A Polymer-Based Recommender System for Precision Agriculture: Enhancing Crop Yield Analysis Using IoT Technology


Open Access

Year : 2025 | Volume : 13 | Special Issue 02 | Page : 86-95
    By

    Deepak Srivastava,

  • Vibhor Sharma,

  • Vinay Avasthi,

  1. Associate Professor, Department of Computer Science and Engineering, Himalayan School of Science and Technology, Swami Rama Himalayan University, Dehradun, Uttarakhand, India
  2. Assistant Professor, Department of Computer Science and Engineering, Himalayan School of Science and Technology, Swami Rama Himalayan University, Dehradun, Uttarakhand, India
  3. Professor, Department of Computer Science and Engineering, Himalayan School of Science and Technology, Swami Rama Himalayan University, Dehradun, Uttarakhand, India

Abstract

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This study introduces an innovative recommender system that employs polymer-based sensors combined with advanced data analytics to deliver personalized recommendations for crop management optimization. The use of polymer-based materials in sensor design allows for the creation of durable, cost-effective, and efficient sensors, well-suited for the challenging conditions typical in agricultural environments. These sensors are designed to withstand variations in numerous factors like temperature of the surrounding, humidity level, and at the same time other external factors, making them highly reliable for continuous monitoring. The system can analyze and interpret real-time soil conditions, moisture levels, nutrient content, and other key indicators essential for crop health. The recommender system then processes this data to provide farmers with actionable insights tailored to specific crop needs, guiding decisions on irrigation, fertilization, and pest control. By following these targeted recommendations, farmers can optimize their resource use, reduce unnecessary input costs, minimize environmental impact, and ultimately enhance crop quality and yield. In conclusion, this research underscores the potential of polymer-based sensor-integrated recommender systems to advance precision agriculture. By offering farmers data-driven guidance that is both accessible and adaptive, this technology can significantly improve resource efficiency and crop yield outcomes. This approach demonstrates an important step forward in sustainable farming practices, as it not only supports higher productivity but also fosters a more environmentally conscious approach to agriculture.

Keywords: Polymer-based sensors, polydimethylsiloxane, recommender system, artificial intelligence, IoT

[This article belongs to Special Issue under section in Journal of Polymer and Composites (jopc)]

How to cite this article:
Deepak Srivastava, Vibhor Sharma, Vinay Avasthi. A Polymer-Based Recommender System for Precision Agriculture: Enhancing Crop Yield Analysis Using IoT Technology. Journal of Polymer and Composites. 2025; 13(02):86-95.
How to cite this URL:
Deepak Srivastava, Vibhor Sharma, Vinay Avasthi. A Polymer-Based Recommender System for Precision Agriculture: Enhancing Crop Yield Analysis Using IoT Technology. Journal of Polymer and Composites. 2025; 13(02):86-95. Available from: https://journals.stmjournals.com/jopc/article=2025/view=0



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Special Issue Open Access Original Research
Volume 13
Special Issue 02
Received 08/04/2023
Accepted 15/11/2024
Published 23/01/2025
Publication Time 656 Days
158

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