Early Detection of Surface Mildew in Maize Kernels Using Machine Vision Coupled with Improved YOLOv5 Deep Learning Model

被引:0
|
作者
Xia, Yu [1 ]
Shen, Ao [1 ]
Che, Tianci [1 ]
Liu, Wenbo [1 ]
Kang, Jie [1 ]
Tang, Wei [1 ]
机构
[1] Shaanxi Univ Sci & Technol, Sch Elect & Control Engn, Xian 710021, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 22期
基金
中国国家自然科学基金;
关键词
deep learning; machine vision; YOLOv5s; maize seed; mildew detection;
D O I
10.3390/app142210489
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Mildew in maize kernels is typically caused by various fungi, necessitating prompt detection and treatment to minimize losses during harvest and storage. In this study, a deep learning YOLOv5s algorithm based on machine vision technology was employed to develop a maize seed surface mildew detection model and to enhance its portability for deployment on additional mobile devices. To guarantee the fruitful progression of this research, an initial experiment was conducted on maize seeds to obtain a sufficient number of images of mildewed maize kernels, which were classified into three grades (sound, mild, and severe). Subsequently, a maize seed image was extracted to create an image of a single maize seed, which was then divided to establish the data set. An enhanced YOLOv5s-ShuffleNet-CBAM model was ultimately developed. The results demonstrated that the model achieved with an mAP50 value of 0.955 and a model size of 2.4 MB. This resulted in a notable reduction in the model parameters and calculation amount while simultaneously enhancing model precision. Furthermore, K-fold cross-validation demonstrated the model stability, and Grad-CAM validated the model effectiveness. In the future, the proposed lightweight model in this study can be applied to other crops in the context of portable or online inspection systems, thus advancing effective and high-quality agricultural applications.
引用
收藏
页数:19
相关论文
共 50 条
  • [1] An Improved YOLOv5 Model: Application to Mixed Impurities Detection for Walnut Kernels
    Yu, Lang
    Qian, Mengbo
    Chen, Qiang
    Sun, Fuxing
    Pan, Jiaxuan
    FOODS, 2023, 12 (03)
  • [2] Improved Detection and Tracking of Objects Based on a Modified Deep Learning Model (YOLOv5)
    Nife N.I.
    Chtourou M.
    International Journal of Interactive Mobile Technologies, 2023, 17 (21): : 145 - 160
  • [3] Improved YOLOv5 Based Deep Learning System for Jellyfish Detection
    Pham, Thi-Ngot
    Nguyen, Viet-Hoan
    Kwon, Ki-Ryong
    Kim, Jae-Hwan
    Huh, Jun-Ho
    IEEE ACCESS, 2024, 12 : 87838 - 87849
  • [4] Knife Detection using YOLOv5: A Deep Learning Approach
    Sinh Huynh Phuoc Truong
    Thang Dang Quoc
    Hien Nguyen Duc
    Phuc Tran Nguyen Huu
    Nguyen Nguyen Quang Vinh
    PROCEEDINGS OF THE 2024 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATION TECHNOLOGY, ICIIT 2024, 2024, : 7 - 12
  • [5] Improved YOLOv5 Smoke Detection Model
    Zheng, Yuanpan
    Xu, Boyang
    Wang, Zhenyu
    Computer Engineering and Applications, 2023, 59 (07): : 214 - 221
  • [6] A Novel YOLOv5 Deep Learning Model for Handwriting Detection and Recognition
    Moustapha, Maliki
    Tasyurek, Murat
    Ozturk, Celal
    INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS, 2023, 32 (04)
  • [7] Intelligent Small Sample Defect Detection of Concrete Surface Using Novel Deep Learning Integrating Improved YOLOv5
    Yongming Han
    Lei Wang
    Youqing Wang
    Zhiqiang Geng
    IEEE/CAA Journal of Automatica Sinica, 2024, 11 (02) : 545 - 547
  • [8] Intelligent Small Sample Defect Detection of Concrete Surface Using Novel Deep Learning Integrating Improved YOLOv5
    Han, Yongming
    Wang, Lei
    Wang, Youqing
    Geng, Zhiqiang
    IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2024, 11 (02) : 545 - 547
  • [9] Detection of Surface Defects in Lightweight Insulators Using Improved YOLOv5
    Guo Yu
    Ma Meiling
    Li Dalin
    LASER & OPTOELECTRONICS PROGRESS, 2023, 60 (24)
  • [10] Surface Defect Detection of Remanufactured Products by Using the Improved Yolov5
    Sun, Weice
    Liu, Zhengqing
    Wang, Qiucheng
    Zhu, Bingbin
    ADVANCES IN REMANUFACTURING, IWAR 2023, 2024, : 239 - 250