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MobileNet Based Apple Leaf Diseases Identification

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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.

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Acknowledgements

This work is partially supported by the National Natural Science Foundation of China under Grant No. 61702360.

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Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China

    Chongke Bi, Jiamin Wang & Baofeng Fu

  2. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China

    Yulin Duan & Yun Shi

  3. Department of Information Management, Tatung University, Taipei, 104, Taiwan

    Jia-Rong Kang

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  1. Chongke Bi
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  2. Jiamin Wang
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  3. Yulin Duan
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  4. Baofeng Fu
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  5. Jia-Rong Kang
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  6. Yun Shi
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Correspondence to Yulin Duan.

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Bi, C., Wang, J., Duan, Y. et al. MobileNet Based Apple Leaf Diseases Identification. Mobile Netw Appl 27, 172–180 (2022). https://doi.org/10.1007/s11036-020-01640-1

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