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Passive optical networks for next-gen culinary automation

  • Himanshu Agarwal , Amit Kishor , Ravi Agarwal , Ashima and Vikas Sharma ORCID logo EMAIL logo
Published/Copyright: September 23, 2025
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Abstract

In today’s fast-paced lifestyle, preparing meals at home has gained importance due to rising dining costs and time constraints, offering benefits of affordability, convenience, and nutrition. However, maintaining dietary variety is essential to prevent monotony, which can often be achieved through creative use of common ingredients and cooking techniques rather than relying on an extensive range of items. To address this, RecipeLens is introduced as an innovative solution that combines ingredient detection with intelligent recipe suggestions, enhanced by the integration of optical communication technologies. Leveraging high-speed and reliable data transmission, optical communication improves the responsiveness of smart kitchen environments by enabling rapid and secure exchanges between mobile devices, cloud databases, and AI modules. RecipeLens utilizes the MobileNet_v2 model for fast and accurate ingredient classification, with recipes dynamically sourced from the Food Recipe API. The inclusion of optical communication reduces latency, enhances user interaction, and ensures seamless image processing and recipe retrieval. Furthermore, optical sensors hold potential for real-time ingredient quality assessment, such as freshness detection and feedback. By blending advanced recognition, recipe recommendations, and high-speed data exchange, RecipeLens empowers users to enhance culinary creativity, explore diverse meals, and enjoy the richness of home cooking with greater efficiency, accessibility, and innovation.


Corresponding author: Vikas Sharma, Department of Electronics and Communication, Swami Vivekanand Subharti University, Meerut, 250005, Uttar Pradesh, India, E-mail:

Acknowledgments

Thanks to all my coauthor for the support.

  1. Research ethics: Not applicable.

  2. Informed consent: We all are fully responsible for this paper.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2025-08-11
Accepted: 2025-08-13
Published Online: 2025-09-23

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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