证集上的准确率达到了86.8%，明显优于其他六种模型。它还在预测临海蜜橘甜度方面表现出了出色的泛化能力和有效性。因此，我们的模型可以为农业生产提供一种高效的水果分级方法，有助于农业现代化，并提高农产品在市场上的竞争力。
