Improved Apple Fruit Target Recognition Method Based on YOLOv7 Model

Huawei Yang, Yinzeng Liu, Shaowei Wang, Huixing Qu, Ning Li, Jie Wu, Yinfa Yan, Hongjian Zhang, Jinxing Wang () and Jianfeng Qiu ()
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Huawei Yang: College of Mechanical and Electrical Engineering, Shandong Agricultural University, Tai’an 271002, China
Yinzeng Liu: Shandong Academy of Agricultural Machinery Sciences, Jinan 250010, China
Shaowei Wang: Shandong Academy of Agricultural Machinery Sciences, Jinan 250010, China
Huixing Qu: College of Mechanical and Electrical Engineering, Shandong Agricultural University, Tai’an 271002, China
Ning Li: Shandong Academy of Agricultural Machinery Sciences, Jinan 250010, China
Jie Wu: College of Mechanical and Electrical Engineering, Shandong Agricultural University, Tai’an 271002, China
Yinfa Yan: College of Mechanical and Electrical Engineering, Shandong Agricultural University, Tai’an 271002, China
Hongjian Zhang: College of Mechanical and Electrical Engineering, Shandong Agricultural University, Tai’an 271002, China
Jinxing Wang: College of Mechanical and Electrical Engineering, Shandong Agricultural University, Tai’an 271002, China
Jianfeng Qiu: College of Radiology, Shandong First Medical University, Tai’an 271000, China

Agriculture, 2023, vol. 13, issue 7, 1-21

Abstract: This study proposes an improved algorithm based on the You Only Look Once v7 (YOLOv7) to address the low accuracy of apple fruit target recognition caused by high fruit density, occlusion, and overlapping issues. Firstly, we proposed a preprocessing algorithm for the split image with overlapping to improve the robotic intelligent picking recognition accuracy. Then, we divided the training, validation, and test sets. Secondly, the MobileOne module was introduced into the backbone network of YOLOv7 to achieve parametric fusion and reduce network computation. Afterward, we improved the SPPCSPS module and changed the serial channel to the parallel channel to enhance the speed of image feature fusion. We added an auxiliary detection head to the head structure. Finally, we conducted fruit target recognition based on model validation and tests. The results showed that the accuracy of the improved YOLOv7 algorithm increased by 6.9%. The recall rate increased by 10%, the mAP1 algorithm increased by 5%, and the mAP2 algorithm increased by 3.8%. The accuracy of the improved YOLOv7 algorithm was 3.5%, 14%, 9.1%, and 6.5% higher than that of other control YOLO algorithms, verifying that the improved YOLOv7 algorithm could significantly improve the fruit target recognition in high-density fruits.

Keywords: deep learning; apple; object detection; data augmentation (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 (5)

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