IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i11p2106-d1275107.html
   My bibliography  Save this article

Application of Vision Technology and Artificial Intelligence in Smart Farming

Author

Listed:
  • Xiuguo Zou

    (College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China
    Faculty of Applied Science, University of British Columbia, Kelowna, BC V1V 1V7, Canada)

  • Zheng Liu

    (Faculty of Applied Science, University of British Columbia, Kelowna, BC V1V 1V7, Canada)

  • Xiaochen Zhu

    (School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Wentian Zhang

    (Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia)

  • Yan Qian

    (College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China)

  • Yuhua Li

    (College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China)

Abstract

With the rapid advancement of technology, traditional farming is gradually transitioning into smart farming [...]

Suggested Citation

  • Xiuguo Zou & Zheng Liu & Xiaochen Zhu & Wentian Zhang & Yan Qian & Yuhua Li, 2023. "Application of Vision Technology and Artificial Intelligence in Smart Farming," Agriculture, MDPI, vol. 13(11), pages 1-4, November.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:11:p:2106-:d:1275107
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/11/2106/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/13/11/2106/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guanjie Jiao & Xiawei Shentu & Xiaochen Zhu & Wenbo Song & Yujia Song & Kexuan Yang, 2022. "Utility of Deep Learning Algorithms in Initial Flowering Period Prediction Models," Agriculture, MDPI, vol. 12(12), pages 1-17, December.
    2. Yongzhe Sun & Zhixin Zhang & Kai Sun & Shuai Li & Jianglin Yu & Linxiao Miao & Zhanguo Zhang & Yang Li & Hongjie Zhao & Zhenbang Hu & Dawei Xin & Qingshan Chen & Rongsheng Zhu, 2023. "Soybean-MVS: Annotated Three-Dimensional Model Dataset of Whole Growth Period Soybeans for 3D Plant Organ Segmentation," Agriculture, MDPI, vol. 13(7), pages 1-19, June.
    3. Xinyi He & Qiyang Cai & Xiuguo Zou & Hua Li & Xuebin Feng & Wenqing Yin & Yan Qian, 2023. "Multi-Modal Late Fusion Rice Seed Variety Classification Based on an Improved Voting Method," Agriculture, MDPI, vol. 13(3), pages 1-16, March.
    4. Yifang Ren & Fenghua Ling & Yong Wang, 2023. "Research on Provincial-Level Soil Moisture Prediction Based on Extreme Gradient Boosting Model," Agriculture, MDPI, vol. 13(5), pages 1-17, April.
    5. Naimin Xu & Guoxiang Sun & Yuhao Bai & Xinzhu Zhou & Jiaqi Cai & Yinfeng Huang, 2023. "Global Reconstruction Method of Maize Population at Seedling Stage Based on Kinect Sensor," Agriculture, MDPI, vol. 13(2), pages 1-15, January.
    6. Jie Ding & Cheng Zhang & Xi Cheng & Yi Yue & Guohua Fan & Yunzhi Wu & Youhua Zhang, 2023. "Method for Classifying Apple Leaf Diseases Based on Dual Attention and Multi-Scale Feature Extraction," Agriculture, MDPI, vol. 13(5), pages 1-19, April.
    7. Hongyun Hao & Peng Fang & Wei Jiang & Xianqiu Sun & Liangju Wang & Hongying Wang, 2022. "Research on Laying Hens Feeding Behavior Detection and Model Visualization Based on Convolutional Neural Network," Agriculture, MDPI, vol. 12(12), pages 1-12, December.
    8. Hong Gu Lee & Min-Jee Kim & Su-bae Kim & Sujin Lee & Hoyoung Lee & Jeong Yong Sin & Changyeun Mo, 2023. "Identifying an Image-Processing Method for Detection of Bee Mite in Honey Bee Based on Keypoint Analysis," Agriculture, MDPI, vol. 13(8), pages 1-17, July.
    9. Yong Li & Hebing Liu & Jialing Wei & Xinming Ma & Guang Zheng & Lei Xi, 2023. "Research on Winter Wheat Growth Stages Recognition Based on Mobile Edge Computing," Agriculture, MDPI, vol. 13(3), pages 1-16, February.
    10. Jinkai Guo & Xiao Xiao & Jianchi Miao & Bingquan Tian & Jing Zhao & Yubin Lan, 2023. "Design and Experiment of a Visual Detection System for Zanthoxylum-Harvesting Robot Based on Improved YOLOv5 Model," Agriculture, MDPI, vol. 13(4), pages 1-18, March.
    Full references (including those not matched with items on IDEAS)

    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.

      More about this item

      Keywords

      n/a;

      Statistics

      Access and download statistics

      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:11:p:2106-:d:1275107. 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.