UAV-based computer vision in precision agriculture can enhance efficiency and reduce environmental impact compared to traditional techniques. However, the computational limitations and power constraints of UAVs hinder their performance, especially for real-time deep learning tasks. Compressed neural network models offer a solution, yet fixed compression levels may result in unacceptable accuracy loss. This paper proposes a novel context-aware approach for energy-efficient, real-time on-UAV weed detection. By incorporating contextual factors such as brightness, saturation, contrast, and vegetation indices, the presented method estimates input image difficulty and dynamically selects the optimal neural network compression level. Leveraging slimmable neural networks, which enable training a single model with various widths during inference, this approach ensures the best accuracy-resource consumption trade-off. Experimental results with two different slimmable networks architectures (Slimmable U-Net și Slimmable Squeeze U-Net) showed that this approach can lead to up to 35% less computations while also increasing the quality of the inference with up to 2%. This significantly improves the practicality and performance of UAV-based weed detection systems.
