<![CDATA[The selection of appropriate crop seeds is a critical factor in enhancing agricultural productivity. Nevertheless, farmers frequently face challenges when trying to determine which crop seeds match the unique features of their surrounding environment and geographic location. To address this, the study introduces a smart recommendation model that leverages real-time environmental measurements alongside vital geographical characteristics to support informed seed selection. The environmental features include temperature, humidity, and rainfall, while the geographical attributes encompass nitrogen, phosphorus, and potassium content. A Gradient Boosting classification algorithm is employed to model the relationships between these features and the optimal crop seed types, based on a labeled dataset. Experimental results demonstrate that the model achieves strong classification performance, indicating its effectiveness in delivering accurate and context-specific seed recommendations. The proposed system highlights the potential of data-driven approaches in supporting agricultural decision-making and can be further integrated into smart farming platforms to optimize crop planning and seed selection, ultimately contributing to improved agricultural outcomes.]]>
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