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December 21, 2025Journal of Mobile Multimedia0 citationsOpen Access

Urban Agriculture through IoT-based Resilient Hydroponic Farming – A Machine Learning Approach

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SPSushant Kumar PattnaikSSSoumya Ranjan SamalSBShuvabrata Bandopadhaya

Key Points

  • This research aims to develop an IoT-based hydroponic farming system for urban agriculture, enhancing productivity and resource efficiency.
  • Proposed an IoT-based hydroponics system using the Nutrient Film Technique (NFT).
  • Utilized sensor networks for real-time data acquisition on plant growth and environmental conditions.
  • Implemented a Machine Learning framework for data analysis and prediction, focusing on nutrient control.
  • Achieved an accuracy of 89.6% in predicting pH values using the Support Vector Machine (SVM) algorithm.
  • Surpassed performance of Decision Tree (DT) and Random Forest Regression methods in prediction tasks.

Abstract

Procuring resilience, resource efficiency, productivity, pest and malady control in agrarian production is imperative when climate change poses a threat. In recent years, hydroponics is considered as an emerging farming technique and is popular in urban areas due to its minimal water use and ability to grow plants without soil. In the Nutrient Film Technique (NFT) based hydroponics system, plants are cultivated by using water content nutrient solutions. Integration of Internet of Things (IoT) technology to NFT based hydroponic systems, many advancements such as minimizing water usage, real-time plant growth monitoring, efficient nutrient diffusion and reduction in human efforts can be achieved. In this work, an IoT based smart hydroponics system using NFT is proposed. Key components of the proposed solution include sensor networks for data acquisition, a robust Machine Learning (ML) framework for data analysis and prediction, as well as actuators for automated control of environmental conditions. The system monitors different real-time environmental parameters and the status of the plant’s growth and controls the nutritional value of water in an automated and cost-effective way. A Support Vector Machine (SVM) algorithm is used to predict the pH values with an accuracy of 89.6%, surpassing the Decision Tree (DT) and Random Forest Regression methods.

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Cite This Study

Pattnaik et al. (2025) studied this question.

synapsesocial.com/papers/69473b64db9c958d0dfca8c2https://doi.org/10.13052/jmm1550-4646.2163
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