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Found 6 papers, 1 papers with code
Crop Row Switching for Vision-Based Navigation: A Comprehensive Approach for Efficient Crop Field Navigation
Vision-based mobile robot navigation systems in arable fields are mostly limited to in-row navigation.
Leaving the Lines Behind: Vision-Based Crop Row Exit for Agricultural Robot Navigation
Usage of purely vision based solutions for row switching is not well explored in existing vision based crop row navigation frameworks.
Vision based Crop Row Navigation under Varying Field Conditions in Arable Fields
We also present a novel crop row detection algorithm for visual servoing in crop row fields.
Deep learning-based Crop Row Detection for Infield Navigation of Agri-Robots
The novel crop row detection algorithm was tested for crop row detection performance and the capability of visual servoing along a crop row.
Towards Infield Navigation: leveraging simulated data for crop row detection
Our method could reach the performance of a deep learning based crop row detection model trained with real-world data by using 60% less labelled real-world data.
Towards agricultural autonomy: crop row detection under varying field conditions using deep learning
This paper presents a novel metric to evaluate the robustness of deep learning based semantic segmentation approaches for crop row detection under different field conditions encountered by a field robot.
