Research and Implementation of Organic Cucumber Intelligent Greenhouse Monitoring System Based on NB-IoT and Raspberry Pi

DOI

10.2991/aer.k.201203.029How to use a DOI?

Keywords

NB-IoT, Deep learning, Convolutional Neural Network, Disease identification, STM32

Abstract

With the development and popularization of computer technology, Internet of Things technology and artificial intelligence technology, the application of machine learning, especially deep learning is receiving increasing attention. In deep learning, CNN(Convolutional Neural Network) have demonstrated their advantages in image processing and have been widely used in various artificial intelligence practice projects with outstanding performance. This article mainly combines artificial intelligence technology and IoT technology based on NB-IoT and STM32 embedded systems to complete the intelligent monitoring and detection of the greenhouse environment in which organic cucumbers grow. In this paper, we use deep learning and convolutional neural network to detect and identify the organic cucumber leaf spot, angular leaf spot, anthracnose and bacterial leaf blight, using STM32 embedded system to realize the detection and control of greenhouse atmosphere environment, soil environment, automatic watering and so on. At the same time, the Alibaba Cloud platform is used to remotely monitor and control these. Through the application of all the above technologies, the intelligent growth of organic cucumbers in the greenhouse is achieved, reducing labor and time costs, and improving production efficiency.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yanyan Cao
AU  - Ligong Cui
AU  - Qian Lv
PY  - 2020
DA  - 2020/12/03
TI  - Research and Implementation of Organic Cucumber Intelligent Greenhouse Monitoring System Based on NB-IoT and Raspberry Pi
BT  - Proceedings of the 2020 9th International Conference on Applied Science, Engineering and Technology (ICASET 2020)
PB  - Atlantis Press
SP  - 157
EP  - 161
SN  - 2352-5401
UR  - https://doi.org/10.2991/aer.k.201203.029
DO  - 10.2991/aer.k.201203.029
ID  - Cao2020
ER  -