Deep reinforcement learning in agricultural IoT-based: A review
The world’s food needs have an impact on innovation in the field of agriculture, and one of them is by implementing deep reinforcement learning (DRL) technology, which is very relevant to the Industrial Revolution 4.0. This research discusses important issues and developments in DRLs that are implem...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/09/e3sconf_issat2024_07004.pdf |
| _version_ | 1797343508513685504 |
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| author | Isa Indra Griha Tofik Effendi Adhan Suhartono |
| author_facet | Isa Indra Griha Tofik Effendi Adhan Suhartono |
| author_sort | Isa Indra Griha Tofik |
| collection | DOAJ |
| description | The world’s food needs have an impact on innovation in the field of agriculture, and one of them is by implementing deep reinforcement learning (DRL) technology, which is very relevant to the Industrial Revolution 4.0. This research discusses important issues and developments in DRLs that are implemented, especially in the field of IoT-based agriculture. The research method uses a Systematic Literature Review (SLR) approach through searching and analysing raw data sources, sorting and selecting relevant data relevant to the topics discussed, discussing topic areas and how trends are in current conditions, and concluding. The purpose of this study is to see how the current state of DRL implementation in agricultural IoT-based. The limitations of the study are that (1) the data sources come from Scopus-indexed journals; (2) the journal period is 2021–2023; (3) the research approach uses SLR; and (4) the focus of the discussion includes the implementation of DRL in agricultural IoT-based systems, the development of DRL technology, and the use of tools in DRL. |
| first_indexed | 2024-03-08T10:48:43Z |
| format | Article |
| id | doaj.art-c53d739516f14400b0a31e349b52a243 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-08T10:48:43Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-c53d739516f14400b0a31e349b52a2432024-01-26T16:52:43ZengEDP SciencesE3S Web of Conferences2267-12422024-01-014790700410.1051/e3sconf/202447907004e3sconf_issat2024_07004Deep reinforcement learning in agricultural IoT-based: A reviewIsa Indra Griha Tofik0Effendi Adhan1Suhartono2Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science, National Chin-Yi University of TechnologyGraduate Institute of Precision Manufacturing, National Chin-Yi University of TechnologyPh.D. Program Prospective Technology of Electrical Engineering and Computer Science, National Chin-Yi University of TechnologyThe world’s food needs have an impact on innovation in the field of agriculture, and one of them is by implementing deep reinforcement learning (DRL) technology, which is very relevant to the Industrial Revolution 4.0. This research discusses important issues and developments in DRLs that are implemented, especially in the field of IoT-based agriculture. The research method uses a Systematic Literature Review (SLR) approach through searching and analysing raw data sources, sorting and selecting relevant data relevant to the topics discussed, discussing topic areas and how trends are in current conditions, and concluding. The purpose of this study is to see how the current state of DRL implementation in agricultural IoT-based. The limitations of the study are that (1) the data sources come from Scopus-indexed journals; (2) the journal period is 2021–2023; (3) the research approach uses SLR; and (4) the focus of the discussion includes the implementation of DRL in agricultural IoT-based systems, the development of DRL technology, and the use of tools in DRL.https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/09/e3sconf_issat2024_07004.pdf |
| spellingShingle | Isa Indra Griha Tofik Effendi Adhan Suhartono Deep reinforcement learning in agricultural IoT-based: A review E3S Web of Conferences |
| title | Deep reinforcement learning in agricultural IoT-based: A review |
| title_full | Deep reinforcement learning in agricultural IoT-based: A review |
| title_fullStr | Deep reinforcement learning in agricultural IoT-based: A review |
| title_full_unstemmed | Deep reinforcement learning in agricultural IoT-based: A review |
| title_short | Deep reinforcement learning in agricultural IoT-based: A review |
| title_sort | deep reinforcement learning in agricultural iot based a review |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/09/e3sconf_issat2024_07004.pdf |
| work_keys_str_mv | AT isaindragrihatofik deepreinforcementlearninginagriculturaliotbasedareview AT effendiadhan deepreinforcementlearninginagriculturaliotbasedareview AT suhartono deepreinforcementlearninginagriculturaliotbasedareview |