Remote Sensing Revolution: Mapping Land Productivity and Vegetation Trends with Unmanned Aerial Vehicles (UAVs)
Abstract: This review paper offers a comprehensive exploration of the multifaceted applications of Unmanned Aerial Vehicles (UAVs) in various domains, showcasing their transformative impact in addressing complex challenges. The evaluation of cloud-based UAV systems' stability reveals their robu...
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Online Access: | http://dx.doi.org/10.2174/0126667312288014240129080801 https://www.eurekaselect.com/article/download/226752 https://www.eurekaselect.com/226752/article |
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crbenthamsciepub:10.2174/0126667312288014240129080801 2024-09-09T19:27:18+00:00 Remote Sensing Revolution: Mapping Land Productivity and Vegetation Trends with Unmanned Aerial Vehicles (UAVs) Harle, Shrikant Bhagat, Amol Dash, Ashish Kumar 2024 http://dx.doi.org/10.2174/0126667312288014240129080801 https://www.eurekaselect.com/article/download/226752 https://www.eurekaselect.com/226752/article en eng Bentham Science Publishers Ltd. Current Applied Materials volume 03 ISSN 2666-7312 journal-article 2024 crbenthamsciepub https://doi.org/10.2174/0126667312288014240129080801 2024-06-20T04:11:33Z Abstract: This review paper offers a comprehensive exploration of the multifaceted applications of Unmanned Aerial Vehicles (UAVs) in various domains, showcasing their transformative impact in addressing complex challenges. The evaluation of cloud-based UAV systems' stability reveals their robustness and reliability, underlining their significance in numerous industries. Additionally, their role in enhancing robot navigation in intricate environments signifies a substantial advancement in robotics and automation. The integration of blockchain technology for secure Internet of Things (IoT) data transfer emphasizes the critical importance of data integrity and confidentiality in the IoT era. Furthermore, the optimization of energy-efficient data collection in IoT networks through UAVs demonstrates their potential to revolutionize data-driven decision-making processes, particularly in fields reliant on data accuracy and timeliness. The paper also highlights the application of deep reinforcement learning to enhance UAV-assisted IoT data collection, showcasing the synergy between advanced machine learning techniques and UAV technology. Finally, the discussion underscores the pivotal role of UAVs in precision agriculture, where they facilitate ecological farming practices and monitor environmental conditions, contributing to the pursuit of sustainable and efficient agriculture. This review reaffirms UAVs' status as transformative tools, reshaping industries and unlocking new frontiers of innovation and problem-solving. With ongoing technological advancements, UAVs are poised to play an increasingly central role in a wide range of applications, promising a future marked by ground breaking possibilities. Key findings include the dominance of the United States and China in the field, exploration of characteristics such as crop production, and innovative UAV-based methods for grassland mapping, maize growth assessment, and Arctic plant species monitoring. The research underscores the potential of UAVs in bridging field ... Article in Journal/Newspaper Arctic Bentham Science Publishers Arctic Current Applied Materials 03 |
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Bentham Science Publishers |
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Abstract: This review paper offers a comprehensive exploration of the multifaceted applications of Unmanned Aerial Vehicles (UAVs) in various domains, showcasing their transformative impact in addressing complex challenges. The evaluation of cloud-based UAV systems' stability reveals their robustness and reliability, underlining their significance in numerous industries. Additionally, their role in enhancing robot navigation in intricate environments signifies a substantial advancement in robotics and automation. The integration of blockchain technology for secure Internet of Things (IoT) data transfer emphasizes the critical importance of data integrity and confidentiality in the IoT era. Furthermore, the optimization of energy-efficient data collection in IoT networks through UAVs demonstrates their potential to revolutionize data-driven decision-making processes, particularly in fields reliant on data accuracy and timeliness. The paper also highlights the application of deep reinforcement learning to enhance UAV-assisted IoT data collection, showcasing the synergy between advanced machine learning techniques and UAV technology. Finally, the discussion underscores the pivotal role of UAVs in precision agriculture, where they facilitate ecological farming practices and monitor environmental conditions, contributing to the pursuit of sustainable and efficient agriculture. This review reaffirms UAVs' status as transformative tools, reshaping industries and unlocking new frontiers of innovation and problem-solving. With ongoing technological advancements, UAVs are poised to play an increasingly central role in a wide range of applications, promising a future marked by ground breaking possibilities. Key findings include the dominance of the United States and China in the field, exploration of characteristics such as crop production, and innovative UAV-based methods for grassland mapping, maize growth assessment, and Arctic plant species monitoring. The research underscores the potential of UAVs in bridging field ... |
format |
Article in Journal/Newspaper |
author |
Harle, Shrikant Bhagat, Amol Dash, Ashish Kumar |
spellingShingle |
Harle, Shrikant Bhagat, Amol Dash, Ashish Kumar Remote Sensing Revolution: Mapping Land Productivity and Vegetation Trends with Unmanned Aerial Vehicles (UAVs) |
author_facet |
Harle, Shrikant Bhagat, Amol Dash, Ashish Kumar |
author_sort |
Harle, Shrikant |
title |
Remote Sensing Revolution: Mapping Land Productivity and Vegetation Trends with Unmanned Aerial Vehicles (UAVs) |
title_short |
Remote Sensing Revolution: Mapping Land Productivity and Vegetation Trends with Unmanned Aerial Vehicles (UAVs) |
title_full |
Remote Sensing Revolution: Mapping Land Productivity and Vegetation Trends with Unmanned Aerial Vehicles (UAVs) |
title_fullStr |
Remote Sensing Revolution: Mapping Land Productivity and Vegetation Trends with Unmanned Aerial Vehicles (UAVs) |
title_full_unstemmed |
Remote Sensing Revolution: Mapping Land Productivity and Vegetation Trends with Unmanned Aerial Vehicles (UAVs) |
title_sort |
remote sensing revolution: mapping land productivity and vegetation trends with unmanned aerial vehicles (uavs) |
publisher |
Bentham Science Publishers Ltd. |
publishDate |
2024 |
url |
http://dx.doi.org/10.2174/0126667312288014240129080801 https://www.eurekaselect.com/article/download/226752 https://www.eurekaselect.com/226752/article |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Current Applied Materials volume 03 ISSN 2666-7312 |
op_doi |
https://doi.org/10.2174/0126667312288014240129080801 |
container_title |
Current Applied Materials |
container_volume |
03 |
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