A pepper quality detection and classification model based on transfer learning combined with convolutional neural network is proposed as a solution for low efficiency of manual pepper sorting at the current stage. The pepper dataset was amplified with data pre-processing methods including rotation, luminance switch, and contrast ratio switch. To improve training speed and precision, a network model was optimized with a fine-tuned VGG 16 model in this research, transfer learning was applied after parameter optimization, and comparative analysis was performed by combining ResNet50, MobileNet V2, and GoogLeNet models. It turned out that the VGG 16 model output anticipation precision was 98.14%, and the prediction loss rate was 0.0669 when the dropout was settled as 0.3, learning rate settled as 0.000001, batch normalization added, and ReLU as activation function. Comparing with other finetune models and network models, this model was of better anticipation performance, as well as faster and more stable convergence rate, which embodied the best performance. Considering the basis of transfer learning and integration with strong generalization and fitting capacity of the VGG 16 finetune model, it is feasible to apply this model to the external quality classification of pepper, thus offering technical reference for further realizing the automatic classification of pepper quality.

中文翻译：

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基于迁移学习结合卷积神经网络的胡椒外部质量检测研究

针对现阶段辣椒人工分选效率低下的问题，提出了一种基于迁移学习结合卷积神经网络的辣椒品质检测与分类模型。通过旋转、亮度切换、对比度切换等数据预处理方法对胡椒数据集进行放大。为了提高训练速度和精度，本研究使用微调的VGG 16模型优化网络模型，参数优化后应用迁移学习，并结合ResNet50、MobileNet V2和GoogLeNet模型进行对比分析。结果发现VGG 16模型输出预测精度为98.14%，当dropout定为0.3时，预测损失率为0.0669，学习率定为0.000001，添加batch normalization，和 ReLU 作为激活函数。与其他finetune模型和网络模型相比，该模型具有更好的预期性能，收敛速度更快更稳定，体现了最佳性能。考虑到VGG 16微调模型具有迁移学习和集成能力强的泛化和拟合能力，将该模型应用于辣椒的外部品质分类是可行的，为进一步实现辣椒品质的自动分类提供技术参考。