Real-Time Detection Technology of Corn Kernel Breakage and Mildew Based on Improved YOLOv5s

Mingming Liu, Yinzeng Liu, Qihuan Wang, Qinghao He and Duanyang Geng ()
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Mingming Liu: School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
Yinzeng Liu: Mechanical and Electronic Engineering College, Shandong Agriculture and Engineering University, Jinan 250100, China
Qihuan Wang: School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
Qinghao He: School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
Duanyang Geng: School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China

Agriculture, 2024, vol. 14, issue 5, 1-16

Abstract: In order to solve low recognition of corn kernel breakage degree and corn kernel mildew degree during corn kernel harvesting, this paper proposes a real-time detection method for corn kernel breakage and mildew based on improved YOlOv5s, which is referred to as the CST-YOLOv5s model algorithm in this paper. The method continuously obtains images through the discrete uniform sampling device of corn kernels and generates whole corn kernels, breakage corn kernels, and mildew corn kernel dataset samples. We aimed at the problems of high similarity of some corn kernel features in the acquired images and the low precision of corn kernel breakage and mildew recognition. Firstly, the CBAM attention mechanism is added to the backbone network of YOLOv5s to finely allocate and process the feature information, highlighting the features of corn breakage and mildew. Secondly, the pyramid pooling structure SPPCPSC, which integrates cross-stage local networks, is adopted to replace the SPPF in YOLOv5s. SPP and CPSC technologies are used to extract and fuse features of different scales, improving the precision of object detection. Finally, the original prediction head is converted into a transformer prediction head to explore the prediction potential with a multi-head attention mechanism. The experimental results show that the CST-YOLOv5s model has a significant improvement in the detection of corn kernel breakage and mildew. Compared with the original YOLOv5s model, the average precision ( AP ) of corn kernel breakage and mildew recognition increased by 5.2% and 7.1%, respectively, and the mean average precision ( mAP ) of all kinds of corn kernel recognition is 96.1%, and the frame rate is 36.7 FPS. Compared with YOLOv4-tiny, YOLOv6n, YOLOv7, YOLOv8s, and YOLOv9-E detection model algorithms, the CST-YOLOv5s model has better overall performance in terms of detection accuracy and speed. This study can provide a reference for real-time detection of breakage and mildew kernels during the harvesting process of corn kernels.

Keywords: corn kernels; breakage; mildew; YOLOv5s; CBAM; SPPCPSC; transformer (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: 2024
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