V.N. Tsakalidou

A Deep Learning Approach for Precision Viticulture, Assessing Grape Maturity via YOLOv7 – Sensors, vol. 23, no. 19, 8126, 2023

E. Badeka, E. Karapatzak, A. Karampatea, E. Bouloumpasi, I. Kalathas, C. Lytridis, E. Tziolas, V. N. Tsakalidou, V. G. Kaburlasos, “A Deep Learning Approach for Precision Viticulture, Assessing Grape Maturity via YOLOv7”, Sensors, vol. 23, no. 19, 8126, 2023. https://doi.org/10.3390/s23198126 (Special Issue “Intelligent Sensing and Machine Vision in Precision Agriculture”. Guest Editors: Yuwei Wang, Liqing Chen, Peng Chen, Bolin Cai) https://www.mdpi.com/journal/sensors/special_issues/Z1SF644ZAW

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Skilled agricultural task delivery by a digital twin – IEEE CAI 2023, Santa Clara (Silicon Valley), California, USA

V. G. Kaburlasos, C. Lytridis, G. Siavalas, V. N. Tsakalidou, C. Tsakmakis, I. Kalathas, T. Pachidis, K. Rantos, K. Kalaboukas, “Skilled Agricultural Task Delivery by a Digital Twin,” 2023 IEEE Conference on Artificial Intelligence (CAI), Santa Clara, CA, USA, 2023, pp. 364-365, doi: 10.1109/CAI54212.2023.00159.

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An Overview of End Effectors in Agricultural Robotic Harvesting Systems – Agriculture 2022, 12(8), 1240

Abstract In recent years, the agricultural sector has turned to robotic automation to deal with the growing demand for food. Harvesting fruits and vegetables is the most labor-intensive and time-consuming among the main agricultural tasks. However, seasonal labor shortage of experienced workers results in low efficiency of harvesting, food losses, and quality deterioration. Therefore, research efforts focus on the automation of manual harvesting operations. Robotic manipulation of delicate products in unstructured environments is challenging. The development of suitable end effectors…

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