Scene and environment monitoring using aerial imagery and deep learning

Mahdi Maktab Dar Oghaz; Manzoor Razaak; Hamideh Kerdegari; Vasileios Argyriou; Paolo Remagnino

doi:10.1109/DCOSS.2019.00078

Scene and environment monitoring using aerial imagery and deep learning

Mahdi Maktab Dar Oghaz, Manzoor Razaak, Hamideh Kerdegari, Vasileios Argyriou, Paolo Remagnino

Research output: Contribution to conference › Paper › peer-review

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.

Original language	English
DOIs	https://doi.org/10.1109/DCOSS.2019.00078
Publication status	Published - May 2019
Event	15th International Conference on Distributed Computing in Sensor Systems (DCOSS) - Santorini Island, Greece Duration: 29 May 2019 → 31 May 2019

Conference

Conference	15th International Conference on Distributed Computing in Sensor Systems (DCOSS)
Period	29/05/19 → 31/05/19

Bibliographical note

Note: 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

Keywords

Crop Monitoring
Image segmentation
UAV
Aerial Imagery
Deep Learning
Computer science and informatics

Access to Document

10.1109/DCOSS.2019.00078

https://dx.doi.org/10.1109/DCOSS.2019.00078

1 Conference contribution

Scene and environment monitoring using aerial imagery and deep learning
Maktab Dar Oghaz, M., Razaak, M., Kerdegari, H., Argyriou, V. & Remagnino, P., May 2019, 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.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Cite this

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title = "Scene and environment monitoring using aerial imagery and deep learning",

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.",

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author = "\{Maktab Dar Oghaz\}, Mahdi and Manzoor Razaak and Hamideh Kerdegari and Vasileios Argyriou and Paolo Remagnino",

note = "Note: 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; 15th International Conference on Distributed Computing in Sensor Systems (DCOSS) ; Conference date: 29-05-2019 Through 31-05-2019",

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AU - Argyriou, Vasileios

AU - Remagnino, Paolo

N1 - Note: 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

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AB - 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.

KW - Crop Monitoring

KW - Image segmentation

KW - UAV

KW - Aerial Imagery

KW - Deep Learning

KW - Computer science and informatics

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DO - 10.1109/DCOSS.2019.00078

M3 - Paper

T2 - 15th International Conference on Distributed Computing in Sensor Systems (DCOSS)

Y2 - 29 May 2019 through 31 May 2019

ER -

Scene and environment monitoring using aerial imagery and deep learning

Abstract

Conference

Bibliographical note

Keywords

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Research output

Scene and environment monitoring using aerial imagery and deep learning

Cite this