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UAVs have been studied and manufactured to help create wireless communications networks that are more flexible and cost-effective than a typical wireless network. These UAV networks could help bridge the digital divide in rural America by providing wireless communications service to areas where cell companies find it too expensive to build conventional cell towers. To test different aspects of a UAV-based millimeter-wave frequency network, we created a MATLAB simulation. The simulation visualizes a digital twin of a farm in eastern Nebraska where UAVs are tested. The simulation allows for link budgeting and interference management calculations by accommodating changes in transmitter and receiver location, frequency of the network, power of the transmitted signal, weather conditions, and antenna specifications. The simulation is able to calculate critical network values such as signal-to-interference-plus noise ratio (SINR), path loss, atmospheric loss, and antenna gains under dynamically changing conditions. 

Millimeter-wave (mmWave) spectrum offers wide bandwidth resources that are promising to realize high-throughput wireless communications in agricultural fields. Due to the relatively small wavelength at this frequency band, mmWave signals tend to be scattered when the wireless link is established above the crop canopy. However, little is known about the scattering effect caused by crop canopy at mmWave. In this work, the scattering loss in the mmWave spectrum is quantified for different crop canopy states that are represented by the leaf area index. In particular, an approach based on a Rayleigh roughness criterion is utilized, coupled with canopy height statistics, to calculate the scattering loss. The results of the model agree well with empirical data collected from agricultural field experiments conducted in Summer 2021. The results demonstrate that as the leaf area index decreases with crop maturity, the scattering loss also decreases. This is the first work that illustrates the feasibility of using the mmWave communication links to perform sensing on the leaf area index, which is a critical metric in estimating crop conditions. 

Millimeter-wave (mmWave) spectrum offers wide bandwidth resources that are promising to realize high- throughput wireless communications in agricultural fields. Due to the relatively small wavelength at this frequency band, mmWave signals tend to be scattered when the wireless link is established above the crop canopy. However, little is known about the scattering effect caused by crop canopy at mmWave. In this work, the scattering loss in the mmWave spectrum is quantified for different crop canopy states that are represented by the leaf area index. In particular, an approach based on a Rayleigh roughness criterion is utilized, coupled with canopy height statistics, to calculate the scattering loss. The results of the model agree well with empirical data collected from agricultural field experiments conducted in Summer 2021. The results demonstrate that as the leaf area index decreases with crop maturity, the scattering loss also decreases. This is the first work that illustrates the feasibility of using the mmWave communication links to perform sensing on the leaf area index, which is a critical metric in estimating crop conditions. 

This paper explores the nexus of two emerging Internet of Things (IoT) components in precision agriculture, which requires vast amounts of agriculture fields to be monitored from air and soil for food production with efficient resource utilization. On the one hand, unmanned aerial vehicles (UAVs) have gained interest in agricultural aerial inspection due to their ubiquity and observation scale. On the other hand, agricultural IoT devices, including buried soil sensors, have gained interest in improving natural resource efficiency in crop production. In this work, the path loss and fading characteristics in wireless links between a UAV and underground (UG) nodes (Air2UG link) are studied to design a UAV altitude optimization solution. A path loss model is developed for the Air2UG link, including fading in the channel, where fading is modeled using a Rician distribution and validated using the Kolmogorov-Smirnov test. Moreover, Rician-K is found to be dependent on the UAV altitude, which is modeled with a Gaussian function with an RMSE of 0.4-1.3 dB. Furthermore, a novel altitude optimization solution is presented to minimize the bit error rate (BER). Results show that the lowest possible altitude does not always minimize the BER. Optimizing the altitude reduces the Air2UG link BER by as much as 8.6-fold. Likewise, altitude optimization can minimize the impacts of increasing burial depth on the BER. Our results and analysis are the first in this field and can be exploited to optimize the altitude and resources of a UAV node to communicate with the sensors embedded in the soil efficiently.