Smart IoT-driven precision agriculture: Land mapping, crop prediction, and irrigation system

Area of Science:

• Agricultural Science
• Computer Science
• Environmental Science

Background:

• Increasing global population necessitates advanced precision agriculture for food security.
• Existing research often lacks integrated smart technologies for sustainable farming systems.
• There is a need for automated systems to optimize resource management in agriculture.

Purpose of the Study:

• To present an integrated design of an IoT-based system for smart agricultural land and crop selection.
• To develop an automated irrigation system utilizing machine learning and fuzzy logic for precision agriculture.
• To enhance resource management and sustainability in farming through computational techniques.

Main Methods:

• Utilized Landsat-8 satellite imagery for agricultural analysis (vegetation, water, salinity indices) via K-means clustering.
• Employed machine learning algorithms (Linear Regression, Random Forest) for crop yield prediction and crop suitability analysis.
• Integrated an IoT network with soil sensors and a fuzzy logic-based, solar-powered irrigation system.

Main Results:

• Achieved high accuracy in crop yield prediction (up to 95.87% with Random Forest) and crop suitability prediction (97.35%).
• LSTM model effectively forecasted healthy vegetation area changes over time.
• Fuzzy logic irrigation system demonstrated significant water savings (around 61%) and faster calibration (66.23%) compared to traditional methods.

Conclusions:

• The integrated system effectively supports smart land and crop selection for sustainable agriculture.
• Advanced computational techniques, including machine learning and fuzzy logic, significantly improve agricultural efficiency and resource management.
• The developed system offers a scalable solution for precision agriculture, addressing global food demands and water scarcity.