Optimising Energy Efficiency in Agricultural Consumer Electronics Using Principal Component Analysis and Deep Q‐Learning Article Swipe
Subir Gupta
,
Upasana Adhikari
,
Pinky Pramanik
,
S. Chowdhury
,
J Shreyas
,
Anurag Sinha
,
Saifullah Khalid
,
S. Y. Malathi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1049/cps2.70029
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1049/cps2.70029
The ability to reduce emissions and improve sustainability in agricultural consumer electronics has been significantly hindered due to the use of energy‐intensive technology within the agricultural sector. This study proposes a new enhancement of deep Q‐learning (DQN) with principal component analysis (PCA) focused on energy efficiency. PCA helps manage massive operational data by performing dimensionality reduction, whereas DQN, a reinforcement learning paradigm, optimises decision‐making during real‐world interactions. The main contribution of this study is in the combined use of PCA and DQN to form customisable, precise, contest‐responsive energy frameworks powered by real‐time analytics on agricultural data—energy management on such a scale has not been approached in the context of sustainable agriculture before. The experiments confirm the optimal model, further achieving a cumulative reward of 72.56, an average emission of 1.83, a Q ‐value of 24.76 and a total zenith value of 75.40% in ensuring numerous noncriteria‐defined efficient energy‐dependent operations. This paradigm not only fills the void in the automation of passive intelligent agricultural systems but also serves as a point of reference for other eco‐critical domains to strive towards greener technology.
Related Topics
Concepts
Principal component analysis
Electronics
Component (thermodynamics)
Agriculture
Computer science
Agricultural economics
Environmental economics
Agricultural science
Engineering
Environmental science
Artificial intelligence
Electrical engineering
Economics
Geography
Physics
Archaeology
Thermodynamics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1049/cps2.70029
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/cps2.70029
- OA Status
- gold
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4414020419
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4414020419Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1049/cps2.70029Digital Object Identifier
- Title
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Optimising Energy Efficiency in Agricultural Consumer Electronics Using Principal Component Analysis and Deep Q‐LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
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Subir Gupta, Upasana Adhikari, Pinky Pramanik, S. Chowdhury, J Shreyas, Anurag Sinha, Saifullah Khalid, S. Y. MalathiList of authors in order
- Landing page
-
https://doi.org/10.1049/cps2.70029Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/cps2.70029Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/cps2.70029Direct OA link when available
- Concepts
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Principal component analysis, Electronics, Component (thermodynamics), Agriculture, Computer science, Agricultural economics, Environmental economics, Agricultural science, Engineering, Environmental science, Artificial intelligence, Electrical engineering, Economics, Geography, Physics, Archaeology, ThermodynamicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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36Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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