Apple Grading Method Design and Implementation for Automatic Grader Based on Improved YOLOv5

Bo Xu, Xiang Cui, Wei Ji (), Hao Yuan and Juncheng Wang
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Bo Xu: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Xiang Cui: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Wei Ji: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Hao Yuan: School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China
Juncheng Wang: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2023, vol. 13, issue 1, 1-18

Abstract: Apple grading is an essential part of the apple marketing process to achieve high profits. In this paper, an improved YOLOv5 apple grading method is proposed to address the problems of low grading accuracy and slow grading speed in the apple grading process and is experimentally verified by the designed automatic apple grading machine. Firstly, the Mish activation function is used instead of the original YOLOv5 activation function, which allows the apple feature information to flow in the deep network and improves the generalization ability of the model. Secondly, the distance intersection overUnion loss function (DIoU_Loss) is used to speed up the border regression rate and improve the model convergence speed. In order to refine the model to focus on apple feature information, a channel attention module (Squeeze Excitation) was added to the YOLOv5 backbone network to enhance information propagation between features and improve the model’s ability to extract fruit features. The experimental results show that the improved YOLOv5 algorithm achieves an average accuracy of 90.6% for apple grading under the test set, which is 14.8%, 11.1%, and 3.7% better than the SSD, YOLOv4, and YOLOv5s models, respectively, with a real-time grading frame rate of 59.63 FPS. Finally, the improved YOLOv5 apple grading algorithm is experimentally validated on the developed apple auto-grader. The improved YOLOv5 apple grading algorithm was experimentally validated on the developed apple auto grader. The experimental results showed that the grading accuracy of the automatic apple grader reached 93%, and the grading speed was four apples/sec, indicating that this method has a high grading speed and accuracy for apples, which is of practical significance for advancing the development of automatic apple grading.

Keywords: apple grader; YOLOv5; attention mechanism SE; DIoU_Loss; mish (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 (3)

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