IOT-Based Adaptive Farming System using Acoustic Sensing for Smart Agriculture

IOT-Based Adaptive Farming System using Acoustic Sensing for Smart Agriculture

Ankur Poonia

Department of Electronics and Communication Engineering

R.V College Of Engineering Bengaluru, India

Yuvraj Singh Giriraj

Department of Electronics and Communication Engineering

R.V College Of Engineering Bengaluru, India

Pinaki Ranjan Nath

Department of Electronics and Communication Engineering

R.V College Of Engineering Bengaluru, India

Darshan Onkar

Department of Electronics and Communication Engineering

R.V College Of Engineering Bengaluru, India

AbstractThe increasing demand for sustainable and efficient agricultural practices has led to the integration of Internet of Things (IoT) technologies into farming systems. This paper presents an IoT-based adaptive farming system that combines environmental monitoring with acoustic sensing to automate key agricultural processes. The system utilizes a NodeMCU microcontroller connected to sensors that measure temperature, humidity, soil moisture, and light intensity, along with an acoustic sensor to detect pest activity. Based on real-time data, the system automates irrigation and generates pest alerts to support timely interventions. A mobile interface via the Blynk IoT platform pro- vides remote monitoring and control capabilities. The proposed system demonstrates improved efficiency, reduced manual effort, and enhanced crop protection, making it a promising solution for precision agriculture.

Index TermsIoT, Smart Agriculture, Acoustic Sensor, Pest Detection, Precision Farming, NodeMCU, Soil Moisture, Automa- tion.

1. INTRODUCTION

   Agriculture has always been a fundamental pillar of human civilization, providing food, economic stability, and employ- ment for a large portion of the global population. Despite its importance, modern agriculture faces several challenges, including unpredictable weather patterns, increasing pest in- festations, water scarcity, and the need to produce more food to meet the demands of a growing population.

   Traditional farming methods often rely on manual moni- toring, which is time-consuming, labor-intensive, and prone to human error. As a result, critical issues such as delayed detection of crop diseases or pests and inefficient use of water and fertilizers lead to reduced crop yields and environmental degradation.

   Recent advancements in the Internet of Things (IoT), em- bedded systems, and sensor networks offer promising solutions

   to these problems. IoT-based smart farming enables real- time monitoring of environmental conditions, automation of irrigation and pest control systems, and data-driven decision- making. By integrating various sensors and communication technologies, farmers can monitor their fields remotely, receive alerts, and optimize the use of resources.

   This paper introduces an IoT-based adaptive farming system that incorporates both environmental and acoustic sensors. The system is designed to monitor key parameters such as temperature, humidity, soil moisture, and light intensity, while also detecting pest activity through sound analysis. It aims to support precision agriculture by automating irrigation and pest control responses, improving efficiency, and minimizing crop losses.

2. MOTIVATION

   The global agricultural landscape is under increasing pres- sure due to rising food demand, diminishing arable land, and unpredictable climate conditions. Feeding a rapidly growing population requires not only an increase in food production but also the efficient use of available resources. Traditional farming practices, which often involve manual labor and reactive decision-making, are no longer sufficient to meet these modern challenges.

   Farmers commonly face difficulties such as delayed detec- tion of pests, overuse or underuse of water, and poor timing of crop interventions, all of which lead to lower yields and higher costs. Moreover, pests often go unnoticed until they cause visible damage, resulting in significant crop loss and reduced productivity.

   The emergence of IoT technology offers a transformative opportunity for agriculture. Smart farming systems allow for continuous environmental monitoring, automation of field

   activities, and real-time data analysis. These systems enable farmers to respond quickly and accurately to changing field conditions.

   The motivation behind this project is to design a cost- effective, intelligent farming solution that not only monitors environmental factors but also detects pest activity using acoustic sensing. By automating irrigation and pest control based on sensor data, the proposed system reduces human effort, conserves resources, and enhances crop health. This innovation represents a step forward in achieving sustainable, data-driven agriculture.

3. PROBLEM STATEMENT

   Modern agriculture continues to suffer from several ineffi- ciencies that hinder productivity and sustainability, especially in small and medium-scale farming operations. Among the most pressing issues are improper irrigation practices, delayed detection of pest infestations, and the absence of real-time data for informed decision-making.

