A Lightweight Method for Peanut Kernel Quality Detection Based on SEA-YOLOv5

Zhixia Liu, Chunyu Wang, Xilin Zhong, Genhua Shi, He Zhang, Dexu Yang and Jing Wang ()
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Zhixia Liu: College of Engineering, Shenyang Agricultural University, Shenyang 110866, China
Chunyu Wang: College of Engineering, Shenyang Agricultural University, Shenyang 110866, China
Xilin Zhong: College of Engineering, Shenyang Agricultural University, Shenyang 110866, China
Genhua Shi: College of Engineering, Shenyang Agricultural University, Shenyang 110866, China
He Zhang: College of Engineering, Shenyang Agricultural University, Shenyang 110866, China
Dexu Yang: College of Engineering, Shenyang Agricultural University, Shenyang 110866, China
Jing Wang: College of Engineering, Shenyang Agricultural University, Shenyang 110866, China

Agriculture, 2024, vol. 14, issue 12, 1-18

Abstract: Peanuts are susceptible to defects such as insect damage, breakage, germinant, and mildew, leading to varying qualities of peanuts. The disparity in peanut kernel quality results in significant differences in their prices and economic value. Conducting real-time, accurate, and non-destructive quality inspections of peanut kernels can effectively increase the utilization rate and commercial value of peanuts. Manual inspections are inefficient and subjective, while photoelectric sorting is costly and less precise. Therefore, this study proposes a peanut kernel quality detection algorithm based on an enhanced YOLO v5 model. Compared to other models, this model is practical, highly accurate, lightweight, and easy to integrate. Initially, YOLO v5s was chosen as the foundational training model through comparison. Subsequently, the original backbone network was replaced with a lightweight ShuffleNet v2 network to improve the model’s ability to differentiate features among various types of peanut kernels and reduce the parameters. The ECA (Efficient Channel Attention) mechanism was introduced into the C3 module to enhance feature extraction capabilities, thereby improving average accuracy. The CIoU loss function was replaced with the alpha-IoU loss function to boost detection accuracy. The experimental results indicated that the improved model, SEA-YOLOv5, achieved an accuracy of 98.8% with a parameter count of 0.47 M and an average detection time of 11.2 ms per image. When compared to other detection models, there was an improvement in accuracy, demonstrating the effectiveness of the proposed peanut kernel quality detection model. Furthermore, this model is suitable for deployment on resource-limited embedded devices such as mobile terminals, enabling real-time and precise detection of peanut kernel quality.

Keywords: peanut kernel; quality testing; YOLOv5; lightweight; 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: 2024
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