Assessing the impact of overhead agrivoltaic systems on GNSS signal performance for precision agriculture

• Agrivoltaic systems reduce GNSS quality, especially the carrier-to-noise ratio C/N₀. • Horizontal and vertical accuracy worsen under agrivoltaics vs conventional orchards. • Despite signal degradation, enough satellites support precision agriculture. • GNSS systems can still function effectively in overhead agrivoltaic environments. • RTK and SLAM techniques can enhance GNSS performance in agrivoltaic systems. Agrivoltaic systems, which integrate solar energy production with agricultural practices, present a sustainable solution to optimize land use. However, specific configurations, such as overhead solar panels, can obstruct Global Navigation Satellite System (GNSS) signals, which are vital for precision agriculture technologies. This study investigates the impact of an agrivoltaic system on GNSS signal performance compared to a conventional orchard in Kressbronn am Bodensee, Germany, using data from multiple GNSS constellations (GPS, GLONASS, Galileo, BeiDou). Key metrics such as carrier-to-noise density ratio (C/N₀), positional accuracy, time to first fix (TTFF), and dilution of precision (DOP) were analyzed. The results showed significant signal degradation under the overhead agrivoltaic system, particularly in C/N₀ and positional accuracy. While there were no significant differences in DOP metrics or TTFF, the average C/N₀ decreased from 30.62 dB-Hz in the conventional orchard to 26.92 dB-Hz in the agrivoltaic system. Moreover, the average of satellites with C/N₀ values below 24 dB-Hz was notably higher in the agrivoltaic area, indicating critical signal degradation. In addition, relying on a single constellation, like GPS, may not provide enough satellites for reliable GNSS performance. Despite the lower signal quality, the average number of satellites with C/N₀ above 24 dB-Hz remained over 22 in both areas. Combined with slight or no significant differences in other metrics compared to the conventional orchard, this suggests GNSS-based systems can support precision agriculture tasks within the agrivoltaic environment. The study highlights the importance of addressing signal interference to avoid hindering the integration of drones, robots, and other advanced technologies in agrivoltaics management, and suggests alternative solutions, such as Real-Time Kinematic (RTK) correction or Simultaneous Localization and Mapping (SLAM) techniques, to enhance performance. These findings are critical as agrivoltaic adoption grows, requiring a balance between solar energy production and the operational needs of precision agriculture.