   Many farmers still depend on traditional, labor-intensive methods to monitor environmental conditions, often rely- ing on visual inspection and fixed schedules for irrigation and pest control. This reactive approach frequently leads to under-irrigation or over-irrigation, which degrades soil quality, wastes water, and adversely affects plant growth. Similarly, pest attacks are often identified only after visible crop dam- age has occurredby which time significant yield loss is inevitable.

   Another key limitation is the lack of affordable and acces- sible tools to provide timely and accurate field data. Most existing smart farming systems either focus solely on environ- mental monitoring or require constant internet connectivity, which may not be feasible in rural areas.

   Therefore, there is a need for an integrated smart farming solution that:

   • Continuously monitors critical environmental parameters;

   • Detects pest activity in its early stages;

     • Responds automatically through actuation mechanisms like irrigation and alerts;

   • And supports remote monitoring and control.

   This project aims to develop such a system using a NodeMCU microcontroller, various environmental sensors, and an acoustic sensor for pest detectionoffering a compre- hensive, real-time, and cost-effective solution to the challenges faced in contemporary farming.

4. OBJECTIVES

   The primary objective of this project is to design and im- plement an IoT-based adaptive farming system that enhances crop productivity and promotes sustainable agriculture through automation and intelligent decision-making. The system inte- grates environmental and acoustic sensors to monitor real-time field conditions and respond accordingly.

   The specific objectives of the project are as follows:

   1. To develop an IoT-enabled smart farming system using the NodeMCU (ESP8266) microcontroller as the core process- ing and control unit.

   2. To monitor key environmental parameters such as tem- perature, humidity, soil moisture, and light intensity using appropriate sensors to assess crop conditions.

   3. To incorporatean acoustic sensor capable of detecting pest activity based on characteristic sound signatures, enabling early warning and control.

   4. To automate the irrigation system using a water pump controlled via a relay, triggered when soil moisture levels fall below a predefined threshold.

   5. To log environmental and pest data in real-time with accurate timestamps, allowing historical trend analysis and improved agricultural decision-making.

   6. To enable remote monitoring and control of the farming system via the Blynk IoT platform, providing farmers with mobile access to sensor readings and system alerts.

   7. To reduce human intervention and optimize resource usage, particularly water and pesticides, thereby improving sustainability and operational efficiency.

5. LITERATURE SURVEY

   Numerous studies have explored the application of IoT in agriculture to address challenges such as inefficient irrigation, environmental monitoring, and data-driven decision-making. However, most systems focus primarily on environmental sensing and lack integrated pest detection, especially through acoustic sensing. The following literature highlights key de- velopments and gaps:

   • [1] S. R. Nandurkar et al. (2014) Title: A Smart Agricul- tural Model Using IoT and Cloud Computing Summary: Pro- posed an IoT-based system for precision irrigation and environ- mental monitoring. Emphasized cloud storage and decision- making through data analytics. Limitation: Relies heavily on stable internet connectivity; lacks pest control features.

   • [2] P. Rawal and K. S. Malode (2018) Title: Monitoring and Controlling of Agriculture Field using IoT Summary: Im- plemented a real-time monitoring system using soil and envi- ronmental sensors. Enabled automation of irrigation schedules. Limitation: No pest detection mechanism included.

   • [3] A. Patil et al. (2017) Title: IoT Based Smart Farming System Summary: Developed a low-cost, scalable IoT solu- tion for monitoring temperature, humidity, and soil moisture. Limitation: Focuses only on irrigation; does not address crop protection or pest alerts.

   • [4] D. Kale and P. Satpute (2017) Title: Pest Detection and Control Technique using Image Processing Summary: Used computer vision for pest identification based on image recog- nition. Limitation: Computationally intensive and dependent on lighting conditions; not suitable for rural deployments with limited resources.

   • [5] R. G. Gharghan (2020) Title: Internet of Things (IoT) for Smart Agriculture: Technologies, Practices and Future Direction Summary: Provided a comprehensive review of IoT technologies and emerging trends in smart farming. Limita- tion: Theoretical in nature; lacked implementation details or validation.

   IJERTV14IS060061

   (This work is licensed under a Creative Commons Attribution 4.0 International License.)

