Neurocognitive-Inspired Memory Architectures for Agricultural Knowledge Systems: Performance Analysis of Hybrid Approaches

Abstract

This paper presents an experimental analysis of neuroscience-inspired memory architectures for agricultural information systems. Drawing from biological memory principles, we implement and evaluate four distinct approaches - Vector Database, Knowledge Graph, Finite State Machine, and a novel Hybrid Memory architecture that integrates these components. Our controlled experiments across diverse agricultural queries demonstrate that the biomimetic hybrid architecture achieves superior relevance scores (0.753) compared to single-component approaches while maintaining acceptable performance tradeoffs. Matrix-based performance analysis reveals how different architectures excel in specific query types: Vector DB for factual retrieval, Knowledge Graph for relational queries, FSM for procedural information, and Hybrid Memory maintaining strong performance across all categories. This research provides quantitative evidence supporting specialized memory designs for agricultural knowledge systems, though current implementations lack multimodal capabilities that would further enhance agricultural decision support.