Author(s)

ZePing Liu^1*, KeXin Sun¹, LeShui Qiao¹, XinTao Wang¹, ChuanDi Xu¹, ZhongPeng Zhang¹, GuiSheng Miao², YuShuo Han³, Yuan Ji⁴, Xiang Ji⁵

Affiliation(s)

¹Trine Engineering Institute, Shaanxi University of Technology, HanZhong 723000, Shaanxi, China.

²School of Physics and Telecommunication Engineering, Shaanxi University of Technology, HanZhong 723000, Shaanxi, China.

³School of Economics and Management, Henan Institute of Science and Technology, XinXiang 453000, Henan, China.

⁴College of Engineering, University of Illinois Chicago, Chicago 60607, America.

⁵Academy of Martial Arts and Performance, Capital University of Physical Education And Sports, Beijing 100091, China.

Corresponding Author

ZePing Liu

ABSTRACT

With the growth of the global population and the rise of agricultural demand, crop pest monitoring and control has become a key link to ensure food security. The traditional manual monitoring method has low efficiency and poor accuracy, which is difficult to meet the needs of modern agriculture. Therefore, a monitoring system for agricultural pests and diseases based on machine vision was designed, which combined with image recognition, deep learning, embedded system and wireless communication technology to realize real-time monitoring and automatic identification of pests. The experimental results show that the identification accuracy of common pests is 92%, the data transmission is stable, and the average response time is 10 seconds, which can meet the real-time monitoring needs of farmland. The introduction of solar powered systems further reduces maintenance costs and improves the sustainability of the system. This study provides an efficient and accurate solution for agricultural pest monitoring, which is of great significance for improving agricultural production efficiency and ensuring food security.

KEYWORDS

Disease and insect pest detection; Machine vision; STM 32 microcontroller; Deep learning

CITE THIS PAPER

ZePing Liu, KeXin Sun, LeShui Qiao, XinTao Wang, ChuanDi Xu, ZhongPeng Zhang, GuiSheng Miao, YuShuo Han, Yuan Ji, Xiang Ji. Agricultural disease and pest detection based on machine vision. Journal of Computer Science and Electrical Engineering. 2025, 7(3): 21-39. DOI: https://doi.org/10.61784/jcsee3053.

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