[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118156-en":3,"doc-seo-118156-105":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},118156,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Machine learning algorithms applied to weed management in integrated crop-livestock systems - a systematic literature review","Environmental pressure to adopt integrated crop-livestock systems has encouraged efforts to lower pesticide and herbicide use while controlling weeds and reducing production costs. This systematic literature review identifies the main machine learning algorithms applied to weed management in ICLS, screening studies through eligibility refinement. Results show most studies from the United States and Asian countries, with machine vision and deep learning dominating model usage (28% and 19%). These approaches support practical solutions such as smart sprayers for targeted herbicide application.","SPECIAL TOPICS  \nREVIEW ARTICLE  \n  Experimental methods and emerging technologies in weed science  \nformerly Planta Daninha  \nMachine learning algorithms applied to weed management in integrated crop-livestock systems: a systematic literature review  \nAna L. B. Gomesa* , Anita M. R. Fernandesa , Bruno A. C. Hortaa , Maurílio F. de Oliveirab a University of Vale do Itajaí, Itajaí, SC, Brazil. b Embrapa Maize and Sorghum, Sete Lagoas, MG, Brazil.  \nAbstract: In recent times, there has been an environmental pressure to “integrated crop-livestock system”. Although no results were found for the reduce the amount of pesticides applied to crops and, consequently, the crop three terms altogether, the combinations involving “weed management”production costs. Therefore, investments have been made in technologies + “integrated crop-livestock system” and “machine learning algorithms”that could potentially reduce the usage of herbicides on weeds. Among + “weed management” returned a significant number of studies which such technologies, Machine Learning approaches are rising in number of were subjected to a second layer of refinement by applying an eligibility applications and potential impact. Therefore, this article aims to identify criteria. The achieved results show that most of the studies were from the the main machine learning algorithms used in integrated crop-livestock United States and from nations in Asia. Machine vision and deep learning systems for weed management. Based on a systematic literature review, were the most used machine learning models, representing 28% and 19% it was possible to determine where the selected studies were performed of all cases, respectively. These systems were applied to different practical and which crop types were mostly used. The main research terms in this solutions, the most prevalent being smart sprayers, which allow for a sitestudy were: “machine learning algorithms” + “weed management” + specific herbicide application.  \nKeywords: Weed control; Weed prevention; Artificial Intelligence; Image processing  \nJournal Information:  \nISSN-2675-9462  \nWebsite: [http://awsjournal.org](http://awsjournal.org)[ ](http://awsjournal.org)Journal of the Brazilian Weed Science Society  \nHow to cite: Gomes ALB, Fernandes AMR, Horta BAC, Oliveira MF. Machine learning algorithms applied to weed management in integrated crop-livestock systems:  \na systematic literature review. Adv Weed Sci. 2024;42:e020240047  \n[https://doi.org/10.51694/AdvWeedSci/2024](https://doi.org/10.51694/AdvWeedSci/2024);42:00004  \nApproved by:  \nEditor in Chief: Carlos Eduardo Schaedler  \nAssociate Editor: Aldo Merotto Junior  \nConflict of Interest: The authors declare that there is no conflict of interest regarding the publication of this manuscript.  \nReceived: September 18, 2023  \nApproved: February 9, 2024  \n* Corresponding author:\u003C[gomes.ana@edu.univali.br](gomes.ana@edu.univali.br)>  \nThis is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided that the original author and source are credited.  \nCopyright: 2022  \n1. Introduction  \nOver the past few years, major investments have been made in the development of technologies to help reduce the use of herbicides on weed control. This reduction in the use of pesticides on agriculture is due to an environmental pressure, as well as aiming to diminish crop production costs. Such technologies make use of artificial intelligence, machine learning and analysis of great volumes of data (Big Data) . The data used in such models can be classified as phytochemical, environmental, images, among others (Jha et al., 2019). Based on these approaches, new methods for dealing with invasive species have been developed. For instance, the automation of mechanical control and the use of smart sprayers allows the development of site-specific applications of herbicides (Oliveir","cbCaieV0pBJomIpF","https://ap.wps.com/l/cbCaieV0pBJomIpF","pdf",751201,1,12,"English","en",105,"# Introduction\n## Study objective and rationale\n## Research questions and approach\n# Material and Methods\n## Systematic literature review design (PRISMA)\n## Keyword extraction and study selection process\n# Results and Discussion\n## Main algorithms identified\n## Solutions developed for ICLS weed management\n# Conclusions\n## Key findings and recommendations for future studies","[{\"question\":\"What is the main objective of the systematic literature review?\",\"answer\":\"To identify and summarize the main machine learning models used for weed management in integrated crop-livestock systems, based on a systematic literature review approach using PRISMA.\"},{\"question\":\"Which machine learning approaches were most frequently used in the reviewed studies?\",\"answer\":\"Machine vision and deep learning were reported as the most used approaches, representing 28% and 19% of the cases, respectively.\"},{\"question\":\"What practical weed-management solutions were most prevalent?\",\"answer\":\"Smart sprayers were the most prevalent solutions, enabling site-specific herbicide application.\"}]","Machine learning algorithms applied to weed management in integrated crop-livestock systems - 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