Vision based Crop Row Navigation under Varying Field Conditions in Arable Fields

Clicks: 152
ID: 282048
2022
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Abstract
Accurate crop row detection is often challenged by the varying field conditions present in real-world arable fields. Traditional colour based segmentation is unable to cater for all such variations. The lack of comprehensive datasets in agricultural environments limits the researchers from developing robust segmentation models to detect crop rows. We present a dataset for crop row detection with 11 field variations from Sugar Beet and Maize crops. We also present a novel crop row detection algorithm for visual servoing in crop row fields. Our algorithm can detect crop rows against varying field conditions such as curved crop rows, weed presence, discontinuities, growth stages, tramlines, shadows and light levels. Our method only uses RGB images from a front-mounted camera on a Husky robot to predict crop rows. Our method outperformed the classic colour based crop row detection baseline. Dense weed presence within inter-row space and discontinuities in crop rows were the most challenging field conditions for our crop row detection algorithm. Our method can detect the end of the crop row and navigate the robot towards the headland area when it reaches the end of the crop row.
Reference Key
gao2022vision Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Rajitha de Silva; Grzegorz Cielniak; Junfeng Gao
Journal arXiv
Year 2022
DOI
DOI not found
URL
http://arxiv.org/abs/2209.14003v1
Keywords
cs.cv cs.ro

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