Research on agricultural IoT pest and disease image recognition algorithm based on STM32

Botao XU; Xiang CHEN

doi:10.11959/j.issn.2096-3750.2023.00365

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Research on agricultural IoT pest and disease image recognition algorithm based on STM32

Theory and Technology | 更新时间：2024-08-16

- Research on agricultural IoT pest and disease image recognition algorithm based on STM32
- Chinese Journal on Internet of Things Vol. 7, Issue 4, Pages: 132-141(2023)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2023.00365
  CLC： TP389.1
- Online First：2023-12，
  
  Published：20 December 2023
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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：

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

Keywords

references

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