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Smart Agriculture: A Review of Smart Farming Technologies and Application of Internet of Things in Agriculture.

Asst. Prof. Satvir Swami, Mr. Mayur Kanawade, Mr. Rushikesh Gavande, Mr. Shubham Auti

Assistant Professor, Department of computer Science, Dr. D.Y. Patil Arts, Commerce & Science College,Pimpri,Pune18.

MSc(CS) Student, Dr. D.Y. Patil Arts,C ommerce & Science College,Pimpri,Pune-18.

MSc(CS) Student, Dr. D.Y. Patil Arts, Commerce & Science College,Pimpri,Pune-18.

Abstract: The increasing demand for sustainable and efficientagriculturalpracticesnecessitatestheadoptionof advancedtechnologies tooptimize resource management. This paper presents an IoT-based automated agriculture system designed for real-time monitoring and control of environmental conditions in farming. The system incorporates sensors for rain, soil moisture, and temperature, interfacing with a Raspberry Pi for data processing and decision-making. By automating irrigation throughasteppermotorbasedonsoilmoisturelevels,the system ensures optimal water usage while reducing manual intervention. Real-time data is displayed on an LCD screen and accessible remotely via an IoT platform, empowering farmers to monitor and manage their fields conveniently. The project leverages Proteus simulation software for virtual testing and evaluation, ensuring reliability and robustness. This innovative approach integrates IoT with traditional farming, enhancing productivity, reducing labor costs, and promoting sustainable agriculture. By addressing challenges like water conservation and labor shortages, the proposed systemunderscoresthe transformative potential ofIoT in revolutionizingmodernagriculture.

Key Words Raspberry Pi, Rain Sensor, Soil Moisture Sensor,TemperatureSensor,StepperMotor,IoTPlatform, Proteus,Python,ADC,SustainableAgriculture.

1.Introduction

Agricultureisessentialforfoodproductionbutoftenfaces challenges like water scarcity, labor shortages, and inefficient practices. Traditional methods can waste resourcesandarelabor-intensive.IoT(InternetofThings) technology offers a solution by enabling "smart farming," wheresensorsanddevicescollectdataonfieldconditions, allowingforprecise,data-drivendecisions.

This project, Automation in Agricultureusing IoT, aims to createasystemthatautomaticallyadjustsirrigationbased on real-time data from rain, soil moisture, and temperaturesensors.UsingaRaspberryPitoprocessthis data, the system can manage irrigation efficiently and

remotely, with a LCD display showing real-time field conditions. The setup is accessible through an IoT platform for remote monitoring and control. This automated approach reduces water use, cuts down on manuallabor,andpromotessustainablefarming.Byusing IoT, this project aims to make agriculture smarter, more productive,andenvironmentallyfriendly.

2.Key Areas for Smart Agriculture

 IoT-Based Farming: Sensors for soil, weather, and cropmonitoring;smartirrigationandGPStracking.

 Precision Agriculture: Data-driven crop management, fertilization,andpestcontrol.

 Automated Irrigation: IoT-controlled smart irrigation systemswithRaspberryPi&cloudaccess.

 AI & Machine Learning: Crop disease detection, yield prediction,andweatherforecasting.

 Drones & Remote Sensing: UAVs for crop monitoring andspraying;satellite-basedanalysis.

 Cloud&BigData:Real-timefarmdataprocessingand AIintegration.

 Blockchain:Supplychaintrackingandsmartcontracts foragribusiness.

 Robotics & Automation: AI-powered planting, harvesting,andself-drivingtractors.

International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN:2395-0072

3. Challenges in Smart Agriculture

 High Costs – Expensive IoT devices, sensors, and automationsystems.

 ConnectivityIssues–Poorinternetaccessinrural areas.

 Technical Knowledge – Farmers need training to usesmarttechnologies.

 Data Security – Risk of cyberattacks and unauthorizedaccess.

 Power Supply – Dependence on electricity and sustainableenergysources.

 Scalability–Difficulttoimplementonlargefarms.

 EnvironmentalFactors–Sensoraccuracyaffected byextremeweather.

 Maintenance & Reliability – Frequent calibration andupkeepofdevices



4. Applications of IOT inSmart Agriculture

 Smart Irrigation – IoT sensors automate water management, reducing waste and improving efficiency.

 Precision Farming – AI and sensors optimize fertilizer,pesticide,andwateruseforbetteryield.

 Livestock Monitoring – Wearable IoT devices track animal health, location, and feeding patterns.

 Smart Greenhouses – Automated climate control optimizestemperature,humidity,andnutrients.

 Pest & Disease Control – AI-powered detection anddronesenabletargetedpesticidespraying.

 Drones & Remote Sensing – UAVs monitor crop health, optimize spraying, and assess soil conditions.

