基于深度学习的香蕉成熟度自动分级

目的:快速、准确分类香蕉成熟度。方法:采集不同成熟度的香蕉图像并建立图库,利用多种神经网络作为分类器提取香蕉特征,通过迁移学习对香蕉6个成熟度等级进行分类,并对最适合进行香蕉成熟度分类的网络模型进行改进,设计简易香蕉成熟度实时检测界面,最后验证模型的可行性和实用性。结果:AlexNet模型最适合用于香蕉成熟度分类,准确率最高,可达到95.56%;通过修改其全连接层结构改进AlexNet模型,模型准确率再提升1.11%。结论:AlexNet模型可快速准确识别并分类不同成熟度的香蕉。

Automatic classification of banana ripeness based on deep learning

Objective: To classify banana ripeness quickly and accurately. Methods: Collect the bananas images of different maturity and establish gallery, using a variety of different neural networks as a classifier, banana feature extracting by migration study classifying banana six maturity level, access to the most suitable for banana maturity classification network model, network model, based on the improved and easily banana maturity real-time detection interface design, Finally, the feasibility and practicability of the model were verified. Results: AlexNet model was most suitable for banana maturity classification with the highest accuracy of 95.56%. AlexNet model was improved by modifying its full-connection layer structure, and the model accuracy was further improved by 1.11%. Conclusion: AlexNet model can quickly and accurately identify and classify bananas of different maturity.