Detection of Curved Rows and Gaps in Aerial Images of Sugarcane Field Using Image Processing Techniques
Author:
Affiliation:
1. Institute of Informatics, Federal University of Goiás, Goiânia, Brazil
2. Institute of Computing, University of Campinas, Campinas, Brazil
Funder
Coordination for the Improvement of Higher Education Personnel (CAPES)–Finance Code 001
National Council for Scientific and Technological Development
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Medicine
Link
http://xplorestaging.ieee.org/ielx7/9349829/9944186/09885256.pdf?arnumber=9885256
Reference41 articles.
1. Metallographic Specimen Imaging Classification: A Machine Learning Approach
2. A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing
3. Sugarcane crop line detection from UAV images using genetic algorithm and Radon transform
4. Integration of Remote Sensing and GIS to Extract Plantation Rows from A Drone-Based Image Point Cloud Digital Surface Model
5. A UAV Guidance System Using Crop Row Detection and Line Follower Algorithms
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1. Rethinking the crop row detection pipeline: An end-to-end method for crop row detection based on row-column attention;Computers and Electronics in Agriculture;2024-10
2. Mapping Gaps in Sugarcane Fields in Unmanned Aerial Vehicle Imagery Using YOLOv5 and ImageJ;Applied Sciences;2024-08-23
3. Row Detection BASED Navigation and Guidance for Agricultural Robots and Autonomous Vehicles in Row-Crop Fields: Methods and Applications;Agronomy;2023-06-30
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