Modeling of Path Loss for RadioWave Propagation in Wireless Sensor Networks in Cassava Crops Using Machine Learning Barrios Ulloa, Alexis Cama Pinto, Alejandro De-la-Hoz-Franco, Emiro Ramírez-Velarde, Raúl Cama Pinto, Dora Agriculture Cassava crops Machine learning 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. 2024-04-03T09:10:26Z 2024-04-03T09:10:26Z 2023-10-25 info:eu-repo/semantics/article Barrios-Ulloa, A.; Cama-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/agriculture13112046 https://hdl.handle.net/10481/90353 10.3390/agriculture13112046 eng http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional MDPI