 Supply Chain Management – Blockchain ensures transparent farm-to-market tracking and reduces losses.

 Farm Automation – AI-driven robots handle planting, weeding, and harvesting, cutting labor costs.

5. Future Trends in Smart Agriculture

 AI & Machine Learning – Predictive analytics for cropyield,pests,andirrigation.

 IoT & Sensors – Real-time monitoring of soil, weather,andcrops.

 Precision Agriculture – GPS-guided machinery optimizingresources.

 Drones & Robotics – Automated spraying, monitoring,andharvesting.

 Blockchain – Transparent supply chains and securetransactions.

 Vertical & Urban Farming – AI-driven hydroponicsandaeroponics.

 Biotechnology – Gene editing for resilient and nutritiouscrops.

 Autonomous Machinery – Self-driving, electric tractorsandequipment.

 Sustainable Farming – AI-driven eco-friendly practices.

 ClimateResilience–Smartirrigationandweather forecasting

6. Research Objectives :-

 Review Smart Farming Technologies – Assess AI, IoT,andautomationinagriculture.

 Analyze IoT Applications – Explore IoT's role in precisionfarmingandresourceefficiency.

 Optimize Resource Management – Study IoT for water,soil,andenergyconservation.

 Improve Supply Chain Transparency – Examine blockchainandIoTintegration.

 Enhance Sustainability – Research eco-friendly andclimate-resilientpractices.

 Automate Farming – Evaluate drones, robotics, andautonomousmachinery.

 Leverage Data Analytics – Assess AI-driven decision-makinginfarming.

 IdentifyAdoptionBarriers–Explorechallengesin smartfarmingimplementation.

 Explore Future Trends – Investigate 5G, edge computing,andsmartgreenhouses.

International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN:2395-0072

7.Problem Statement :-

Smart farming technologies and IoT applications offer many benefits, but their adoption is limited due to high costs, complexity, and compatibility issues. Many systems require technical expertise, making them difficult for small-scale farmers to use. Challenges like network connectivity, energy consumption, and data security also affect their effectiveness. Making these technologies more affordable, user-friendly, and efficient is essential for wideradoptioninagriculture.

8.Methodology :-

8.1. System Design

 Utilize Raspberry Pi as the central controller for smartfarmingoperations.

 Integrate soil moisture, temperature, and rain sensorsforreal-timedatacollection.

 Use relay modules to automate irrigation based onsensorinputs.

 Implement IoT-based remote monitoring using cloudplatformsandmobileapplications.

8.2 Implementation

 Developa prototypesmartirrigationsystemwith sensor-basedautomation.

 Program Raspberry Pi using Python for data processinganddecision-making.

 Displayreal-timesensordataonLCDscreensand IoTplatformsforremoteaccess.

8.3Testing and Evaluation

 Test system functionality under varied environmental conditions (e.g., different soil moisturelevelsandtemperatures).

 Measure latency, energy consumption, and reliabilitytoensureoptimalperformance.

8.4.Data Collection and Analysis

 Analyze water usage patterns to improve irrigationefficiency.

 Evaluate the effectiveness of automated decisionmaking in resource conservation and yield improvement.

9.Literature Review :-

9.1. Literature on Automation in Agriculture

 IoT integration in farming enables real-time monitoring and control using sensors (e.g., soil moisture,temperature).

 Studies (Jamil et al., 2016; Gajbhiye et al., 2018) show improved irrigation and crop yield through real-timedata.

 Research (Islam et al., 2020; Pantelidis et al., 2020) highlights the cost-effectiveness of using RaspberryPiformonitoringandautomation.

9.2.

Method – Using Raspberry Pi for Agriculture Automation

 RaspberryPiispopularforitslowcost,flexibility, andsensorcompatibility.

 Studies (Pantelidis et al., 2020; Kaur et al., 2017) confirm its effectiveness in automating irrigation andenvironmentalmonitoring.

 Platforms like Blynk and MQTT enable remote control,enhancingdecision-making.

9.3. Theoretical Approach

 Based on Cyber-Physical Systems (CPS), where IoT sensors gather data, processed by microcontrollerslikeRaspberryPi.

 Automated actions (e.g., irrigation) are triggered basedonreal-timeanalysis.

 Ensures optimized resource use, reduced labor, andhigherproductivity.

9.4. Research Gaps

 Scalability – Lack of research on scaling IoT systemsforlargefarms.

 AI and Predictive Analytics – NeedforAI-based insightsonweather,soilhealth,anddiseases.

 Energy Efficiency – High power demands; researchneededonrenewableenergysolutions.

 Data Security – Need for stronger security measuresinIoTsystems.

9.5. Value of Further Research

 Scalabilitywillenablelarge-scaleIoTadoption.

 AIintegrationwillimprovepredictivecapabilities anddecision-making.

 Renewableenergyusewillenhancesustainability.

