## 4 Results
As the graph demonstrates, cloudy conditions reduce both the communication range and key generation speed due to increased light scattering, while lower atmospheric attenuation at night enhances these parameters. Additionally, Figure 11 presents a heatmap depicting the quantum channel error rate as a function of the detector noise levels under varying atmospheric conditions. The error rate increases with rising detector noise, especially under foggy conditions, where photon scattering is more pronounced. In contrast, clear day and night-time conditions exhibit superior performance, with error rates as low as 0.05 at 5 Hz of detector noise. Night-time operation consistently outperforms other conditions due to the reduced background noise and atmospheric interference. These findings underscore the sensitivity of the QKD system to both environmental and hardware-induced noise, thereby highlighting the importance of optimizing detector parameters for reliable performance. Figure 10: Key generation rate vs. communication range under different atmospheric conditions. The graph compares key generation rates under clear day (blue), cloudy (orange), and night (gold) conditions. The QKD system exhibits optimal performance at night due to reduced photon scattering and environmental interference. Error bars indicate variability across the simulated conditions. Increases in detector noise necessitate more intensive error correction, which may, in turn, reduce the effective data transfer rate. A statistical analysis was performed to evaluate the reliability of the simulation results. Confidence intervals and standard deviations were computed for both the key generation rate and the quantum channel error counts across all scenarios, with the results demonstrating statistical significance at a 95% confidence level. Furthermore, the incorporation of the LZW data compression algorithm reduced the volume of transmitted data, thereby increasing the transfer rate. On average, a 40% data compression ratio was achieved, resulting in a 60% improvement in the data transfer speed. However, the use of data compression also heightened the probability of errors in the quantum channel, as even minor errors in compressed data can lead to significant distortions upon decompression. Regarding computational complexity, the proposed algorithm scales linearly with data size for LZW and polynomial for BB84, while the complexity for AES-128 remains constant. Experiments using data sizes up to 1 GB did not reveal significant performance degradation; however, processing larger datasets may require further algorithmic optimization or enhanced computational resources. The simulation estimates indicated that approximately 100 CPU cycles are required per byte of data, which is significantly lower than the computational cost associated with traditional asymmetric encryption methods such as RSA. In multi-UAV networks, the QKD system maintained stable communication efficiency, with a less than 15% degradation in key generation rates when scaling up to 20 concurrently operating UAVs. The application of frequency-hopping techniques further mitigated the interference between overlapping channels, thereby confirming the proposed algorithm's suitability for UAVs with limited computing resources. In summary, both numerical simulations and theoretical analyses confirm that the proposed approach effectively secures data transmission in UAV-based GIS applications for agricultural monitoring. The hybrid encryption scheme--combining QKD with classical methods--provides high data transfer speeds, low computational overhead, and robust resistance to cyber-attacks, making it a promising solution for protecting sensitive information in low Earth orbit environments. Moreover, the integration of AI-based anomaly detection increased the computational overhead by approximately 10% while enhancing the  
Figure 11: Quantum channel error rate vs. detector noise across atmospheric conditions.