Autonomous Navigation in Digital Agriculture: Using the Segment-Anything-Model for Corn Row Identification
Author:
Affiliation:
1. Purdue University, 615 West State Street,Department of Agricultural and Biological Engineering,West Lafayette,IN,USA,47907
2. Purdue University, 465 Northwestern Avenue,School of Electrical and Computer Engineering,West Lafayette,IN,USA,47907
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10490313/10490314/10490329.pdf?arnumber=10490329
Reference13 articles.
1. A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots
2. Ground and aerial robots for agricultural production: Opportunities and challenges;Pitla;Council for Agricultural Science and Technology (CAST),2020
3. Vision-based navigation and guidance for agricultural autonomous vehicles and robots: A review
4. Lattice: A Vision for Machine Learning, Data Engineering, and Policy Considerations for Digital Agriculture at Scale
5. Applications of machine vision in agricultural robot navigation: A review
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