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Weed invasion poses a significant threat to global rice production, causing substantial yield losses and environmental degradation from excessive herbicide use. Unmanned Aerial Vehicles (UAVs), combined with advanced remote sensing and deep learning techniques, offer a transformative approach for precise weed and rice classification, supporting site-specific weed management. This review not only synthesizes recent advancements in deep learning methods using UAV-acquired data, diverse vegetation indices, and multiple sensor modalities (RGB, multispectral, hyperspectral, thermal, and LiDAR) but also provides a critical perspective on the evolution of model architectures, highlighting key trends and challenges in real-world agricultural applications. We discuss persistent issues, including data scarcity, limited model generalizability across varying environmental conditions, and the computational demands for real-time deployment. Furthermore, we propose future research directions informed by our perspective on the field’s development, emphasizing synthetic data generation via generative adversarial networks, advanced attention mechanisms, and the integration of UAVs with ground-based robotic platforms to enable more autonomous, efficient, and sustainable agricultural practices. This review thus offers both a comprehensive synthesis and a forward-looking viewpoint on advancing UAV-based precision weed management in rice cultivation. By integrating these insights, we provide a roadmap for translating UAV-based weed detection from experimental research to scalable, field-ready solutions.
Ahmad et al. (Mon,) studied this question.