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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
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    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)

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