Agricultural Remote Sensing with Case-Based Reasoning

The integration of Case-Based Reasoning (CBR) with Internet of Things (IoT) technologies are proposed for enhancing decision-making efficiency in agricultural practices. The proposed conceptual model (CBRIoT) leverages IoT devices such as soil moisture sensors, weather stations, and drones to collect real-time environmental data, which is then processed, stored, and integrated into a central knowledge base. The CBR system retrieves similar past cases based on new IoT data, offering personalized recommendations for agricultural interventions, such as irrigation, pest control, and disease management. A real-time feedback loop allows dynamic adaptation of recommendations, responding to changing conditions like weather shifts or pest infestations. The system’s performance is evaluated across various datasets (Agriculture-Vision, Extended Agriculture-Vision, Smart Agriculture, and others), demonstrating that the CBRIoT model outperforms traditional models in terms of decision-making efficiency, processing time, and scalability. The results show that the CBRIoT model significantly improves decision-making accuracy and processing time compared to Vegetation Index Models (VIM), Machine Learning-Based Classification (MLBC), Geostatistical Models (GM), and Rule-Based Decision Support Systems (RBDSS). The system continuously updates its knowledge base through new data and interventions, leading to a self-improving decision support system that enhances agricultural practices over time. The CBR+IoT approach provides a scalable and efficient solution for modernizing agriculture, offering actionable insights to farmers and contributing to better resource management and crop yields.