DDNSAS: Deep reinforcement learning based deep Q-learning network for smart agriculture system-Reference-Cited by-同舟云学术

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DDNSAS: Deep reinforcement learning based deep Q-learning network for smart agriculture system

Published:2023-09 Issue: Volume:39 Page:100890
ISSN:2210-5379
Container-title:Sustainable Computing: Informatics and Systems
language:en
Short-container-title:Sustainable Computing: Informatics and Systems

Author:

Devarajan Ganesh Gopal,Nagarajan Senthil Murugan^ORCID,T.V. Ramana,T. Vignesh,Ghosh Uttam,Alnumay Waleed

Funder

King Saud University

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,General Computer Science

Reference32 articles.

1. Social Framework of Agriculture;Mann,2021

2. Innovation of agricultural economic management in the process of constructing smart agriculture by big data;Su;Sustain. Comput. Inf. Syst.,2021

3. Evidence supports the potential for climate-smart agriculture in Tanzania;Jones;Glob. Food Secur.,2023

4. Smart agriculture–Urgent need of the day in developing countries;Goel;Sustain. Comput. Inf. Syst.,2021

5. Architecting user-centric internet of things for smart agriculture;Sinha;Sustain. Comput. Inf. Syst.,2019

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1. Current applications and potential future directions of reinforcement learning-based Digital Twins in agriculture;Smart Agricultural Technology;2024-08

2. Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects;PeerJ Computer Science;2024-06-17

3. Enhancing Cloud IoT Security With Blockchain and SDN;2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE);2024-05-09

4. Fortifying Database Security: Integrating Advanced Honeypot Technology for Resilient SQL Injection Defense;2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE);2024-05-09

5. Emerging Technologies for Sustainable Agriculture: The Power of Humans and the Way Ahead;IEEE Access;2024

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