MobileNet Based Apple Leaf Diseases Identification
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MobileNet Based Apple Leaf Diseases Identification Chongke Bi 1 & Jiamin Wang 1 & Yulin Duan 2 & Baofeng Fu 1 & Jia-Rong Kang 3 & Yun Shi 2
# Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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. Keywords Apple leaf diseases . Mobile device . MobileNet . Deep learning
1 Introduction With a high nutritional and medicinal value, apple is one of the most productive types of fruit in the world. However, various diseases occur frequently on a large scale in apple production, such as Apple Alternaria leaf blotch (caused by Alternaria alternata f.sp. mali), and Apple rust (caused by Pucciniaceae glue rust), which affect the quality of fruits and thereby causing substantial economic losses. Currently, the apple leaf diseases are mainly inspected by experienced experts. They need to check the apple leaves one by one. This is a huge job. The number of leaves for one apple tree is large enough. For a whole apple yield, we do not have enough experienced experts to finish such kind of inspection
* Yulin Duan [email protected] 1
College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
2
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
3
Department of Information Management, Tatung University, Taipei 104, Taiwan
task. Furthermore, a large number of errors will be appeared when these experts become tired, especially for some similar leaf diseases. Therefore, we need an algorithm to help farmers to resolve this problem. This algorithm can let non-experience-farmers to identify these apple leaf diseases without the helps from experts. This algorithm should have the following 3 merits: – – –
Stable: the
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