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International Conference on Machine Learning for Cyber Security

ML4CS 2020: Machine Learning for Cyber Security pp 206–220Cite as

Machine Learning Agricultural Application Based on the Secure Edge Computing Platform

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Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12486))

Abstract

Machine learning (ML) is fast becoming a powerful tool for increasing agricultural production, for instance, ML predicts weather and yield through satellite images. Such approaches, however, most applications are based on expensive cloud servers to handle massive calculations, which will easily lead to important data leakage (e.g. crop cultivation methods, annual output, etc.), and increase the economic burden of farmers. Motivated by the status of agriculture, especially small farms that are underfunded and require data confidentiality. We propose an application for agricultural object detection based on a secure edge computing platform that can be used for agricultural output statistics and automated spraying agriculture. Compared with cloud-based ML agricultural applications, edge-based computing platforms do not require data uploading and downloading, which protects data security and reduces capital costs. We use some techniques in the training model and compression model to solve the problems of model accuracy and real-time, as follows: (1) Category-based Assisted Excitation model is proposed to improve the YOLOv3 accuracy. (2) Use layer pruning and channel pruning together to achieve a small-scale structure search and maximize real-time performance. We have evaluated our optimized model based on NVIDIA Jetson TX2 and Fruit Dataset. Its frames per second (FPS) is 11.63, mean average precision (MAP) is 89.6%.

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Author information

Authors and Affiliations

  1. Suzhou Institute for Advanced Study, University of Science and Technology of China, Suzhou, 215000, China

    Wu Fan, Huanghe Liu & Zhu Zongwei

  2. Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China

    Zhuoqun Xu

Authors
  1. Wu Fan
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  2. Zhuoqun Xu
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  3. Huanghe Liu
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  4. Zhu Zongwei
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Corresponding author

Correspondence to Huanghe Liu .

Editor information

Editors and Affiliations

  1. Xidian University, Xi'an, China

    Xiaofeng Chen

  2. Guangzhou University, Guangzhou, China

    Hongyang Yan

  3. Michigan State University, East Lansing, MI, USA

    Qiben Yan

  4. Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science, Thuwal, Saudi Arabia

    Xiangliang Zhang

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Cite this paper

Fan, W., Xu, Z., Liu, H., Zongwei, Z. (2020). Machine Learning Agricultural Application Based on the Secure Edge Computing Platform. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12486. Springer, Cham. https://doi.org/10.1007/978-3-030-62223-7_18

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  • DOI: https://doi.org/10.1007/978-3-030-62223-7_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62222-0

  • Online ISBN: 978-3-030-62223-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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