Empirical Model of Radio Wave Propagation in the Presence of Vegetation inside Greenhouses Using Regularized Regressions

Spain is Europe’s leading exporter of tomatoes harvested in greenhouses. The production of tomatoes should be kept and increased, supported by precision agriculture to meet food and commercial demand. The wireless sensor network (WSN) has demonstrated to be a tool to provide farmers with useful information on the state of their plantations due to its practical deployment. However, in order to measure its deployment within a crop, it is necessary to know the communication coverage of the nodes that make up the network. The multipath propagation of radio waves between the transceivers of the WSN nodes inside a greenhouse is degraded and attenuated by the intricate complex of stems, branches, leaf twigs, and fruits, all randomly oriented, that block the line of sight, consequently generating a signal power loss as the distance increases. Although the COST235 (European Cooperation in Science and Technology - COST), ITU-R (International Telecommunications Union—Radiocommunication Sector), FITU-R (Fitted ITU-R), and Weisbberger models provide an explanation of the radio wave propagation in the presence of vegetation in the 2.4 GHz ICM band, some significant discrepancies were found when they are applied to field tests with tomato greenhouses. In this paper, a novel method is proposed for determining an empirical model of radio wave attenuation for vegetation in the 2.4 GHz band, which includes the vegetation height as a parameter in addition to the distance between transceivers of WNS nodes. The empirical attenuation model was obtained applying regularized regressions with a multiparametric equation using experimental signal RSSI measurements achieved by our own RSSI measurement system for our field tests in four plantations. The evaluation parameters gave 0.948 for R(2), 0.946 for R(2) Adj considering fifth grade polynomial (20 parameters), and 0.942 for R(2), and 0.940 for R(2) Adj when a reduction of parameters was applied using the cross validation (15 parameters). These results verify the rationality and reliability of the empirical model. Finally, the model was validated considering experimental data from other plantations, reaching similar results to our proposed model.