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The successful implementation of vision-based navigation in agriculturalfields hinges upon two critical components: 1) the accurate identification ofkey components within the scene, and 2) the identification of lanes through thedetection of boundary lines that separate the crops from the traversableground. We propose Agronav, an end-to-end vision-based autonomous navigationframework, which outputs the centerline from the input image by sequentiallyprocessing it through semantic segmentation and semantic line detection models.We also present Agroscapes, a pixel-level annotated dataset collected acrosssix different crops, captured from varying heights and angles. This ensuresthat the framework trained on Agroscapes is generalizable across both groundand aerial robotic platforms. Codes, models and dataset will be released at\href{https://github.com/shivamkumarpanda/agronav}{github.com/shivamkumarpanda/agronav}.

Agronav: Autonomous Navigation Framework for Agricultural Robots and Vehicles using Semantic Segmentation and Semantic Line Detection