Hybrid AI Frameworks for Smart Agriculture and Precision Farming Analytics
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This chapter explores the application of hybrid artificial intelligence (AI) frameworks in the domain of smart agriculture and precision farming. We present a comprehensive overview of how the integration of various AI techniques, including machine learning, deep learning, and ensemble methods, can revolutionize agricultural practices. A novel hybrid AI framework is proposed, designed to leverage data from diverse sources such as IoT sensors, drones, and satellites to provide actionable insights for farmers. The chapter details a research methodology for developing and evaluating a crop yield prediction system based on this framework. A synthetic dataset is created to simulate real-world agricultural conditions, and a comparative analysis of different machine learning models, including Random Forest, XGBoost, Gradient Boosting, and a hybrid ensemble, is conducted. The results demonstrate the superior performance of the hybrid ensemble model in accurately predicting crop yields. The chapter concludes with a discussion on the implications of these findings for the future of agriculture and outlines potential directions for future research.
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Principles of Hybrid Intelligent Systems
