IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i5p4443-d1085340.html
   My bibliography  Save this article

Benchmarking of CNN Models and MobileNet-BiLSTM Approach to Classification of Tomato Seed Cultivars

Author

Listed:
  • Kadir Sabanci

    (Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, Karaman 70100, Turkey)

Abstract

In the present study, a deep learning-based two-scenario method is proposed to distinguish tomato seed cultivars. First, images of seeds of four different tomato cultivars (Sacher F1, Green Zebra, Pineapple, and Ozarowski) were taken. Each seed was then cropped on the raw image and saved as a new image. The number of images in the dataset was increased using data augmentation techniques. In the first scenario, these seed images were classified with four different CNN (convolutional neural network) models (ResNet18, ResNet50, GoogleNet, and MobileNetv2). The highest classification accuracy of 93.44% was obtained with the MobileNetv2 model. In the second scenario, 1280 deep features obtained from MobileNetv2 fed the inputs of the Bidirectional Long Short-Term Memory (BiLSTM) network. In the classification made using the BiLSTM network, 96.09% accuracy was obtained. The results show that different tomato seed cultivars can be distinguished quickly and accurately by the proposed deep learning-based method. The performed study is a great novelty in distinguishing seed cultivars and the developed innovative approach involving deep learning in tomato seed image analysis, and can be used as a comprehensive procedure for practical tomato seed classification.

Suggested Citation

  • Kadir Sabanci, 2023. "Benchmarking of CNN Models and MobileNet-BiLSTM Approach to Classification of Tomato Seed Cultivars," Sustainability, MDPI, vol. 15(5), pages 1-14, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4443-:d:1085340
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/5/4443/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/5/4443/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Peng Xu & Qian Tan & Yunpeng Zhang & Xiantao Zha & Songmei Yang & Ranbing Yang, 2022. "Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning," Agriculture, MDPI, vol. 12(2), pages 1-16, February.
    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. Huan Lin & Xiaolei Deng & Jianping Yu & Xiaoliang Jiang & Dongsong Zhang, 2023. "A Study of Sustainable Product Design Evaluation Based on the Analytic Hierarchy Process and Deep Residual Networks," Sustainability, MDPI, vol. 15(19), pages 1-22, October.

    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. Weidong Zhu & Jun Sun & Simin Wang & Jifeng Shen & Kaifeng Yang & Xin Zhou, 2022. "Identifying Field Crop Diseases Using Transformer-Embedded Convolutional Neural Network," Agriculture, MDPI, vol. 12(8), pages 1-19, July.
    2. Yu Wang & Hongyi Bai & Laijun Sun & Yan Tang & Yonglong Huo & Rui Min, 2022. "The Rapid and Accurate Detection of Kidney Bean Seeds Based on a Compressed Yolov3 Model," Agriculture, MDPI, vol. 12(8), pages 1-21, August.
    3. Xianguo Ren & Haiqing Tian & Kai Zhao & Dapeng Li & Ziqing Xiao & Yang Yu & Fei Liu, 2022. "Research on pH Value Detection Method during Maize Silage Secondary Fermentation Based on Computer Vision," Agriculture, MDPI, vol. 12(10), pages 1-17, October.
    4. Lili Yang & Changlong Wang & Jianfeng Yu & Nan Xu & Dongwei Wang, 2023. "Method of Peanut Pod Quality Detection Based on Improved ResNet," Agriculture, MDPI, vol. 13(7), pages 1-20, July.
    5. Dimitre D. Dimitrov, 2023. "Internet and Computers for Agriculture," Agriculture, MDPI, vol. 13(1), pages 1-7, January.
    6. Guangyu Hou & Haihua Chen & Mingkun Jiang & Runxin Niu, 2023. "An Overview of the Application of Machine Vision in Recognition and Localization of Fruit and Vegetable Harvesting Robots," Agriculture, MDPI, vol. 13(9), pages 1-31, September.

    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:jsusta:v:15:y:2023:i:5:p:4443-:d:1085340. 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.