Anomaly Detection in Autonomous Fixed-Wing UAV Agricultural Mapping Missions Using Deep Learning

Abstract

Autonomous fixed-wing UAVs are now a cornerstone of precision agriculture, especially when it comes to large-scale mapping. But even small aerodynamic disturbances or slight shifts in the center of gravity (CG) can introduce subtle instabilities in flight. These small changes may not seem dramatic, yet they can reduce mapping accuracy and potentially affect operational safety.

In this study, we present an unsupervised deep learning framework designed to detect such flight anomalies using multivariate telemetry data(specifically roll, pitch, and altitude signals). Three autoencoder architectures were tested: LSTM, GRU, and a 1D-CNN. The evaluation was based on telemetry collected from 13 real autonomous agricultural flights, where CG variations were intentionally controlled to create realistic instability conditions.

Anomalies were identified using window-level reconstruction errors, and performance was evaluated through threshold-independent ROC–AUC analysis. All three models perfectly classified severe anomalies, achieving accuracy and F1-scores of 1.00. When it came to milder CG deviations, differences became more apparent. The GRU autoencoder showed the strongest sensitivity, reaching an ROC–AUC of 1.00 and detecting over 98% of mild anomaly cases. The LSTM followed closely with near-perfect separability (AUC = 0.99), while the 1D-CNN delivered solid performance (AUC = 0.97) with the added advantage of much lower computational complexity.

Overall, the results confirm that recurrent autoencoder architectures are highly effective for real-time UAV anomaly monitoring. Among them, the GRU offers the best balance between detection performance and model efficiency, making it particularly well-suited for embedded agricultural UAV applications.