IoT-Based Strawberry Disease Prediction System for Smart Farming

Area of Science:

• Agricultural Science
• Computer Science
• Environmental Science

Background:

• Accurate crop disease prediction requires integrated analysis beyond individual factors.
• Existing systems often lack comprehensive data handling and reliable communication for agricultural environments.

Purpose of the Study:

• To develop a cloud-based integrated system for comprehensive agricultural environment information analysis and prediction.
• To enhance farm management through a unified platform for device, data, and model management.
• To improve the accuracy of crop disease prediction models.

Main Methods:

• Development of a cloud-based Farm as a Service (FaaS) integrated system.
• Integration of Internet of Things (IoT) device management and data analysis.
• Construction of an IoT-Hub network model for reliable data transfer in diverse environments.
• Verification using a strawberry infection prediction system compared against existing models.

Main Results:

• The FaaS system effectively integrates data collection, analysis, and prediction for agricultural environments.
• The IoT-Hub model demonstrated high communication reliability, even in poor network conditions.
• The integrated FaaS system showed improved performance in strawberry disease prediction compared to other models.

Conclusions:

• A comprehensive, cloud-based FaaS system significantly enhances crop disease prediction accuracy.
• The developed IoT-Hub network ensures stable and reliable data communication essential for smart farming.
• This integrated approach offers a robust solution for modern agricultural challenges.