Meta-Learning Approaches for Context-Aware Decision Support in Smart Agriculture and Autonomous Systems

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The increasing reliance on intelligent systems in agriculture and autonomous operations dictates the need for robust context-aware decision support systems (CADSS) to cope with heterogeneous and changing conditions. Conventional systems struggle with heterogeneous data and dynamic contexts, motivating the need for a meta-learning-driven solution. The paper proposes a CADSS-based meta-learning (CADSS-ML) framework to advance the adaptability and precision of the system in those domains. This proposed CADSS-ML framework develops hierarchical meta-learning using model-agnostic meta-learning (MAML) to allow fast task adaptation with ease and reptile algorithms for efficient optimization. Convolutional Neural Networks (CNN) have been used for feature extraction in spatial data, whereas transformer models have been used for temporal data. The Graph Neural Network (GNN) based module processes a sensor network topology, while an attention mechanism dynamically updates the weights on contextual variables. To perform decision-making, reinforcement learning with reward shaping guarantees adaptive and optimal control action outcomes. The proposed system also incorporates federated learning for data privacy across distributed sensor nodes. Experimental results confirm that improvements in prediction accuracy, decision reliability, and resource utilization are enhanced by 20%, 15%, and 12%, respectively, compared with baseline models. Some of the diverse applications of the proposed framework include precision farming, autonomous navigation, and smart irrigation.