RIC-Net: A plant disease classification model based on the fusion of Inception and residual structure and embedded attention mechanism
RIC-Net: A plant disease classification model based on the fusion of Inception and residual structure and embedded attention mechanism
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DOI：
10.1016/j.compag.2021.106644
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发表时间：
2022-02
期刊：
影响因子：
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通讯作者：
Yun Zhao;Chengda Sun;Xing Xu;Jiagui Chen
中科院分区：
文献类型：
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作者：
Yun Zhao;Chengda Sun;Xing Xu;Jiagui Chen
本文提出了一种基于Inception和残差结构的卷积神经网络，并嵌入了改进的卷积块注意模块（CBAM），旨在提高植物叶病的分类能力。玉米、土豆和西红柿是中国南方种植最多的谷物。这三种作物的叶片非常脆弱和敏感，易受叶部疾病的影响，如玉米的叶枯病、马铃薯的晚疫病和番茄的花叶病毒。这些疾病不能在早期阶段发现。因此，通过深度学习技术来检测农作物的病害类别，可以有效地防止病害的传播，保证植物的正常生长。在本实验中，我们的模型取得了99.55%的总体准确率为玉米，马铃薯和番茄的三种疾病的识别。此外，我们还分别对这三种植物进行了测试。对玉米、马铃薯和番茄的分类准确率分别为98.44%、99.43%和95.20%。我们还开发了一个基于Web的实时植物病害分类系统，并部署了我们的模型。该系统在时间和准确性评价指标上具有良好的性能。实验结果表明，与现有的图像分类模型相比，该模型具有参数少、训练时间短、识别精度高等优点。
In this paper, we proposed a convolutional neural network based on Inception and residual structure with an embedded modified convolutional block attention module (CBAM), aiming to improve the classification of plant leaf diseases. Corn, potatoes and tomatoes are the most cultivated grains in southern China. The leaves of the three crops are very fragile and sensitive and are susceptible to leaf diseases, such as leaf blight of corn, late blight of potato and mosaic virus of tomato. These diseases cannot be identified at early stages. Therefore, an efficient solution is proposed by deep learning techniques to detect the disease categories of crops, which can effectively prevent the spread of diseases and ensure the normal growth of plants. In this experiment, our model achieved an overall accuracy of 99.55% for the identification of the three diseases of corn, potato and tomato. In addition, we tested the three plants individually. The classification accuracy of our model on corn, potato and tomato was 98.44%, 99.43% and 95.20%, respectively. We have also developed a web-based real-time plant disease classification system and deployed our model. The system had good performance in time and accuracy evaluation metrics. The results of this study showed that our model had fewer parameters, shorter training time, and higher recognition accuracy compared to existing image classification models.