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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: June 2026
Volume 11 | Issue 6
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Impact Factor : 8.14
Issue per Year : 12
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Issue Published : 121
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| Paper Title: | SoilGuard: An IoT and LSTM-Based Predictive Irrigation System for Smart Farming |
| Authors Name: | Srikanth S P , Latha B T , Gowtham H M , Sai Sanket M , Puneeth P |
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IJRTI_212305
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| Published Paper Id: | IJRTI2605134 |
| Published In: | Volume 11 Issue 5, May-2026 |
| DOI: | |
| Abstract: | SoilGuard aspires to make irrigation more intelligent, trustworthy, and waste-free by moving away from conventional guesswork and preset watering schedules to real-time, data-driven decision-making. The system allows farmers to understand actual field conditions rather than regular schedules or observation by monitoring the exact amount of moisture in the soil using tiny IoT sensors. These readings are continuously collected by the ESP8266 microcontroller and uploaded to the Blynk cloud platform, enabling users to easily and conveniently monitor their soil status from any location. Over time, accumulation of more data enables an LSTM to learn how the soil changes throughout the day-that is, how rapidly it dries out, how it responds to changing weather conditions, and how long it remains wet after irrigation. The model uses these patterns that it has learned to make educated guesses as to when the moisture level will fall below a healthy threshold. The system uses a relay that automatically switches on the water pump before the soil dries up. This makes sure plants get water whenever they need it without needing constant supervision or someone else's attention. As compared with simple systems working on the basis of thresholds, this method of prediction works much faster and more effectively. That helps to minimize wasting too much water; thus, the soil moisture levels can stay healthier. SoilGuard has been tested for its consistency and quick response, by ensuring a good amount of water is saved; hence, it is practical, affordable, and reliable for farmers who would like to experience a more modernized means of irrigation management that is eco-friendly. |
| Keywords: | LSTM Neural Network, Predictive Irrigation, ESP8266, Blynk Cloud, Autonomous Irrigation, Smart Agriculture, Internet of Things (IoT), Soil Moisture Monitoring, and Smart Farming. |
| Cite Article: | "SoilGuard: An IoT and LSTM-Based Predictive Irrigation System for Smart Farming", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.b282-b286, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605134.pdf |
| Downloads: | 00079 |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2605134
Registration ID:212305
Published In: Volume 11 Issue 5, May-2026
DOI (Digital Object Identifier):
Page No: b282-b286 Country: Bengaluru, Karnataka, India Research Area: Electronics & Communication Engg. Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2605134 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2605134 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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