T. Pachidis

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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Grapevine Plant Image Dataset for Pruning – Data 2022, 7(8), 110

Abstract Grapevine pruning is conducted during winter, and it is a very important and expensive task for wine producers managing their vineyard. During grapevine pruning every year, the past year’s canes should be removed and should provide the possibility for new canes to grow and produce grapes. It is a difficult procedure, and it is not yet fully automated. However, some attempts have been made by the research community. Based on the literature, grapevine pruning automation is approximated with the…

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A review of the state-of-art, limitations and perspectives of machine vision for grape ripening estimation – EFITA International Conference 2021

E. Vrochidou, C. Bazinas, G. A. Papakostas, T. Pachidis, V. G. Kaburlasos, “A review of the state-of-art, limitations and perspectives of machine vision for grape ripening estimation”, 13th EFITA (European Federation for Information Technology in Agriculture, Food and Environment) International Conference, 25-26 May 2021. In: MDPI Engineering Proceedings 2021, 9 (1), 2; https://www.mdpi.com/2673-4591/9/1/2

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Grape stem detection using regression convolutional neural networks – Computers and Electronics in Agriculture, vol. 186, p. 106220, 2021

Τ. Kalampokas, Ε. Vrochidou, G. A. Papakostas, T. Pachidis, and V. G. Kaburlasos, “Grape stem detection using regression convolutional neural networks,” Comput. Electron. Agric., vol. 186, p. 106220, Jul. 2021, doi: 10.1016/j.compag.2021.106220.

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An Autonomous Grape-Harvester Robot: Integrated System Architecture – Electronics, vol. 10, no. 9, p. 1056

E. Vrochidou, K. Tziridis, A. Nikolaou, T. Kalampokas, G. A. Papakostas, T. P. Pachidis, S. Mamalis, S. Koundouras, V G. Kaburlasos, “An autonomous grape-harvester robot: integrated system architecture”, Electronics 2021, 10(9), 1056; https://doi.org/10.3390/electronics10091056 (Special Issue on Control of Mobile Robots – Section “Systems & Control Engineering”. Guest Editor: Vladan Papic).

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