CROP ROW DETECTION PROCEDURE USING LOW-COST UAV IMAGERY SYSTEM

13 citations as recorded by crossref.

1. Deep4Fusion: A Deep FORage Fusion framework for high-throughput phenotyping for green and dry matter yield traits L. Rodrigues et al.
2. Integration of Remote Sensing and GIS to Extract Plantation Rows from A Drone-Based Image Point Cloud Digital Surface Model N. Fareed & K. Rehman
3. A Semi-Automatic Workflow to Extract Irregularly Aligned Plots and Sub-Plots: A Case Study on Lentil Breeding Populations T. Ha et al.
4. Detection of Curved Rows and Gaps in Aerial Images of Sugarcane Field Using Image Processing Techniques B. Moraes Rocha et al.
5. Mapping for Autonomous Navigation of Agricultural Robots Through Crop Rows Using UAV H. Mansur et al.
6. UAV-g 2019: Unmanned Aerial Vehicles in Geomatics F. Nex
7. Robotic crop row tracking around weeds using cereal-specific features X. Li et al.
8. Vision-based navigation and guidance for agricultural autonomous vehicles and robots: A review Y. Bai et al.
9. Row Detection BASED Navigation and Guidance for Agricultural Robots and Autonomous Vehicles in Row-Crop Fields: Methods and Applications J. Shi et al.
10. Recognition method of maize crop rows at the seedling stage based on MS-ERFNet model X. Liu et al.
11. Visual to near-infrared image translation for precision agriculture operations using GANs and aerial images M. Krestenitis et al.
12. A learning-based approach for forest row orientation estimation from UAV LiDAR point clouds M. Liu et al.
13. Deep Learning Applied to Phenotyping of Biomass in Forages with UAV-Based RGB Imagery W. Castro et al.