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}.

翻译：视觉导航在农业田间的成功实施取决于两个关键要素：1）对场景中关键组件的准确识别，以及2）通过检测区分农作物与可通行地面的边界线来识别导航路径。我们提出了Agronav——一种端到端的基于视觉的自主导航框架，该框架通过依次经过语义分割模型与语义线检测模型处理输入图像，最终输出导航中心线。同时，我们发布了Agroscapes数据集，这是一个针对六种不同农作物、从不同高度和角度采集的像素级标注数据集。这保证了基于Agroscapes训练的框架能够泛化适用于地面与空中机器人平台。代码、模型与数据集将在\href{https://github.com/shivamkumarpanda/agronav}{github.com/shivamkumarpanda/agronav}发布。