[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120829-en":3,"doc-seo-120829-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120829,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","A New Procedure for Combining UAV-Based Imagery and Machine Learning in Precision Agriculture","Drone imagery from an experimental sugar beet field with high weed diffusion was used to develop and evaluate a machine learning pipeline for vegetation patch identification. Georeferenced images were combined with hue-based preprocessing, embedded via an image encoder, and assessed through supervised learning. Six common algorithms—including logistic regression, k-nearest neighbors, decision trees, random forests, neural networks, and support-vector machines—were tested. The proposed method accurately recognized crops and weeds across a wide cultivation area using training from single partial images, and supports integration into autonomous weed-management systems to reduce water, nutrients, and herbicide use in precision agriculture.","sustainability   \nArticle  \nA New Procedure for Combining UAV-Based Imagery and Machine Learning in Precision Agriculture  \nCristiano Fragassa 1, *, Giuliano Vitali 2, Luis Emmi 3 and Marco Arru 4  \nCitation: Fragassa, C.; Vitali, G.; Emmi, L.; Arru, M. A New Procedure for Combining UAV-Based Imagery and Machine Learning in Precision Agriculture. Sustainability 2023, 15, 998. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)su15020998  \nAcademic Editors: Daniele Sarri, Marco Vieri, Gabriel Araójo e Silva Ferraz and Marco Sozzi  \nReceived: 27 October 2022  \nRevised: 30 December 2022  \nAccepted: 30 December 2022  \nPublished: 5 January 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Industrial Engineering, Alma Mater Studiorum University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy  \n2 Department of Agricultural and Food Sciences, Alma Mater Studiorum University of Bologna, Viale Fanin 44, 40127 Bologna, Italy  \n3 Centre for Automation and Robotics, Arganda del Rey, 28500 Madrid, Spain  \n4 Ardesia Technologies Srl, Via Bruno Tosarelli 300, 40055 Villanova, Italy  \n* Correspondence: [cristiano.fragassa@unibo.it](cristiano.fragassa@unibo.it)  \nAbstract: 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.  \nKeywords: precision agriculture; agricultural robotics; environmental sustainability; unmanned aerial vehicle (UAV); image analysis; machine learning; sugar beet; weeding  \n1. Introduction  \nPrecision agriculture (PA) is an agricultural management strategy based on data that are collected, processed, analyzed, and combined with other information to drive decisions based on spatial and temporal variability to improve efficiency in the use of resources and the productivity, quality, and profitability of agricultural production [1,2] .  \nAlthough the benefits offered by PA to humankind and the environment are evident [3,4], efforts aimed at transforming its general concepts into actions are in progress.  \nThe present article refers to the use of automated and intelligent techniques to recognize vegetable varieties, differentiate cultivation and weeds, and understand how to treat different portions of a cultivated field.  \nThe identification relies on images of vegetation, usually taken from above, that are subsequently processed using specific image analysis and modeling strategies [5] . This analysis of vegetal covers has been pursued for years for classification purposes, surveys, mapping, biodiversity analysis, and cover dynamics, tasks typically based on remote sensing (satellites, aircrafts) [6] . However, there are limitations when attempting to perform mapping/classification exercises.  \nThe vegetation canopy is a dynamically structured biological system made of species that strongly interact with one anothe","cbCaibSeM0O8M3Zn","https://ap.wps.com/l/cbCaibSeM0O8M3Zn","pdf",2384860,1,25,"English","en",105,"# Introduction\n## Precision agriculture and spatial decision-making\n## Image-based vegetation and weed recognition\n## Remote sensing foundations and current limitations\n## Motivation for UAV imagery and sustainable weed management\n## Color models and RGB limitations\n# Method overview (from abstract and framing)\n## Georeferenced images with hue-based preprocessing\n## Image embedding and supervised learning evaluation","[{\"question\":\"What problem does the proposed procedure address in precision agriculture?\",\"answer\":\"It targets vegetation patch identification by recognizing crops and weeds from UAV images to support sustainable, autonomous weed management.\"},{\"question\":\"How are the UAV images processed before machine learning?\",\"answer\":\"Georeferenced images undergo hue-based preprocessing, are transformed through an image embedder, and then evaluated using supervised learning.\"},{\"question\":\"Which machine learning algorithms are used and what is the reported outcome?\",\"answer\":\"Six algorithms are tested, and the method can precisely recognize crops and weeds across a wide field using training from single partial images.\"}]","A New Procedure for Combining UAV-Based Imagery and Machine Learning in Precision Agriculture | 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problem does the proposed procedure address in precision agriculture?","Question",{"text":75,"@type":76},"It targets vegetation patch identification by recognizing crops and weeds from UAV images to support sustainable, autonomous weed management.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are the UAV images processed before machine learning?",{"text":80,"@type":76},"Georeferenced images undergo hue-based preprocessing, are transformed through an image embedder, and then evaluated using supervised learning.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning algorithms are used and what is the reported outcome?",{"text":84,"@type":76},"Six algorithms are tested, and the method can precisely recognize crops and weeds across a wide field using training from single partial 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