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This current investigation is intended to create an overarching state-of-the-art system that integrates unsupervised soil clustering with pattern recognition-based anomaly detection for the intention of revolutionizing precision farming. Most conventional techniques of classifying soil do not involve dynamic variation in the properties of soil and are unable to detect anomalous conditions that affect agricultural productivity. By incorporating the application of adaptive incremental clustering algorithms and pattern-based analysis techniques, this research introduces a better solution that can classify soil dynamically according to various attributes in addition to outlier detection from defined patterns. The new architecture continues prior research in auto-incremental clustering for dynamic soil classification and industrial anomaly detection by adding a two-phase framework: a high-level sophisticated unsupervised learning algorithm for dynamic soil classification that learns to accommodate new soil samples and environmental conditions, and a high- level sophisticated pattern recognition system that detects anomalous soil conditions through temporal changes in soil parameters. This merging is anticipated to enhance classification precision by 15-20% over existing approaches and decrease false positive anomaly detection by over 30%, thus enabling farmers to make more accurate choices in precision agriculture based on more trustworthy data.