基于STM32的农业物联网病虫害图像识别算法研究

许柏涛; 陈翔

doi:10.11959/j.issn.2096-3750.2023.00365

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基于STM32的农业物联网病虫害图像识别算法研究

理论与技术 | 更新时间：2024-08-16

- 基于STM32的农业物联网病虫害图像识别算法研究
- Research on agricultural IoT pest and disease image recognition algorithm based on STM32
- 物联网学报 2023年7卷第4期页码：132-141
- 作者机构：
- 作者简介：
  
  [ "许柏涛（1998- ），男，中山大学电子与信息工程学院硕士生，主要研究方向为物联网、图像识别、边缘计算等" ]
  [ "陈翔（1980- ），男，博士，中山大学电子与信息工程学院教授，主要研究方向为无线与移动通信、卫星通信、物联网、电信大数据" ]
- 基金信息：
  
  广东省现代农业产业技术创新团队专项基金资助项目;The Guangdong Provincial Special Fund for Modern Agriculture Industry Technology Innovation Teams(2023KJ122)
- DOI：10.11959/j.issn.2096-3750.2023.00365
  中图分类号： TP389.1
- 纸质出版日期：2023-12-20，
  
  网络出版日期：2023-12，
- 稿件说明：
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许柏涛, 陈翔. 基于STM32的农业物联网病虫害图像识别算法研究[J]. 物联网学报, 2023,7(4):132-141.

BOTAO XU, XIANG CHEN. Research on agricultural IoT pest and disease image recognition algorithm based on STM32. [J]. Chinese journal on internet of things, 2023, 7(4): 132-141.
许柏涛, 陈翔. 基于STM32的农业物联网病虫害图像识别算法研究[J]. 物联网学报, 2023,7(4):132-141. DOI： 10.11959/j.issn.2096-3750.2023.00365.

BOTAO XU, XIANG CHEN. Research on agricultural IoT pest and disease image recognition algorithm based on STM32. [J]. Chinese journal on internet of things, 2023, 7(4): 132-141. DOI： 10.11959/j.issn.2096-3750.2023.00365.

摘要

在现代农业物联网系统中，边缘计算是不可或缺的组成部分。在此背景下，可将轻量级病虫害图像识别任务置于边缘设备上，然而受限于设备计算和存储能力，该任务面临着不小的挑战。为了解决这些问题，提出了一种以经济实用的STM32为边缘设备进行病虫害图像识别的方法。该方法针对STM32的特点，基于MobileNetv2结构做出改进，并应用量化感知训练技术对神经网络模型进行压缩，提高了模型的可移植性。同时，模型使用X-CUBE-AI部署并进行了性能评估。实验结果表明，改进模型不仅保证了图像分类准确率，而且相较于其他轻量级神经网络，该模型对STM32的Flash与RAM资源的占用有所减小。

Abstract

In modern agriculture IoT systems

edge computing is an indispensable component.In this context

it is feasible to deploy lightweight pest and disease image recognition tasks on edge devices.However

due to the constraints of device computation and storage capabilities

this task faces significant challenges.To address these challenges

an economically practical method was proposed for pest and disease image recognition on STM32 edge devices.Specifically

the MobileNetv2 structure was improved to better suit the characteristics of STM32

quantization-aware training technique was used to compresses the network

model portability was enhanced.Meanwhile

the X-CUBE-AI was used to arrange the model and evaluate the performance.Experimental results demonstrate that the proposed model not only ensures image classification accuracy but also reduces the Flash and RAM resource consumption on STM32 compared to other lightweight networks.

关键词

农业物联网边缘计算病虫害识别STM32

Keywords

agricultural IoTedge computingpest and disease recognitionSTM32

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