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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. 

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. 

Wireless networks in agricultural environments are unique in many ways. Recent measurements reveal that the dynamics of crop growth impact wireless propagation channels with a long-term seasonal pattern. Additionally, short-term environmental factors, such as strong wind, result in variations in channel statistics. Next-generation agricultural fields, populated by autonomous tractors, drones, and high-throughput sensing systems, require high-throughput connectivity infrastructure, resulting in the future deployment of high-frequency networks, where they have not been deployed before. More specifically, when millimeter-wave (mmWave) communication systems, a viable candidate for 5G and 6G high-throughput solutions, are deployed for higher throughput, these issues become more prominent due to the relatively small wavelength at this frequency band. To improve coverage in the mmWave spectrum in agricultural settings, reconfigurable intelligent surfaces (RISs) are a promising solution with low energy consumption and high cost efficiency when compared to half-duplex active relays with multiple antennas. To ensure link resiliency under dynamic channel behavior, an adaptive RIS for broadband wireless agricultural networks (AgRIS) at mmWave band is designed in this work. AgRIS relies on output from a time-series model that forecasts the short-term wind speed based on measured wind data, which is readily available in most farms. The temporal correlation between link reliability and wind speed is demonstrated through extensive field experiments. Our simulation results demonstrate that AgRIS with a small footprint of 11 × 11 elements can help mitigate the adversarial effects of wind-induced signal level drop by up to 8 dB and provides high energy efficiency of 1 Gbits/joule.