Recognition of Cordyceps Based on Machine Vision and Deep Learning

Zihao Xia, Aimin Sun, Hangdong Hou, Qingfeng Song, Hongli Yang, Liyong Ma () and Fang Dong ()
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Zihao Xia: School of Mechanical Engineering, Hebei University of Architecture, Zhangjiakou 075051, China
Aimin Sun: School of Mechanical Engineering, Hebei University of Architecture, Zhangjiakou 075051, China
Hangdong Hou: School of Mechanical Engineering, Hebei University of Architecture, Zhangjiakou 075051, China
Qingfeng Song: School of Mechanical Engineering, Hebei University of Architecture, Zhangjiakou 075051, China
Hongli Yang: School of Mechanical Engineering, Hebei University of Architecture, Zhangjiakou 075051, China
Liyong Ma: School of Mechanical Engineering, Hebei University of Architecture, Zhangjiakou 075051, China
Fang Dong: Light Alloy Research Institute, Central South University, Changsha 410083, China

Agriculture, 2025, vol. 15, issue 7, 1-26

Abstract: In a natural environment, due to the small size of caterpillar fungus, its indistinct features, similar color to surrounding weeds and background, and overlapping instances of caterpillar fungus, identifying caterpillar fungus poses significant challenges. To address these issues, this paper proposes a new MRAA network, which consists of a feature fusion pyramid network (MRFPN) and the backbone network N-CSPDarknet53. MRFPN is used to solve the problem of weak features. In N-CSPDarknet53, the Da-Conv module is proposed to address the background and color interference problems in shallow feature maps. The MRAA network significantly improves accuracy, achieving an accuracy rate of 0.202 AP S for small-target recognition, which represents a 12% increase compared to the baseline of 0.180 AP S . Additionally, the model size is small (9.88 M), making it lightweight. It is easy to deploy in embedded devices, which greatly promotes the development and application of caterpillar fungus identification.

Keywords: caterpillar fungus recognition; small target detection; lightweight model; feature fusion network; attention mechanism (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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