A Novel Instance Segmentation‐Based Navigation Line Extraction for Paddy‐Field Weeding Robot

ABSTRACT Rice cultivation is a cornerstone of global food security, yet effective weed management remains a critical challenge. Autonomous weeding robots offer a sustainable alternative, but their efficacy hinges on precise navigation, particularly in the complex visual environments of paddy fields. This study introduces a novel instance segmentation‐based navigation line extraction method tailored for paddy‐field robots. Unlike conventional machine vision techniques, which struggle with noise, water reflections, and irregular crop rows, our approach leverages a lightweight instance segmentation model to accurately identify and differentiate rice seedling rows. The model, optimized for deployment on resource‐constrained edge devices like the Jetson Nano, achieves high precision with minimal angular and pixel deviations (0.54° and 6.40 pixels, respectively). It also demonstrates real‐time inference speeds of 16.13 FPS, making it suitable for practical applications. Furthermore, our method eliminates the need for high‐precision data annotation, reducing labeling complexity and enhancing generalizability. Comparative experiments with existing methods, such as YOLOv8‐seg, ESNet, VGG‐UNet, Mobile‐UNet, and Mobile‐DeeplabV3+, demonstrate that our method achieves superior performance in both accuracy and computational efficiency. The proposed navigation line extraction method not only meets the demands of agricultural robots in paddy fields but also lays a foundation for future advancements in automated field navigation, potentially integrating GNSS/INS systems for fully autonomous operations. This research represents a significant step toward sustainable and efficient agricultural practices, addressing both environmental and economic challenges in rice cultivation.