Details

Autor(en) / Beteiligte

• de Silva, Rajitha
• Cielniak, Grzegorz
• Gao, Junfeng

Titel

Vision based crop row navigation under varying field conditions in arable fields

Ist Teil von

• Computers and electronics in agriculture, 2024-02, Vol.217, p.108581, Article 108581

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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. The proposed method uses deep learning based crop row skeleton segmentation method followed by a crop row scanning algorithm that identifies the central crop row which the robot then follows. The unique dataset we used with skeleton representations for crop row detection enables robust crop row detection in challenging real world field conditions. 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. Dense weed presence within inter-row space and discontinuities in crop rows were the most challenging field conditions for our crop row detection algorithm. An End-of Row detector algorithm was developed to detect the end of the crop row and navigate the robot towards the headland area when it reaches the end of the crop row. •A novel multi-crop dataset for crop row detection with Sugar Beet and Maize crops.•The field variations in a dataset dictates the crop-agnostic nature of predictions.•The triangle scan method leads to stable navigation, unaffected by crop row mask IoU.•The controller guided the robot precisely despite initial position errors up to 20°.•The EOR detector senses the robot’s arrival at a crop row’s end, guiding its exit.