A comparative study of fine-tuning deep learning models for plant disease identification
A comparative study of fine-tuning deep learning models for plant disease identification
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DOI：
10.1016/j.compag.2018.03.032
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发表时间：
2019-06-01
影响因子：
8.3
通讯作者：
Liu Yingchun
中科院分区：
文献类型：
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作者：
Too, Edna Chebet;Li Yujian;Liu Yingchun
深度学习最近吸引了很多关注，旨在开发一个快速，自动和准确的图像识别和分类系统。在这项工作中，重点是对最先进的深度卷积神经网络进行微调和评估，用于基于图像的植物疾病分类。对深度学习架构进行了实证比较。评估的架构包括VGG 16，Inception V4，50层，101层和152层的ResNet以及121层的DenseNet。用于实验的数据是38个不同的类，包括来自plantVillage的14种植物的叶片的患病和健康图像。快速和准确的植物病害识别模型，以便可以早期应用准确的措施。从而缓解粮食安全问题。在我们的实验中，DenseNets的准确性有随着时代数量的增加而不断提高的趋势，没有过度拟合和性能恶化的迹象。此外，DenseNets需要相当少的参数和合理的计算时间来实现最先进的性能。它达到了99.75%的测试准确率，击败了其他架构。Keras与Theano后端用于执行架构的训练。
Deep learning has recently attracted a lot of attention with the aim to develop a quick, automatic and accurate system for image identification and classification. In this work, the focus was on fine-tuning and evaluation of state-of-the-art deep convolutional neural network for image-based plant disease classification. An empirical comparison of the deep learning architecture is done. The architectures evaluated include VGG 16, Inception V4, ResNet with 50, 101 and 152 layers and DenseNets with 121 layers. The data used for the experiment is 38 different classes including diseased and healthy images of leafs of 14 plants from plantVillage. Fast and accurate models for plant disease identification are desired so that accurate measures can be applied early. Thus, alleviating the problem of food security. In our experiment, DenseNets has tendency's to consistently improve in accuracy with growing number of epochs, with no signs of overfitting and performance deterioration. Moreover, DenseNets requires a considerably less number of parameters and reasonable computing time to achieve state-of-the-art performances. It achieves a testing accuracy score of 99.75% to beat the rest of the architectures. Keras with Theano backend was used to perform the training of the architectures.