Agriculture is a key sector that supports the econ- omy and ensures food security, particularly in developing coun- tries where many people depend on farming for their livelihood. However, traditional farming methods are still widely practiced and mainly depend on manual observation and farmers’ expe- rience. This often leads to inefficiencies and challenges, such as unpredictable weather conditions, poor irrigation practices, soil degradation, and plant diseases. As a result, farmers frequently face reduced crop yields and financial losses.
To overcome these challenges, modern technologies like the Internet of Things (IoT) and Artificial Intelligence (AI) are being introduced into agriculture. This project presents a Smart Agriculture System that integrates IoT and AI to improve farming efficiency and productivity. The system uses IoT sensors installed in fields to continuously monitor key environmental factors such as soil moisture, temperature, and humidity. The collected data is transmitted to a cloud platform, where it is stored and analyzed in real time.
AI algorithms process this data to evaluate soil conditions and seasonal patterns, helping farmers select the most suitable crops for cultivation. In addition, an AI-based image processing system detects plant diseases from crop images and provides recommendations for appropriate fertilizers and pesticides. This helps in early detection and prevention of crop damage.
A user-friendly mobile application allows farmers to access real-time data, receive crop suggestions, get disease alerts, and learn about government schemes. Overall, this system reduces manual effort, improves resource management, increases pro- ductivity, and promotes sustainable farming practices.
Keywords
Internet of Things (IoT), Artificial Intelligence (AI), Smart Agriculture, Digital Farming, Preci- sion Farming, Farm Automation, Wireless Sensor Systems, Soil Condition Monitoring, Environmental Sensing, Real-Time Mon- itoring, Crop Yield Estimation, Smart Irrigation System, Plant Disease Detection, Convolutional Neural Networks (CNN), Image- Based Plant Analysis, Agricultural Data Analysis, Cloud-Enabled Farming System, IoT-Driven Monitoring, Decision Support Sys- tems, Sustainable Agriculture Technologies.
Conclusion
The Results and Discussion section provides a compre- hensive evaluation of how effectively the Smart Agriculture System performs under real-world conditions. The sensor output graph plays an important role by illustrating variations in key environmental parameters such as temperature, soil moisture, and humidity over several days. These continuous readings enable farmers to observe patterns and fluctuations in field conditions, helping them make timely and informed decisions regarding irrigation, crop care, and overall farm management. With access to both real-time and historical data, farmers gain a deeper understanding of how environmental factors influence crop growth.
The disease detection accuracy chart further demonstrates the effectiveness of the AI-based model integrated into the system, achieving an overall accuracy of around 95%, The model shows strong capability in identifying various plant diseases, including blight, mosaic virus, rust, mildew, and pest damage, with individual accuracy ranging between approxi- mately 93% and 97%. Additionally, the crop recommendation output supports better decision-making by suggesting suitable crops such as tomato, potato, corn, and green beans based on soil and weather analysis. Overall, the results highlight that the system successfully integrates monitoring, analysis, and intelligent recommendations, leading to improved productivity, efficient resource utilization, and more sustainable farming practices.
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How to Cite This Paper
Dolagovinda Mahanta, Debasish Pradhan, Baidehi Jena, BKSS Pattnaik (2026). Intelligent farming system leveraging IoT powered by AI Technology. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
