Skip to content
Research Article Open access CC BY 4.0

Laboratory Evaluation of Soil Moisture Sensors for Precision Irrigation in Agriculture

Aashabai A.R, Hari. N, Charith Kumar. K, Srinivasulu. M, Manoj Kumar. G

International Journal of Environment and Climate Change · pp. 321–332 · Published 9 Aug 2024

10.9734/ijecc/2024/v14i84353

Abstract

Water is essential for agricultural production and food security, making efficient water use critical. Accurate measurement of soil moisture is vital for scheduling irrigation, ensuring that crops receive the right amount of water at the right time. This prevents both under- and over-irrigation, conserving water and maximizing crop production. Currently, digital soil moisture sensors are used for their accuracy and instant measurement capabilities. The soil moisture sensor's functionality was evaluated through observations of four Capacitive soil moisture sensors 1.2 (SMS A, B, C, D) at various moisture levels, with gravimetric method verification. The calibrated linear equations demonstrated a strong linear relationship for each sensor. The coefficient of determination (R2) were also found to be 0.92, 0.93, 0.91 and 0.93 respectively. These R² values indicate a strong linear relationship for each sensor. An automated drip irrigation module was developed by integrating these soil moisture sensors with the Arduino platform. The system activates the irrigation motor when soil moisture content falls below the field capacity or a desired set value, and turns it off once the required moisture level is reached. This sensor-based automated drip irrigation module enables farmers to irrigate their fields precisely and efficiently, delivering the right quantity of water at the right time. The system is also suitable for deficit irrigation practices, improving crop yield and water use efficiency.

Soil moisture field capacity sensor arduino automatic irrigation water use efficiency

References (34)

  1. 1 Remote Sensing and Cropping Practices: A Review [DOI]
  2. 2 The internet of things in agriculture for sustainable rural development [DOI]
  3. 3 Monitoring and Control Systems in Agriculture Using Intelligent Sensor Techniques: A Review of the Aeroponic System [DOI]
  4. 4 Comparison of four soil moisture sensor types under field conditions in Switzerland [DOI]
  5. 5 A wireless application of drip irrigation automation supported by soil moisture sensors [DOI]
  6. 6 A smart farming concept based on smart embedded electronics, internet of things and wireless sensor network [DOI]
  7. 7 Automation of soil moisture sensor-based basin irrigation system [DOI]
  8. 8 Design and Performance Evaluation of a Low-Cost Autonomous Sensor Interface for a Smart IoT-Based Irrigation Monitoring and Control System [DOI]
  9. 9 Greenhouse Monitoring and Automation Using Arduino: a Review on Precision Farming and Internet of Things (IoT) [DOI]
  10. 10 Designing Low-Cost Capacitive-Based Soil Moisture Sensor and Smart Monitoring Unit Operated by Solar Cells for Greenhouse Irrigation Management [DOI]
  11. 11 Evaluation of IoT based smart drip irrigation and ETc based system for sweet corn [DOI]
  12. 12 Multi-Sensors based smart nutrient reuse management system for closed soilless culture under protected cultivation [DOI]
  13. 13 Sensor-based irrigation management of soilless basil using a new smart irrigation system: Effects of set-point on plant physiological responses and crop performance [DOI]
  14. 14 Irrigation Management of Greenhouse Tomato and Cucumber Using Tensiometer: Effects on Yield, Quality and Water Use [DOI]
  15. 15 Evaluation of Deficit Irrigation and Water Quality on Production and Water Productivity of Tomato in Greenhouse [DOI]
  16. 16 Automated Irrigation System Using a Wireless Sensor Network and GPRS Module [DOI]
  17. 17 Design and Realization of Low Cost Control for Greenhouse Environment with Remote Control [DOI]
  18. 18 Effects of Biochar on Soil Properties and Tomato Growth [DOI]
  19. 19 Evaluation of the effectiveness of an automated irrigation system using wheat crops [DOI]
  20. 20 Evaluation of Soil Moisture Sensors under Intelligent Irrigation Systems for Economical Crops in Arid Regions [DOI]
  21. 21 Remote monitoring systems in greenhouse based on NodeMCU ESP8266 microcontroller and Android [DOI]
  22. 22 Development of Low-cost Soil Moisture Monitoring System for Efficient Irrigation Water Management of Upland Crops [DOI]
  23. 23 Irrigation Scheduling in Processing Tomato to Save Water: A Smart Approach Combining Plant and Soil Monitoring [DOI]
  24. 24 Field evaluation and performance of capacitance probes for automated drip irrigation of watermelons [DOI]
  25. 25 SENSOR AND CLOUD BASED SMART IRRIGATION SYSTEM WITH ARDUINO: A TECHNICAL REVIEW [DOI]
  26. 26 The Internet of Things: Vision, Architecture and Applications [DOI]
  27. 27 Water use efficiency and economics of tomato using drip irrigation under netthouse conditions.
  28. 28 Soil health assessment under protected cultivation of vegetable crops in North West Himalayas [DOI]
  29. 29 Development and integration of soil moisture sensor with drip system for precise irrigation scheduling through mobile phone [DOI]
  30. 30 Assesment of Variety of Tomato for Growth Parameters under Cover and Open Field Conditon [DOI]
  31. 31 Long Term Cultivation Effect on Soil Health Inside Poly Houses Under Sub-Tropical Condition of North East India [DOI]
  32. 32 The influence of water field capacity and fertilizer combinations on tomato under intelligent drip in greenhouse [DOI]
  33. 33 EVALUATION OF AUTOMATED DRIP IRRIGATION SYSTEM USING LOW COST SOIL MOISTURE SENSOR
  34. 34 DEVELOPMENT OF A SENSOR BASED AUTOMATED DRIP IRRIGATION MODULE

Cited by 2

PSO-LSTM for Optimum Fertilizer Level Forecasting with NPK Virtual Sensors

Abd Rahman Dzaky, Muhammad Faris Fathoni, Aji Gautama Putrada · 2024 7th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) · 2024

Showing 1 of 2 known citations — external sources report more than can currently be individually listed.

Related research

Article metrics

Real usage data collected on this platform.

11

Page views

0

PDF downloads

0

Outbound clicks

2

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.