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Mr. Vasileios Moysiadis
Technical Scientific at Institute for Bio-Economy and Agri-Technology (Ibo)

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Research Keywords & Expertise

0 Deep Learning
0 Robotics
0 Agriculture 4.0
0 UGV
0 machine vision and image processing

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Journal article
Published: 31 May 2021 in Sensors
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This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection and localization of the canopy of orchard trees under various conditions (i.e., different seasons, different tree ages, different levels of weed coverage). The implemented dataset was composed of images from three different walnut orchards. The achieved variability of the dataset resulted in obtaining images that fell under seven different use cases. The best-trained model achieved 91%, 90%, and 87% accuracy for training, validation, and testing, respectively. The trained model was also tested on never-before-seen orthomosaic images or orchards based on two methods (oversampling and undersampling) in order to tackle issues with out-of-the-field boundary transparent pixels from the image. Even though the training dataset did not contain orthomosaic images, it achieved performance levels that reached up to 99%, demonstrating the robustness of the proposed approach.

ACS Style

Athanasios Anagnostis; Aristotelis Tagarakis; Dimitrios Kateris; Vasileios Moysiadis; Claus Sørensen; Simon Pearson; Dionysis Bochtis. Orchard Mapping with Deep Learning Semantic Segmentation. Sensors 2021, 21, 3813 .

AMA Style

Athanasios Anagnostis, Aristotelis Tagarakis, Dimitrios Kateris, Vasileios Moysiadis, Claus Sørensen, Simon Pearson, Dionysis Bochtis. Orchard Mapping with Deep Learning Semantic Segmentation. Sensors. 2021; 21 (11):3813.

Chicago/Turabian Style

Athanasios Anagnostis; Aristotelis Tagarakis; Dimitrios Kateris; Vasileios Moysiadis; Claus Sørensen; Simon Pearson; Dionysis Bochtis. 2021. "Orchard Mapping with Deep Learning Semantic Segmentation." Sensors 21, no. 11: 3813.

Review
Published: 17 May 2020 in Applied Sciences
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The advent of mobile robots in agriculture has signaled a digital transformation with new automation technologies optimize a range of labor-intensive, resources-demanding, and time-consuming agri-field operations. To that end a generally accepted technical lexicon for mobile robots is lacking as pertinent terms are often used interchangeably. This creates confusion among research and practice stakeholders. In addition, a consistent definition of planning attributes in automated agricultural operations is still missing as relevant research is sparse. In this regard, a “narrative” review was adopted (1) to provide the basic terminology over technical aspects of mobile robots used in autonomous operations and (2) assess fundamental planning aspects of mobile robots in agricultural environments. Based on the synthesized evidence from extant studies, seven planning attributes have been included: (i) high-level control-specific attributes, which include reasoning architecture, the world model, and planning level, (ii) operation-specific attributes, which include locomotion–task connection and capacity constraints, and (iii) physical robot-specific attributes, which include vehicle configuration and vehicle kinematics.

ACS Style

Vasileios Moisiadis; Naoum Tsolakis; Dimitris Katikaridis; Claus G. Sørensen; Simon Pearson; Dionysis Bochtis. Mobile Robotics in Agricultural Operations: A Narrative Review on Planning Aspects. Applied Sciences 2020, 10, 3453 .

AMA Style

Vasileios Moisiadis, Naoum Tsolakis, Dimitris Katikaridis, Claus G. Sørensen, Simon Pearson, Dionysis Bochtis. Mobile Robotics in Agricultural Operations: A Narrative Review on Planning Aspects. Applied Sciences. 2020; 10 (10):3453.

Chicago/Turabian Style

Vasileios Moisiadis; Naoum Tsolakis; Dimitris Katikaridis; Claus G. Sørensen; Simon Pearson; Dionysis Bochtis. 2020. "Mobile Robotics in Agricultural Operations: A Narrative Review on Planning Aspects." Applied Sciences 10, no. 10: 3453.