   These studies demonstrate the effectiveness of IoT in agri- culture but reveal a recurring limitation: the absence of real- time pest detection, particularly using lightweight, low-power methods. This project fills that gap by integrating acoustic sensing to enable early pest detection and proactive control alongside traditional environmental monitoring.

6. PROPOSED METHODOLOGY

   This section outlines the architecture and working of the proposed adaptive farming system. The methodology inte- grates both hardware and software components to create a responsive, low-power, and cost-effective smart agriculture solution.

   1. System Overview

      The system is centered around the NodeMCU ESP8266 microcontroller, which acts as the main controller. It interfaces with multiple sensors to monitor environmental and pest- related data, processes the information, and controls actuators accordingly. The real-time sensor data is also transmitted to the Blynk IoT platform, enabling remote monitoring and control through a mobile application.

   2. Sensors and Data Acquisition

      The following sensors are used for continuous monitoring of the field:

      DHT11: Measures ambient temperature and humidity.

      Soil Moisture Sensor: Determines the water content in the soil.

      LDR (Light Dependent Resistor): Monitors ambient light intensity to assess sunlight availability.

      Acoustic Sensor: Detects high-frequency sound patterns typical of insect activity.

      Each sensor is polled periodically, and its readings are stored temporarily on the NodeMCU. Noise and anomalies are minimized using basic filtering techniques such as moving averages.

   3. Control Logic

      The system applies a set of logical conditions to determine necessary actions:

      If soil moisture falls below a defined threshold, a DC water pump is activated via a relay to irrigate the field.

      If temperature exceeds a critical value and humidity is low, early irrigation is triggered to prevent plant stress.

      If the acoustic sensor detects sound patterns matching known pest signatures, a buzzer is triggered as a repellent, and a pest alert is sent to the farmer via Blynk.

      When no significant environmental change is detected over a period, the microcontroller enters deep sleep mode to conserve power.

   4. Communication and Mobile Interface

      Data from the NodeMCU is transmitted over Wi-Fi using the Blynk IoT platform. The mobile app displays:

      Real-time sensor values (temperature, moisture, light, pest status)

      Notifications when thresholds are crossed Manual controls for the water pump and buzzer Historical data graphs for analysis

      This enables farmers to make decisions remotely and receive alerts promptly.

   5. System Architecture Diagram

   A block diagram of the system is shown in Fig. 1, which illustrates the interaction between sensors, NodeMCU, actua- tors, and the Blynk cloud platform.

   Fig. 1. System Architecture of IoT-based Adaptive Farming System

7. TRAINING AND OPTIMIZATION

   Testing and optimization are critical to ensuring the reli- ability of embedded IoT-based systems, especially in agri- cultural environments where conditions can be harsh and unpredictable. This section outlines the testing phases, op- timization strategies, and results obtained for the Adaptive Farming System based on acoustic sensing.

   1. Testing Strategy

      1. Unit Testing: Each component was individually tested for correctness:

         • DHT11 Sensor: Validated using calibrated thermometers across varying climates.

         • Soil Moisture Sensor: Compared against gravimetric soil sampling for accuracy.

         • LDR: Evaluated under shade, direct sunlight, and artifi- cial light conditions.

         • Acoustic Sensor: Tested with sound simulations, includ- ing claps and pest-like buzzes.

      2. Actuator Testing: Relay, pump, and buzzer were tested under field conditions:

         • Relay: Measured for switching response time (under 50 ms).

         • Pump: Assessed for flow rate and current draw under different loads.

           • Buzzer: Checked using a decibel meter (approx. 85 dB at trigger).

      3. NodeMCU Testing: The ESP8266 module was evaluated for the following:

         • Wi-Fi handshake time.

         • Memory usage under real-time logging.

         • GPIO pin switching speed and sability.

      4. Power System Testing: The 18650 Li-ion batteries and

         Fig. 3. Three types of potato leaf datasets- healthy, early blight and late blight

   2. Optimization Techniques

      1. Code Optimization:

         7805 voltage regulator were monitored for:

         • Voltage stability under varying loads.

         • Replaced blocking

           millis() function.

           delay() with non-blocking

         • Load capacity during prolonged use.

         • Thermal behavior during continuous 12+ hour operation.

