Indabot: A Simulation-Based Study of Low-Cost, ArUco Marker-Based Autonomous Navigation for Indoor Plant Phenotyping
Karamanoglu Mehmetbey University, Department of Electrical and Electronics Engineering, Karaman, Türkiye

Published: September 29, 2026
This issue of the International Journal of Applied Methods in Electronics and Computers (Vol. 14, No. 3, 2026) presents applied research on autonomous navigation, privacy-preserving healthcare AI, predictive maintenance, and industrial computer vision. The featured articles cover low-cost ArUco marker-based autonomous navigation for indoor plant phenotyping, real-time personally identifiable information (PII) redaction in healthcare large language model workflows, sampling strategies and metric sensitivity for imbalanced predictive maintenance using ensemble methods, and experimental evaluation of YOLOv10 and RT-DETR for weld defect detection.
Karamanoglu Mehmetbey University, Department of Electrical and Electronics Engineering, Karaman, Türkiye
University of Benin, Edo State, Nigeria; Chemnitz University of Technology, Germany and Fraunhofer-Institute for Electronic Nano Systems (ENAS) Chemnitz, Germany
Tekirdağ Namık Kemal University, Çorlu Faculty of Engineering, Electrical and Electronics Engineering Department, Çorlu, Tekirdağ, Türkiye
Sahin Tanker Limited Company, Asagipinarbasi Organized Industrial Zone, 521 Street 7, 42250, Konya, Türkiye