Scene and environment monitoring using aerial imagery and deep learning

Maktab Dar Oghaz, Mahdi, Razaak, Manzoor, Kerdegari, Hamideh, Argyriou, Vasileios and Remagnino, Paolo (2019) Scene and environment monitoring using aerial imagery and deep learning. In: 15th International Conference on Distributed Computing in Sensor Systems (DCOSS); 29-31 May 2019, Santorini Island, Greece.

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Abstract

Unmanned Aerial vehicles (UAV) are a promising technology for smart farming related applications. Aerial monitoring of agriculture farms with UAV enables key decision-making pertaining to crop monitoring. Advancements in deep learning techniques have further enhanced the precision and reliability of aerial imagery based analysis. The capabilities to mount various kinds of sensors (RGB, spectral cameras) on UAV allows remote crop analysis applications such as vegetation classification and segmentation, crop counting, yield monitoring and prediction, crop mapping, weed detection, disease and nutrient deficiency detection and others. A significant amount of studies are found in the literature that explores UAV for smart farming applications. In this paper, a review of studies applying deep learning on UAV imagery for smart farming is presented. Based on the application, we have classified these studies into five major groups including: vegetation identification, classification and segmentation, crop counting and yield predictions, crop mapping, weed detection and crop disease and nutrient deficiency detection. An in depth critical analysis of each study is provided.

Item Type: Conference or Workshop Item (Paper)
Event Title: 15th International Conference on Distributed Computing in Sensor Systems (DCOSS)
Additional Information: This work is co-funded by the EU-H2020 within the MON-ICA project under grant agreement number 732350. The Titan X Pascal used for this research was donated by NVIDIA. Published in: 15th International Conference on Distributed Computing in Sensor Systems (DCOSS) Institute of Electrical and Electronics Engineers, Inc. ISSN (online) 2325-2944 ISBN 9781728105703
Uncontrolled Keywords: Crop Monitoring, Image segmentation, UAV, Aerial Imagery, Deep Learning
Research Area: Computer science and informatics
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing
Faculty of Science, Engineering and Computing > School of Computer Science and Mathematics
Depositing User: Philip Keates
Date Deposited: 26 Feb 2020 11:47
Last Modified: 26 Feb 2020 11:47
DOI: https://doi.org/10.1109/DCOSS.2019.00078
URI: http://eprints.kingston.ac.uk/id/eprint/44030

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