T. Pachidis

Fuzzy lattice reasoning (FLR) for decision-making on an ontology of constraints toward agricultural robot harvest – FLINS 2022 on Machine learning, Multi agent and Cyber physical systems, Tianjin, China

Abstract A sustainable production of high-quality agricultural products calls for personalized- rather than for massive- operations. The aforementioned (personalized) operations can be pursued by human-like reasoning applicable per case. The interest here is in agricultural grape robot harvest where a binary decision needs to be taken, given a set of ambiguous constraints represented by a Boolean lattice ontology of inequalities. Fuzzy lattice reasoning (FLR) is employed for decision making. Preliminary experimental results on expert data demonstrate the advantages of the…

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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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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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