基于STM32的农业物联网病虫害图像识别算法研究

doi:10.11959/j.issn.2096-3750.2023.00365

Abstract

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.

Key words: agricultural IoT, edge computing, pest and disease recognition, STM32

CLC Number:

TP389.1

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.

Figures/Tables 12

输入	运行	输出
h×w×k	1×1 Conv2d, ReLU6	h×w×tk
h×w×tk	3×3 dwise, ReLU6	$\frac{h}{s} \times \frac{w}{s} \times t k$
$\frac{h}{s} \times \frac{w}{s} \times t k$	1×1 Conv2d	$\frac{h}{s} \times \frac{w}{s} \times k^{'}$

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输入	运行	t	c	n	s
160²×3	Conv2d	-	32	1	2
80²×32	STM32-block	-	16	1	1
80²×16	bottleneck	6	24	2	2
40²×24	STM32-block	-	32	2	1
40²×32	bottleneck	6	64	2	2
20²×64	STM32-block	-	96	3	1
20²×96	bottleneck	6	160	2	2
10²×160	STM32-block	-	320	1	1
10²×320	Avgpool	-	-	1	-
1×1×320	Conv2d 1×1	-	1 280	1	1
1×1×1 280	Conv2d 1×1	-	k

类别	数量/张
Alternaria_Boltch	5 343
Brown_Spot	5 655
Grey_Spot	4 810
Mosaic	4 875
Rust	5 694

类别	数量/张
Early_Blight_Fungus	792
Healthy	1 381
Late_Blight_Water_Mold	1 513
Leaf_Mold_Fungus	755
Powdery_Mildew	1 469
Septoria_Leaf_Spot_Fungus	1 403
Spider_Mite_Damage	929

网络模型	准确率（C model）	准确率（Python model）	Flash/KB	RAM/KB	参数量	CPU推理时间/s
STM32-MobileNet（×0.35）	90.69%	91.89%	256.52	137.26	139 038	512.745
FD_MobileNet（×0.35）	86.39%	88.72%	324.49	96.12	251 429	300.733
MobileNet（×0.35）	90.71%	93.67%	425.40	234.63	319 232	978.16
MobileNetv2（×0.35）	90.67%	92.44%	498.41	165.20	420 667	661.169

网络模型	准确率（C model）	准确率（Python model）	Flash/KB	RAM/KB	参数量	CPU推理时间/s
STM32-MobileNet（×0.35）	87.28%	87.40%	257.84	137.37	139 919	158.373
FD_mobilenet（×0.35）	80.01%	84.37%	325.20	96.12	252 147	98.934
MobileNetv2（×0.35）	87.52%	89.76%	436.23	237.25	320 134	216.960

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

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