刘洋, 冯全, 王书志. 基于轻量级CNN的植物病害识别方法及移动端应用[J]. 农业工程学报, 2019, 35(17): 194-204. DOI: 10.11975/j.issn.1002-6819.2019.17.024
    引用本文: 刘洋, 冯全, 王书志. 基于轻量级CNN的植物病害识别方法及移动端应用[J]. 农业工程学报, 2019, 35(17): 194-204. DOI: 10.11975/j.issn.1002-6819.2019.17.024
    Liu Yang, Feng Quan, Wang Shuzhi. Plant disease identification method based on lightweight CNN and mobile application[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(17): 194-204. DOI: 10.11975/j.issn.1002-6819.2019.17.024
    Citation: Liu Yang, Feng Quan, Wang Shuzhi. Plant disease identification method based on lightweight CNN and mobile application[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(17): 194-204. DOI: 10.11975/j.issn.1002-6819.2019.17.024

    基于轻量级CNN的植物病害识别方法及移动端应用

    Plant disease identification method based on lightweight CNN and mobile application

    • 摘要: 为了实现在手机端植物病害叶片检测,对MobileNet和Inception V3 2种轻量级卷积神经网络进行迁移学习,得到2种作物病害分类模型,将2种分类模型分别移植到Android手机端,在识别精度、运算速度和网络尺寸之间进行平衡,选择最优模型。试验表明,MobileNet和Inception V3在PlantVillage数据集(共38类26种病害)上平均识别率分别是95.02%和95.62%。在自建图像集葡萄病害叶片的识别中MobileNet和Inception V3平均识别率分别是87.50%、88.06%,Inception V3的整体识别精度略高,但MobileNet在所有类别的识别上均衡性更好;在模型尺寸方面Inception V3的模型尺寸大小为87.5 MB,MobileNet的模型尺寸为17.1 MB,大约是后者5倍;2种模型移植到手机端时,MobileNet和Inception V3的APP所占内存分别是21.5 和125 MB;在手机端单张图片的识别时间方面,Inception V3平均计算时间约是174 ms,MobileNet的平均计算时间约是134 ms,后者的平均计算时间比前者快40 ms;在手机端MobileNet相比于Inception V3占用内存更小,运算时间更快。说明MobileNet更适合在手机端进行植物病害识别应用。

       

      Abstract: Abstract: Because of the cultivation environment, management level, climate and other conditions, there are a variety of different types of diseases over the plant growth period. The problem of plant diseases in agricultural production has become one of the important factors that restrict crop growth. Timely and accurate identification of disease types is of great significance for effective disease control. The disease identification methods based on deep neural network have achieved very high recognition accuracy. However, the ordinary deep neural networks are too large in size and mainly running on computers. At present, smart phones are more and more popular, and a disease recognition system is very meaningful if it can be run on the phones directly. In this paper, the lightweight convolutional neural network (CNN) was employed to design a mobile APP for plant disease identification, which could be easily used in the field. The lightweight networks used in this paper included MobileNet and Inception V3. We had compared the performance of the 2 methods in several aspects in order to get the best model and transplant to the mobile phone. We pre-trained the 2 network models on the ImageNet dataset and then migrated on the PlantVillage dataset and the self-built grape disease dataset. Disease classification test experiments were performed on the 2 sets using the 2 training models obtained. The experimental results showed that MobileNet and Inception V3 had an average recognition accuracy of 95.02% and 95.62% over the PlantVillage dataset (a total of 38 species), and 87.50%, 88.06% respectively over the grape disease dataset. The recognition accuracy of the 2 networks was generally consistent. For the PlantVillage dataset, the recognition rates of tomato leaf early blight were the lowest in the 2 databases, 70% and 68%, respectively for MobileNet and Inception V3. For the grape downy mildew on the dataset, the 2 networks also showed the lowest recognition rates, which were 76.67% and 68.33%, respectively. MobileNet's lowest disease recognition rate was higher than that of Inception V3, which meant that MobileNet was more balanced than Inception V3 in the accuracy of multiple species of disease identification and was more suitable for practical use. In terms of model size, Inception V3 was 87.5 MB, and MobileNet was 17.1 MB. The former was about 5 times larger than the latter. The APP with Inception V3 and MobileNet occupied 125 and 21.5 MB of mobile phone memory respectively. To identify a single picture on the phone, APP with Inception V3 took 174 ms while APP with MobileNet took only 134, 40 ms faster. In summary, the classification accuracy of the 2 network was very close, but MobileNet required less memory and ran faster than Inception V3, indicating that the former was more suitable for mobile phone applications. The images in the PlantVillage dataset were taken indoors and the background image was simple, thus the recognition accuracy over it was high. However, the accuracy decreased over the grape disease dataset in which the images were collected in natural conditions, indicating that the network was significantly affected by external environment such as illumination changes and background. In the follow-up study, we will collect more plant disease leaves taken under natural conditions for training to develop a more robust APP for plant disease identification.

       

    /

    返回文章
    返回