MobileNet Based Apple Leaf Diseases Identification

文献类型： 外文期刊

第一作者： Bi, Chongke

作者： Bi, Chongke;Wang, Jiamin;Fu, Baofeng;Duan, Yulin;Shi, Yun;Kang, Jia-Rong

作者机构：

关键词： Apple leaf diseases; Mobile device; MobileNet; Deep learning

期刊名称：MOBILE NETWORKS & APPLICATIONS （ 影响因子：3.426； 五年影响因子：2.783 ）

ISSN： 1383-469X

年卷期：

页码：

收录情况： SCI

摘要： Alternaria leaf blotch, and rust are two common types of apple leaf diseases that severely affect apple yield. A timely and effective detection of apple leaf diseases is crucial for ensuring the healthy development of the apple industry. In general, these diseases are inspected by experienced experts one by one. This is a time-consuming task with unstable precision. Therefore, in this paper, we proposed a LOW-COST, STABLE, HIGH precision apple leaf diseases identification method. This is achieved by employing MobileNet model. Firstly, comparing with general deep learning model, it is a LOW-COST model because it can be easily deployed on mobile devices. Secondly, instead of experienced experts, everyone can finish the apple leaf diseases inspection STABLELY by the help of our algorithm. Thirdly, the precision of MobileNet is nearly the same with existing complicated deep learning models. Finally, in order to demonstrated the effectiveness of our proposed method, several experiments have been carried out for apple leaf diseases identification. We have compared the efficiency and precision with the famous CNN models: i.e. ResNet152 and InceptionV3. Here, the apple disease datasets (including classes: Alternaria leaf blotch and rust leaf) were collected by the agriculture experts in Shaanxi Province, China.

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