MYOLO: A Lightweight Fresh Shiitake Mushroom Detection Model Based on YOLOv3

Peichao Cong (), Hao Feng, Kunfeng Lv, Jiachao Zhou and Shanda Li
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Peichao Cong: School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China
Hao Feng: School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China
Kunfeng Lv: School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China
Jiachao Zhou: School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China
Shanda Li: School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China

Agriculture, 2023, vol. 13, issue 2, 1-23

Abstract: Fruit and vegetable inspection aids robotic harvesting in modern agricultural production. For rapid and accurate detection of fresh shiitake mushrooms, picking robots must overcome the complex conditions of the growing environment, diverse morphology, dense shading, and changing field of view. The current work focuses on improving inspection accuracy at the expense of timeliness. This paper proposes a lightweight shiitake mushroom detection model called Mushroom You Only Look Once (MYOLO) based on You Only Look Once (YOLO) v3. To reduce the complexity of the network structure and computation and improve real-time detection, a lightweight GhostNet16 was built instead of DarkNet53 as the backbone network. Spatial pyramid pooling was introduced at the end of the backbone network to achieve multiscale local feature fusion and improve the detection accuracy. Furthermore, a neck network called shuffle adaptive spatial feature pyramid network (ASA-FPN) was designed to improve fresh shiitake mushroom detection, including that of densely shaded mushrooms, as well as the localization accuracy. Finally, the Complete Intersection over Union (CIoU) loss function was used to optimize the model and improve its convergence efficiency. MYOLO achieved a mean average precision ( mAP ) of 97.03%, 29.8M parameters, and a detection speed of 19.78 ms, showing excellent timeliness and detectability with a 2.04% higher mAP and 2.08 times fewer parameters than the original model. Thus, it provides an important theoretical basis for automatic picking of fresh shiitake mushrooms.

Keywords: picking robot; fresh mushroom sorting; YOLOv3; detection model; lightweight (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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