Cloud Computing-Based Smart Agriculture Decision Support System (DSS) for Medium-Scale Farmers
Keywords:
Decision Support System, Cloud Computing, Smart Agriculture, Internet of Things, Resource OptimizationAbstract
In the era of Agriculture 4.0, the main challenge for medium-scale farmers is the inability to process massive data into tactical decisions and the high cost of IT infrastructure. Purpose: This study aims to design and test the effectiveness of a cloud computing-based Decision Support System (DSS) in optimizing the use of resources and crop yields. Method: This experimental study uses a system development methodology (SDLC) that integrates IoT with a hybrid cloud architecture, where soil and weather sensor data are processed through a Rule-Based Reasoning algorithm. Result: The test results showed a data transmission reliability rate of 98.5% with a decision recommendation accuracy of 92%.Significantly, this system is able to reduce water consumption by up to 32%, decrease fertilizer usage by 20%, and lower operational costs by 27%, which contributes to an estimated increase in crop yield of 10.3%. Conclusion: The implementation of a cloud-based DSS has proven to be an efficient and scalable solution for medium-scale farmers to migrate towards precision agriculture. Future research should integrate deep learning algorithms for visual pest detection to enhance the comprehensiveness of this intelligent system.
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