Viktoria Nikoleta Tsakalidou

Papers

Total Citations

164

H-Index

About

Viktoria Nikoleta Tsakalidou is a rising researcher at the forefront of agricultural robotics and precision farming, with a focus on automating labor-intensive tasks to address global food demand and labor shortages. Her major contributions span three critical areas: end-effector design for robotic harvesting, deep learning for crop assessment, and digital twin technology for skilled agricultural tasks. Her most-cited work, "An Overview of End Effectors in Agricultural Robotic Harvesting Systems" (2022, 127 citations), provides a comprehensive synthesis of robotic grippers and manipulators, establishing a foundational reference for the field. In "A Deep Learning Approach for Precision Viticulture, Assessing Grape Maturity via YOLOv7" (2023, 34 citations), she demonstrates how advanced computer vision can optimize harvest timing and quality in vineyards. Her more recent work on digital twins (2023) explores the integration of human-like intelligence into robotic systems using Lattice Computing, pushing the boundaries of autonomous decision-making in agriculture. Tsakalidou’s research is characterized by its practical impact—bridging cutting-edge AI with real-world farming challenges—and her growing citation record reflects her influence on both roboticists and agronomists. She is a key voice in the next wave of smart agriculture, where robots not only perform tasks but adapt with precision.