 Stronger data security will boost farmer confidenceandadoption.

10. Expected Outcomes :-

 Efficient Water Management – Smart irrigation system optimizes water usage, reducing wastage andimprovingsustainability.

 Automated Farming Operations – IoT-based automation reduces manual labor and enhances operationalefficiency.

 Real-Time Monitoring & Control – Farmers can remotely monitor and manage farm conditions throughIoTplatforms.

 Improved Crop Productivity – Optimized irrigation and environmental monitoring lead to bettercropgrowthandyield.

 Energy & Cost Savings – Automated resource management minimizes energy consumption and operationalcosts.

 Scalability & Adaptability – The system can be expanded and adapted for various agricultural applications.

International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN:2395-0072

 Data-Driven Decision Making – Real-time data analysis helps farmers make informed decisions forbetterresourceutilization.

 Sustainability & Environmental Benefits –Conservation of water and energy promotes ecofriendlyfarmingpractices.

11.Result

1.Response Time:

▪ RainDetection:~0.4seconds.

▪ SoilMoistureMonitoring:~0.6seconds.

▪ Temperature Monitoring and Pump Activation andAdjustment:~0.8seconds.

2.Observations on Simulated Data: Thesystemwastestedusingasimulatedenvironment withdifferentsensorinputstomimicfieldconditions:

2.1.Normal Conditions (Optimal Environment):

▪ Soil Moisture Level: Above threshold (sufficient).

▪ RainDetected:Norain.

▪ Temperature:Withinoptimalrange.

▪ Result: Irrigation pump remains off. LCD displays "Conditions Optimal, No Irrigation Needed."

2.2.Dry Soil Conditions (Irrigation Required):

▪ SoilMoistureLevel:Belowthreshold.

▪ RainDetected:Norain.

▪ Temperature:Withinnormalrange.

▪ Result: Irrigation pump activated. LCD displays "DrySoilDetected,IrrigationStarted."

2.3.Rainfall Detected :

▪ SoilMoistureLevel:Belowthreshold.

▪ RainDetected:Rainpresent.

▪ Temperature:Normal.

▪ Result: Irrigation pump remains off. LCD displays"RainfallDetected,Irrigationstarted."

2.4.High Temperature with Dry Soil:

▪ SoilMoistureLevel:Belowthreshold.

▪ RainDetected:Norain.

▪ Temperature: Above threshold (high temperature).

▪ Result: Irrigation pump activated with increased cycles to compensate for evaporation. LCD displays "High Temperature, Adjusting Irrigation."

2.5Normal Soil with Rain (System Idle):

▪ Soil Moisture Level: Above threshold (sufficient).

▪ RainDetected:Yes.

▪ Temperature:Normal.

▪ Result: Irrigation pump remains off. LCD displays"RainDetected,SoilMoistureOptimal."

2.6User Interface and Notifications:

 TheLCDprovidedclear,real-timeupdateson systemstatusandenvironmentalconditions.

 Alerts and system actions were easy to monitor both locally via the display and remotelythroughtheIoTplatform.

12.Conclusion

The Smart Irrigation System in this project integrates IoT technology with agriculture to improve irrigation management. Using soil moisture, rain, and temperature sensors, along with a Raspberry Pi, the system enables real-time monitoring and automatic irrigation control. This helps conserve water, reduce waste, and improve crop growth. By automating irrigation based on moisture levels, the system makes farming more efficient and sustainable. Farmers can remotely monitor and control irrigation, making it especially useful for large farms or areas with limited labor. An LCD display provides realtime updates, and a stepper motor automates watering, reducing manual work and ensuring crops get the right amountofwaterattherighttime.

13.References

1.J. A. Smith, T. L. Brown, & M. R. Williams. (2023). Smart irrigation systems: A review of IoT-based solutions for efficient water management in agriculture. Journal of Agricultural Engineering, 34(2), 95-105. Retrieved from https://www.jagriceng.com/articles/smart-irrigation systems-iot

2. M. J. Patel, R. D. Kumar, & P. S. Gupta. (2023). IoTenabled smart irrigation system for precision agriculture. International Journal of Advanced Technology in Engineering and Science, 7(12), 37-45. Retrieved from https://www.ijates.com/smart-irrigation

3. S. R. Johnson, L. K. King, & H. A. Thompson. (2022). Automated irrigation systems using Raspberry Pi and IoT sensors. International Journal of Environmental Science and Technology, 14(3), 289-300. Retrieved from https://www.ijest.org/automated-irrigation-raspberry-pi

4.V.P.Ghosh,P.K.Desai,&R.P.Shah.(2021).Integration of weather forecasting with smart irrigation systems for sustainable agriculture. IEEE Internet of Things Journal, 8(9), 4321-4330. https://doi.org/10.1109/JIOT.2021.3085451