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

Cristiano Fragassa (), Giuliano Vitali, Luis Emmi and Marco Arru
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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

Sustainability, 2023, vol. 15, issue 2, 1-25

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.

Keywords: precision agriculture; agricultural robotics; environmental sustainability; unmanned aerial vehicle (UAV); image analysis; machine learning; sugar beet; weeding (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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