IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i2p998-d1026150.html
   My bibliography  Save this article

A New Procedure for Combining UAV-Based Imagery and Machine Learning in Precision Agriculture

Author

Listed:
  • Cristiano Fragassa

    (Department of Industrial Engineering, Alma Mater Studiorum University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy)

  • Giuliano Vitali

    (Department of Agricultural and Food Sciences, Alma Mater Studiorum University of Bologna, Viale Fanin 44, 40127 Bologna, Italy)

  • Luis Emmi

    (Centre for Automation and Robotics, Arganda del Rey, 28500 Madrid, Spain)

  • Marco Arru

    (Ardesia Technologies Srl, Via Bruno Tosarelli 300, 40055 Villanova, Italy)

Abstract

Drone images from an experimental field cropped with sugar beet with a high diffusion of weeds taken from different flying altitudes were used to develop and test a machine learning method for vegetation patch identification. Georeferenced images were combined with a hue-based preprocessing analysis, digital transformation by an image embedder, and evaluation by supervised learning. Specifically, six of the most common machine learning algorithms were applied (i.e., logistic regression, k-nearest neighbors, decision tree, random forest, neural network, and support-vector machine). The proposed method was able to precisely recognize crops and weeds throughout a wide cultivation field, training from single partial images. The information has been designed to be easily integrated into autonomous weed management systems with the aim of reducing the use of water, nutrients, and herbicides for precision agriculture.

Suggested Citation

  • Cristiano Fragassa & Giuliano Vitali & Luis Emmi & Marco Arru, 2023. "A New Procedure for Combining UAV-Based Imagery and Machine Learning in Precision Agriculture," Sustainability, MDPI, vol. 15(2), pages 1-25, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:998-:d:1026150
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/2/998/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/2/998/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Suprava Chakraborty & Devaraj Elangovan & Padma Lakshmi Govindarajan & Mohamed F. ELnaggar & Mohammed M. Alrashed & Salah Kamel, 2022. "A Comprehensive Review of Path Planning for Agricultural Ground Robots," Sustainability, MDPI, vol. 14(15), pages 1-19, July.
    2. Faris A. Almalki & Ben Othman Soufiene & Saeed H. Alsamhi & Hedi Sakli, 2021. "A Low-Cost Platform for Environmental Smart Farming Monitoring System Based on IoT and UAVs," Sustainability, MDPI, vol. 13(11), pages 1-26, May.
    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.
    1. Gabriel G. R. de Castro & Guido S. Berger & Alvaro Cantieri & Marco Teixeira & José Lima & Ana I. Pereira & Milena F. Pinto, 2023. "Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs," Agriculture, MDPI, vol. 13(2), pages 1-25, January.
    2. Kosior, Katarzyna, 2023. "Projekty Badawczo-Rozwojowe Na Rzecz Rolnictwa Cyfrowego W Polsce," Roczniki (Annals), Polish Association of Agricultural Economists and Agribusiness - Stowarzyszenie Ekonomistow Rolnictwa e Agrobiznesu (SERiA), vol. 2023(1).
    3. Tyler Parsons & Fattah Hanafi Sheikhha & Omid Ahmadi Khiyavi & Jaho Seo & Wongun Kim & Sangdae Lee, 2022. "Optimal Path Generation with Obstacle Avoidance and Subfield Connection for an Autonomous Tractor," Agriculture, MDPI, vol. 13(1), pages 1-16, December.
    4. Tian Tian & Li Li & Jing Wang, 2022. "The Effect and Mechanism of Agricultural Informatization on Economic Development: Based on a Spatial Heterogeneity Perspective," Sustainability, MDPI, vol. 14(6), pages 1-18, March.
    5. Faris A. Almalki & Maha Aljohani & Merfat Algethami & Ben Othman Soufiene, 2022. "Incorporating Drone and AI to Empower Smart Journalism via Optimizing a Propagation Model," Sustainability, MDPI, vol. 14(7), pages 1-24, March.
    6. Mohammad Amiri-Zarandi & Mehdi Hazrati Fard & Samira Yousefinaghani & Mitra Kaviani & Rozita Dara, 2022. "A Platform Approach to Smart Farm Information Processing," Agriculture, MDPI, vol. 12(6), pages 1-18, June.
    7. Haoming Shi & Fei Xu & Jinfu Cheng & Victor Shi, 2023. "Exploring the Evolution of the Food Chain under Environmental Pollution with Mathematical Modeling and Numerical Simulation," Sustainability, MDPI, vol. 15(13), pages 1-17, June.
    8. Alexander V. Klokov & Egor Yu. Loktionov & Yuri V. Loktionov & Vladimir A. Panchenko & Elizaveta S. Sharaborova, 2023. "A Mini-Review of Current Activities and Future Trends in Agrivoltaics," Energies, MDPI, vol. 16(7), pages 1-18, March.

    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:2:p:998-:d:1026150. 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.