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