A Deep Learning Model of Radio Wave Propagation for Precision Agriculture and Sensor System in Greenhouses Cama Pinto, Dora Damas Hermoso, Miguel Holgado Terriza, Juan Antonio Deep learning Neural network Precision agriculture Propagation model Wireless sensor networks The production of crops in greenhouses will ensure the demand for food for the world’s population in the coming decades. Precision agriculture is an important tool for this purpose, supported among other things, by the technology of wireless sensor networks (WSN) in the monitoring of agronomic parameters. Therefore, prior planning of the deployment of WSN nodes is relevant because their coverage decreases when the radio waves are attenuated by the foliage of the plantation. In that sense, the method proposed in this study applies Deep Learning to develop an empirical model of radio wave attenuation when it crosses vegetation that includes height and distance between the transceivers of the WSN nodes. The model quality is expressed via the parameters cross-validation, R2 of 0.966, while its generalized error is 0.920 verifying the reliability of the empirical model. 2023-03-03T08:28:22Z 2023-03-03T08:28:22Z 2023-01-13 journal article Cama-Pinto, D... [et al.]. A Deep Learning Model of RadioWave Propagation for Precision Agriculture and Sensor System in Greenhouses. Agronomy 2023, 13, 244. [https://doi.org/10.3390/agronomy13010244] https://hdl.handle.net/10481/80364 10.3390/agronomy13010244 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional MDPI