Empowering Farmers with IoT, UAVs, and Deep Learning in Smart Agriculture
This review article explores the transformative influence of Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), and Deep Learning (DL) in modern agriculture, outlining their applications and impact on Smart Agriculture Systems (SAS). Examining various wireless communication technologies with...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/21/e3sconf_icecs2024_04007.pdf |
| _version_ | 1827344673411170304 |
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| author | Abdul Ameer S. Alkhafaji Mohammed Ayad Jaffer Zain Al-Farouni Mohammed |
| author_facet | Abdul Ameer S. Alkhafaji Mohammed Ayad Jaffer Zain Al-Farouni Mohammed |
| author_sort | Abdul Ameer S. |
| collection | DOAJ |
| description | This review article explores the transformative influence of Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), and Deep Learning (DL) in modern agriculture, outlining their applications and impact on Smart Agriculture Systems (SAS). Examining various wireless communication technologies within IoT, including LoRa, Zigbee, and cellular networks like 5G, the study delineates their roles in enabling real-time monitoring and data transmission across expansive agricultural landscapes. Moving to UAVs, the review highlights their pivotal role in precision agriculture, elucidating how these aerial platforms equipped with diverse sensing technologies and cameras facilitate crop monitoring, disease detection, and targeted pesticide spraying. The integration of Deep Learning techniques, particularly Convolutional Neural Networks (CNNs), is discussed to emphasise their significance in disease detection, pest management, soil parameter estimation, and weed identification. The synthesis of these technologies reshapes traditional agricultural methodologies, empowering farmers with data-driven decision-making tools for optimized yield, sustainable practices, and efficient resource utilization. This comprehensive exploration aims to provide insights into the synergy of IoT, UAVs, and DL, laying the groundwork for the evolution of agricultural practices worldwide towards increased productivity and sustainability. |
| first_indexed | 2024-03-07T22:50:10Z |
| format | Article |
| id | doaj.art-215e6d83f36741f0ac967b627ddba13a |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-07T22:50:10Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-215e6d83f36741f0ac967b627ddba13a2024-02-23T10:21:00ZengEDP SciencesE3S Web of Conferences2267-12422024-01-014910400710.1051/e3sconf/202449104007e3sconf_icecs2024_04007Empowering Farmers with IoT, UAVs, and Deep Learning in Smart AgricultureAbdul Ameer S.0Alkhafaji Mohammed Ayad1Jaffer Zain2Al-Farouni Mohammed3Ahl Al Bayt UniversityNational University Of Science And Technology, Dhi QarMedical Technical College, Al-Farahidi UniversityCollege of technical engineering, The Islamic universityThis review article explores the transformative influence of Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), and Deep Learning (DL) in modern agriculture, outlining their applications and impact on Smart Agriculture Systems (SAS). Examining various wireless communication technologies within IoT, including LoRa, Zigbee, and cellular networks like 5G, the study delineates their roles in enabling real-time monitoring and data transmission across expansive agricultural landscapes. Moving to UAVs, the review highlights their pivotal role in precision agriculture, elucidating how these aerial platforms equipped with diverse sensing technologies and cameras facilitate crop monitoring, disease detection, and targeted pesticide spraying. The integration of Deep Learning techniques, particularly Convolutional Neural Networks (CNNs), is discussed to emphasise their significance in disease detection, pest management, soil parameter estimation, and weed identification. The synthesis of these technologies reshapes traditional agricultural methodologies, empowering farmers with data-driven decision-making tools for optimized yield, sustainable practices, and efficient resource utilization. This comprehensive exploration aims to provide insights into the synergy of IoT, UAVs, and DL, laying the groundwork for the evolution of agricultural practices worldwide towards increased productivity and sustainability.https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/21/e3sconf_icecs2024_04007.pdf |
| spellingShingle | Abdul Ameer S. Alkhafaji Mohammed Ayad Jaffer Zain Al-Farouni Mohammed Empowering Farmers with IoT, UAVs, and Deep Learning in Smart Agriculture E3S Web of Conferences |
| title | Empowering Farmers with IoT, UAVs, and Deep Learning in Smart Agriculture |
| title_full | Empowering Farmers with IoT, UAVs, and Deep Learning in Smart Agriculture |
| title_fullStr | Empowering Farmers with IoT, UAVs, and Deep Learning in Smart Agriculture |
| title_full_unstemmed | Empowering Farmers with IoT, UAVs, and Deep Learning in Smart Agriculture |
| title_short | Empowering Farmers with IoT, UAVs, and Deep Learning in Smart Agriculture |
| title_sort | empowering farmers with iot uavs and deep learning in smart agriculture |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/21/e3sconf_icecs2024_04007.pdf |
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