A Counting Method of Red Jujube Based on Improved YOLOv5s

Yichen Qiao, Yaohua Hu (), Zhouzhou Zheng, Huanbo Yang, Kaili Zhang, Juncai Hou () and Jiapan Guo
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Yichen Qiao: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Yaohua Hu: College of Optical, Mechanical, and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China
Zhouzhou Zheng: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Huanbo Yang: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Kaili Zhang: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Juncai Hou: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Jiapan Guo: Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, 9747 AG Groningen, The Netherlands

Agriculture, 2022, vol. 12, issue 12, 1-20

Abstract: Due to complex environmental factors such as illumination, shading between leaves and fruits, shading between fruits, and so on, it is a challenging task to quickly identify red jujubes and count red jujubes in orchards. A counting method of red jujube based on improved YOLOv5s was proposed, which realized the fast and accurate detection of red jujubes and reduced the model scale and estimation error. ShuffleNet V2 was used as the backbone of the model to improve model detection ability and light the weight. In addition, the Stem, a novel data loading module, was proposed to prevent the loss of information due to the change in feature map size. PANet was replaced by BiFPN to enhance the model feature fusion capability and improve the model accuracy. Finally, the improved YOLOv5s detection model was used to count red jujubes. The experimental results showed that the overall performance of the improved model was better than that of YOLOv5s. Compared with the YOLOv5s, the improved model was 6.25% and 8.33% of the original network in terms of the number of model parameters and model size, and the Precision, Recall, F1-score, AP, and Fps were improved by 4.3%, 2.0%, 3.1%, 0.6%, and 3.6%, respectively. In addition, RMSE and MAPE decreased by 20.87% and 5.18%, respectively. Therefore, the improved model has advantages in memory occupation and recognition accuracy, and the method provides a basis for the estimation of red jujube yield by vision.

Keywords: count red jujubes; red jujube; improved YOLOv5s; ShuffleNet V2 Unit; Stem; BiFPN (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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