PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
October 9, 2025Bulletin of the National Research Centre/Bulletin of the National Research Center9 citationsOpen Access

Smart farming for a sustainable future: implementing IoT-based systems in precision agriculture

View Full Paper
SKS. N. KumarSKS. KannadhasanAJAndrew Thomas Jacob

Key Points

  • Real-time monitoring under 2% error improves soil quality analysis and corrective actions.
  • The multi-sensor system efficiently identifies nutrient deficiencies in crops and enhances suitability.
  • The mobile application enables easy access for farmers, integrating actionable feedback on cultivation.
  • Combining IoT technology with precision agriculture may significantly enhance resource utilization.

Abstract

Abstract This research work comprises an IoT-based multi-sensor system that is capable of collecting a wide variety of parameters from soil as well as weather and displays these on a mobile application, which can be easily accessible to farmers for their daily cultivation needs. Real-time monitoring of various soil parameters with in-field implementation of the multi-sensor system helps in quick and accurate estimation and relaying of this collected data to the farmers for soil quality analysis, and corrections or adjustments can be made thereafter. It will be a huge benefit to the agricultural sector as it will bring about a new horizon to precision farming, as against the old agricultural practices where trained personnel and separate laboratory analysis are required. This research work presents a new IoT-based precision agriculture system with multi-sensor technology to quantify important soil parameters: pH, moisture, temperature, and NPK level. A case study on Kottayam rubber plantations proved the efficiency of the system in detecting site-specific nutrient deficiencies and enhancing crop suitability estimation. This paper fills the existing gap for integrating low-cost IoT-based monitoring with actionable feedback and mobile access. The system has future extensions in terms of integrating with weather forecasts, pest prediction modules, and machine learning-based yield prediction. A mobile app, ‘Dhristi,’ was developed for data-visualizing and presenting farmers with tailored cultivation recommendations. The system achieved high accuracy with a mean error margin of below 2% and offers a green solution for traditional soil testing, enhancing yield predictability and resource utilization.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Kumar et al. (2025) studied this question.

synapsesocial.com/papers/68e7ba40ccde5f1021f64b3chttps://doi.org/10.1186/s42269-025-01366-8
Ask AI
Helpful
Bookmark
Share
View Full Paper

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Smart Agriculture for Sustainability: The Implementation of Smart Irrigation Using Real-Time Embedded System Technology2024 · 41 citations
  2. 2Design and Field Implementation of a Low-Cost, Open-Hardware Platform for Hydrological Monitoring2021 · 16 citations
  3. 3LoRa based intelligent soil and weather condition monitoring with internet of things for precision agriculture in smart cities2022 · 126 citations
  4. 4A Low-Power IoT Device for Measuring Water Table Levels and Soil Moisture to Ease Increased Crop Yields2022 · 17 citations
  5. 5Precision Agriculture: Strategies and Technology Adoption2022 · 15 citations