A Lightweight Detection Method for Blueberry Fruit Maturity Based on an Improved YOLOv5 Algorithm

Feng Xiao, Haibin Wang (), Yueqin Xu and Zhen Shi
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Feng Xiao: College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
Haibin Wang: College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
Yueqin Xu: College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
Zhen Shi: College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China

Agriculture, 2023, vol. 14, issue 1, 1-18

Abstract: In order to achieve accurate, fast, and robust recognition of blueberry fruit maturity stages for edge devices such as orchard inspection robots, this research proposes a lightweight detection method based on an improved YOLOv5 algorithm. In the improved YOLOv5 algorithm, the ShuffleNet module is used to achieve lightweight deep-convolutional neural networks. The Convolutional Block Attention Module (CBAM) is also used to enhance the feature fusion capability of lightweight deep-convolutional neural networks. The effectiveness of this method is evaluated using the blueberry fruit dataset. The experimental results demonstrate that this method can effectively detect blueberry fruits and recognize their maturity stages in orchard environments. The average recall ( R ) of the detection is 92.0%. The mean average precision (mAP) of the detection at a threshold of 0.5 is 91.5%. The average speed of the detection is 67.1 frames per second (fps). Compared to other detection algorithms, such as YOLOv5, SSD, and Faster R-CNN, this method has a smaller model size, smaller network parameters, lower memory usage, lower computation usage, and faster detection speed while maintaining high detection performance. It is more suitable for migration and deployment on edge devices. This research can serve as a reference for the development of fruit detection systems for intelligent orchard devices.

Keywords: blueberry fruit; deep learning; machine vision; object detection; YOLOv5 (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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