A Deep Learning Model of Radio Wave Propagation for Precision Agriculture and Sensor System in Greenhouses
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 param...
| Main Authors: | , , , , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
| Published: |
MDPI
2023
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/14178 |
| _version_ | 1789406275585441792 |
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| author | Cama-Pinto, Dora Damas, Miguel Holgado-Terriza, Juan Antonio Arrabal Campos, Francisco Manuel Martínez Lao, Juan Antonio Cama-Pinto, Alejandro Manzano Agugliaro, Francisco Rogelio |
| author_facet | Cama-Pinto, Dora Damas, Miguel Holgado-Terriza, Juan Antonio Arrabal Campos, Francisco Manuel Martínez Lao, Juan Antonio Cama-Pinto, Alejandro Manzano Agugliaro, Francisco Rogelio |
| author_sort | Cama-Pinto, Dora |
| collection | DSpace |
| description | 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. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-14178 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2023 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-141782023-04-12T19:28:12Z A Deep Learning Model of Radio Wave Propagation for Precision Agriculture and Sensor System in Greenhouses Cama-Pinto, Dora Damas, Miguel Holgado-Terriza, Juan Antonio Arrabal Campos, Francisco Manuel Martínez Lao, Juan Antonio Cama-Pinto, Alejandro Manzano Agugliaro, Francisco Rogelio 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. 2023-01-25T18:19:49Z 2023-01-25T18:19:49Z 2023-01-13 info:eu-repo/semantics/article 2073-4395 http://hdl.handle.net/10835/14178 10.3390/agronomy13010244 en https://www.mdpi.com/2073-4395/13/1/244 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | deep learning neural network precision agriculture propagation model wireless sensor networks Cama-Pinto, Dora Damas, Miguel Holgado-Terriza, Juan Antonio Arrabal Campos, Francisco Manuel Martínez Lao, Juan Antonio Cama-Pinto, Alejandro Manzano Agugliaro, Francisco Rogelio A Deep Learning Model of Radio Wave Propagation for Precision Agriculture and Sensor System in Greenhouses |
| title | A Deep Learning Model of Radio Wave Propagation for Precision Agriculture and Sensor System in Greenhouses |
| title_full | A Deep Learning Model of Radio Wave Propagation for Precision Agriculture and Sensor System in Greenhouses |
| title_fullStr | A Deep Learning Model of Radio Wave Propagation for Precision Agriculture and Sensor System in Greenhouses |
| title_full_unstemmed | A Deep Learning Model of Radio Wave Propagation for Precision Agriculture and Sensor System in Greenhouses |
| title_short | A Deep Learning Model of Radio Wave Propagation for Precision Agriculture and Sensor System in Greenhouses |
| title_sort | deep learning model of radio wave propagation for precision agriculture and sensor system in greenhouses |
| topic | deep learning neural network precision agriculture propagation model wireless sensor networks |
| url | http://hdl.handle.net/10835/14178 |
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