           1. Integration Testing: Following unit testing, all modules were integrated and tested in combination. A 24-hour runtime trial confirmed synchronized operation of sensors, actuators, and communication subsystems under typical agricultural field conditions.

           2. Field Testing: The system was deployed in outdoor conditions and evaluated for:

              • Resistance to environmental stress (heat, wind, and rain).

              • Live alert performance via the Blynk mobile application.

              • Durability of enclosures, wiring, and sensor placements.

                Fig. 2. Early Prototype Setup for Field Testing

              • Modular code design for reusability and quick debugging.

              • Interrupt-based acoustic sensor activation reduced power drain.

      2. Power Optimization:

         • Deep sleep mode for NodeMCU during idle time ex- tended battery life from 12 to over 20 hours.

         • Selective sensor powering saved idle energy.

         • Capacitor-buffered LDO regulators smoothed transitions, reducing heat.

      3. Communication Optimization:

         • Tested MQTT protocol for faster, more efficient data transfer.

         • Batching of sensor readings reduced transmission fre- quency and energy usage.

      4. Sensor Filtering and Calibration:

         • Real-time rolling average filters used for smoothing noisy data.

         • Pest frequencies profiled over 7 days to distinguish am- bient vs. pest noise.

      5. Mechanical Optimization:

         • Breadboard replaced with custom PCB to minimize con- nection errors.

         • Waterproof 3D-printed enclosure with airflow and mesh filters improved reliability.

   3. Performance Summary

   After optimization, the system showed significant improve- ments across key metrics shown in the table below:

   Fig. 4. Performance Metrics Before and After Optimization.

8. RESULTS AND DISCUSSION

   The system demonstrated effective real-time monitoring and response to various environmental parameters. The outputs from temperature, humidity, soil moisture, light intensity, and acoustic sensors were logged and analyzed.

   1. Sensor Output and Responses

      • Temperature & Humidity: Measured using DHT11 sensor; triggered irrigation under high-temperature, low- humidity conditions.

      • Soil Moisture: DC pump activated via relay when values fell below the 30% threshold.

      • Light Intensity: LDR data used to avoid unnecessary watering during low-light periods.

      • Pest Detection: Acoustic sensor identified frequency anomalies, triggering a buzzer and app alert.

   2. Mobile Interface and Data Logging

      The Blynk platform was used to display live readings and historical data trends. Graphs plotted over 24-hour cycles assisted in understanding irrigation frequency and pest activity.

   3. Performance Evaluation

      The performance of the adaptive farming system was as- sessed based on key operational metrics such as battery life, pest detection accuracy, irrigation responsiveness, and sensor reliability. Field testing revealed that after optimization, the system exhibited improved battery efficiency, extending operational time from 12 hours to over 20 hours. The pest detection lag was reduced from an average of 3 seconds to approximately 1 second due to improved interrupt-driven acoustic analysis. Sensor readings showed enhanced accuracy following calibration and noise filtering. The integration with the Blynk platform enabled seamless real-time monitoring and control, validating the systems suitability for real-world deployment in small to medium-scale agricultural setups.

      TABLE I

      System Performance: Pre- vs Post-Optimization

9. CONCLUSION

   This work successfully developed and tested an adaptive IoT- based farming system that automates environmental mon- itoring and pest detection using acoustic and environmental sensors. The NodeMCU-based architecture demonstrated reli- able performance in automating irrigation and responding to pest threats, enhancing both efficiency and crop health.

   The integration with the Blynk app allowed real-time monitoring and control, empowering farmers with actionable insights. Overall, the system reduced manual intervention, pro- moted sustainable agriculture, and offered early intervention mechanisms critical for yield preservation.

10. FUTURE SCOPE

To scale and improve this system, several enhancements are planned:

• Solar Integration: Add solar panels for self-sustained energy supply.

• AI-based Pest Classification: Implement machine learn- ing to distinguish between types of pests based on sound signatures.

• LoRa/GSM Communication: Replace Wi-Fi with LoRa or GSM modules for rural areas with limited internet access.

• Multi-Zone Mapping: Deploy sensor nodes across dif- ferent field zones to enable zone-wise data collection and irrigation.

• Cloud Analytics and Chatbot: Use cloud storage for ad- vanced analytics and develop a farmer-assistance chatbot within the mobile app.

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