[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126721-en":3,"doc-seo-126721-105":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},126721,962084925782,"Ava Thompson","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Modeling of Path Loss for Radio Wave Propagation in Wireless Sensor Networks in Cassava Crops Using Machine Learning","Modeling radio signal propagation remains central to planning wireless communication systems, particularly wireless sensor networks (WSN) deployed in vegetated agricultural environments such as cassava fields. Although many propagation models exist, accurately characterizing wireless-channel attenuation is still challenging and highly relevant for deployment decisions. This work evaluates ITU-R, FITU-R, and COST-235 vegetation propagation models against path-loss measurements and applies machine learning methods (LR, K-NN, SVM, RF) to improve prediction performance.","agriculture   \nArticle  \nModeling of Path Loss for Radio Wave Propagation in Wireless Sensor Networks in Cassava Crops Using Machine Learning  \nAlexis Barrios-Ulloa 1, Alejandro Cama-Pinto 2, *, Emiro De-la-Hoz-Franco 2, Raól Ram½rez-Velarde 3 and Dora Cama-Pinto 4,5, *  \nCitation: Barrios-Ulloa, A.;  \nCama-Pinto, A.; De-la-Hoz-Franco, E.; Ramírez-Velarde, R.; Cama-Pinto, D. Modeling of Path Loss for Radio Wave Propagation in Wireless Sensor Networks in Cassava Crops Using Machine Learning. Agriculture 2023, 13, 2046. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)agriculture13112046  \nAcademic Editors: Gniewko Niedbała, Sebastian Kujawa, Magdalena Piekutowska and Tomasz Wojciechowski  \nReceived: 15 September 2023  \nRevised: 10 October 2023  \nAccepted: 18 October 2023  \nPublished: 25 October 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 Electronic Engineering, Universidad de Sucre, Sincelejo 700001, Colombia; [alexis.barrios@unisucre.edu.co](alexis.barrios@unisucre.edu.co)  \n2 Department of Computer Science and Electronics, Universidad de la Costa, Barranquilla 080002, Colombia; [edelahoz@cuc.edu.co](edelahoz@cuc.edu.co)  \n3 School of Engineering and Sciences, Instituto Tecnolâgico y de Estudios Superiores de Monterrey, Monterrey 64849, Mexico; [rramirez@tec.mx](rramirez@tec.mx)  \n4 Faculty of Industrial Engineering, Universidad Nacional Mayor de San Marcos, Lima 15081, Peru  \n5 Department of Computer Architecture and Technology, University of Granada, 18071 Granada, Spain  \n* Correspondence: [acama1@cuc.edu.co](acama1@cuc.edu.co) (A.C.-P.); [doracamapinto@correo.ugr.es](doracamapinto@correo.ugr.es) (D.C.-P.)  \nAbstract: Modeling radio signal propagation remains one of the most critical tasks in the planning of wireless communication systems, including wireless sensor networks (WSN) . Despite the existence of a considerable number of propagation models, the studies aimed at characterizing the attenuation in the wireless channel are still numerous and relevant. These studies are used in the design and planning of wireless networks deployed in various environments, including those with abundant vegetation. This paper analyzes the performance of three vegetation propagation models, ITU-R, FITU-R, and COST-235, and compares them with path loss measurements conducted in a cassava fieldin Sincelejo, Colombia. Additionally, we applied four machine learning techniques: linear regression (LR), k-nearest neighbors (K-NN), support vector machine (SVM), and random forest (RF), aiming to enhance prediction accuracy levels. The results show that vegetation models based on traditional approaches are not able to adequately characterize attenuation, while models obtained by machine learning using RF, K-NN, and SVM can predict path loss in cassava with RMSE and MAE values below 5 dB.  \nKeywords: agriculture; cassava crops; machine learning; radio wave propagation models; wireless sensor networks  \n1. Introduction  \nAgriculture is an activity of great relevance in several countries, and in many of them, it is considered a priority within their national security policies because of its importance in sustaining the population [1] . Like other industrial sectors, agriculture has undergone asignificant evolution in the last two decades, transitioning from a model based on variable monitoring to one of greater autonomy and automation in cultivation. This evolution can encompass the entire production chain, from planting to the final marketing stage [2] . In this regard, precision agriculture (PA) is one of the most significant concepts in the modern agricultural industry, owing to the adopt","cbCaikRsUkF754pi","https://ap.wps.com/l/cbCaikRsUkF754pi","pdf",3564741,1,15,"English","en",105,"# Introduction\n## Precision agriculture and WSN use\n## Need for propagation modeling\n# Radio wave propagation models and research scope\n## Vegetation-based models (ITU-R, FITU-R, COST-235)\n## Path-loss measurements in cassava fields\n# Machine learning methods for prediction\n## Linear regression, K-NN, SVM, random forest\n# Results and comparison\n## Error metrics and model performance","[{\"question\":\"Which vegetation propagation models are analyzed in the study?\",\"answer\":\"The study analyzes ITU-R, FITU-R, and COST-235 vegetation propagation models and compares them with measured path loss in a cassava field.\"},{\"question\":\"How does machine learning improve path-loss prediction in cassava crops?\",\"answer\":\"The paper applies linear regression, K-NN, support vector machine, and random forest to predict path loss, showing lower prediction errors than traditional vegetation models.\"},{\"question\":\"Why is efficient radio propagation modeling important for wireless sensor networks in agriculture?\",\"answer\":\"Accurate modeling supports proper WSN deployment by accounting for attenuation caused by both path characteristics and the vegetation environment, reducing issues like insufficient coverage or unnecessary node usage.\"}]","Modeling of Path Loss for Radio Wave Propagation in Wireless Sensor Networks in Cassava Crops Using Machine Learning | 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vegetation propagation models are analyzed in the study?","Question",{"text":75,"@type":76},"The study analyzes ITU-R, FITU-R, and COST-235 vegetation propagation models and compares them with measured path loss in a cassava field.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does machine learning improve path-loss prediction in cassava crops?",{"text":80,"@type":76},"The paper applies linear regression, K-NN, support vector machine, and random forest to predict path loss, showing lower prediction errors than traditional vegetation models.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is efficient radio propagation modeling important for wireless sensor networks in agriculture?",{"text":84,"@type":76},"Accurate modeling supports proper WSN deployment by accounting for attenuation caused by both path characteristics and the vegetation environment, reducing issues like insufficient coverage or unnecessary node 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