Modeling of Path Loss for Radio Wave Propagation in Wireless Sensor Networks in Cassava Crops Using Machine Learning

Alexis Barrios-Ulloa, Alejandro Cama-Pinto (), Emiro De-la-Hoz-Franco, Raúl Ramírez-Velarde and Dora Cama-Pinto ()
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Alexis Barrios-Ulloa: Department of Electronic Engineering, Universidad de Sucre, Sincelejo 700001, Colombia
Alejandro Cama-Pinto: Department of Computer Science and Electronics, Universidad de la Costa, Barranquilla 080002, Colombia
Emiro De-la-Hoz-Franco: Department of Computer Science and Electronics, Universidad de la Costa, Barranquilla 080002, Colombia
Raúl Ramírez-Velarde: School of Engineering and Sciences, Instituto Tecnológico y de Estudios Superiores de Monterrey, Monterrey 64849, Mexico
Dora Cama-Pinto: Faculty of Industrial Engineering, Universidad Nacional Mayor de San Marcos, Lima 15081, Peru

Agriculture, 2023, vol. 13, issue 11, 1-15

Abstract: 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 field in 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 .

Keywords: agriculture; cassava crops; machine learning; radio wave propagation models; wireless sensor networks (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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