Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt

Area of Science:

• Agricultural Science
• Computer Science
• Environmental Science

Background:

• Improving farm productivity is crucial for global food security and profitability.
• Current manual data collection for crop performance is slow, labor-intensive, and prone to errors.
• Existing methods struggle to account for crucial environmental factors, leading to unreliable data.

Purpose of the Study:

• To design and present SmartFarmNet, an Internet of Things (IoT)-based platform for automated farm data collection and analysis.
• To develop a system capable of filtering invalid data and providing personalized crop recommendations.
• To enhance farm productivity and profitability through data-driven insights.

Main Methods:

• Utilized a wide range of IoT devices, including sensors, cameras, and weather stations, for data acquisition.
• Developed algorithms for automated data correlation and invalid data filtering.
• Implemented cloud storage for efficient data management and analysis.
• Integrated mobile applications for human observations and data recording.

Main Results:

• SmartFarmNet successfully automates the collection of environmental, soil, fertilization, and irrigation data.
• The platform effectively filters out invalid data, improving the accuracy of crop performance assessments.
• Personalized crop forecasts and recommendations are computed for specific farms.
• SmartFarmNet is the world's largest system of its kind, supporting numerous sensors, crops, and users.

Conclusions:

• SmartFarmNet offers a scalable and efficient solution for modern agriculture.
• The platform significantly improves the reliability and utility of farm performance data.
• Automated data analysis and personalized recommendations empower farmers to increase productivity and profitability.