IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i9p1728-d1229786.html
   My bibliography  Save this article

Method of Attention-Based CNN for Weighing Pleurotus eryngii

Author

Listed:
  • Junmin Jia

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
    These authors contributed equally to this work.)

  • Fei Hu

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
    These authors contributed equally to this work.)

  • Xubo Zhang

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Zongyou Ben

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Yifan Wang

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Kunjie Chen

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

Abstract

Automatic weight detection is an essential step in the factory production of Pleurotus eryngii . In this study, a data set containing 1154 Pleurotus eryngii images was created, and then machine vision technology was used to extract eight two-dimensional features from the images. Because the fruiting bodies of Pleurotus eryngii have different shapes, these features were less correlated with weight. This paper proposed a multidimensional feature derivation method and an Attention-Based CNN model to solve this problem. This study aimed to realize the traditional feature screening task by deep learning algorithms and built an estimation model. Compared with different regression algorithms, the R 2 , RMSE , MAE , and MAPE of the Attention-Based CNN were 0.971, 7.77, 5.69, and 5.87%, respectively, and showed the best performance. Therefore, it can be used as an accurate, objective, and effective method for automatic weight measurements of Pleurotus eryngii .

Suggested Citation

  • Junmin Jia & Fei Hu & Xubo Zhang & Zongyou Ben & Yifan Wang & Kunjie Chen, 2023. "Method of Attention-Based CNN for Weighing Pleurotus eryngii," Agriculture, MDPI, vol. 13(9), pages 1-14, August.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:9:p:1728-:d:1229786
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/9/1728/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/9/1728/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jaesu Lee & Haseeb Nazki & Jeonghyun Baek & Youngsin Hong & Meonghun Lee, 2020. "Artificial Intelligence Approach for Tomato Detection and Mass Estimation in Precision Agriculture," Sustainability, MDPI, vol. 12(21), pages 1-15, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mingjie Wu & Xuanxi Yang & Lijun Yun & Chenggui Yang & Zaiqing Chen & Yuelong Xia, 2024. "A General Image Super-Resolution Reconstruction Technique for Walnut Object Detection Model," Agriculture, MDPI, vol. 14(8), pages 1-25, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stępień Sebastian & Smędzik-Ambroży Katarzyna & Polcyn Jan & Kwiliński Aleksy & Maican Ionut, 2023. "Are small farms sustainable and technologically smart? Evidence from Poland, Romania, and Lithuania," Central European Economic Journal, Sciendo, vol. 10(57), pages 116-132, January.
    2. Xiang Yue & Kai Qi & Xinyi Na & Yang Zhang & Yanhua Liu & Cuihong Liu, 2023. "Improved YOLOv8-Seg Network for Instance Segmentation of Healthy and Diseased Tomato Plants in the Growth Stage," Agriculture, MDPI, vol. 13(8), pages 1-15, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:13:y:2023:i:9:p:1728-:d:1229786. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.