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In contemporary agriculture, harnessing data-driven methodologies is paramount for optimizing crop management and augmenting agricultural output. This project introduces a comprehensive framework focusing on crop prediction, recommendation, yield estimation, rainfall projection, and fertilizer suggestion. Leveraging historical and real-time data on soil attributes, environmental variables, and crop performance, the framework utilizes decision tree algorithms, random forest classifiers, and regression models to furnish actionable insights for farmers. Through an intuitive interface, the framework facilitates informed decision-making by recommending suitable crops based on soil conditions, predicting crop yields, forecasting rainfall patterns, and proposing optimal fertilizer utilization. The project underscores the pivotal role of data-driven strategies in modern agriculture and endeavors to equip farmers with efficient tools for augmenting crop productivity, resource management, and sustainability in agricultural practices.

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A Multi-stage Machine Learning Pipeline for Agro-Climatic Feasibility Analysis and Dynamic Yield with Price Prediction

  • 会议论文

  • 发表日期：

    2026年

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  • Subhadeep Barman,
  • Asmaul Hassan,
  • P. Lekha,
  • Ruhul Amin,
  • Priyanshi Xess

摘要

In contemporary agriculture, harnessing data-driven methodologies is paramount for optimizing crop management and augmenting agricultural output. This project introduces a comprehensive framework focusing on crop prediction, recommendation, yield estimation, rainfall projection, and fertilizer suggestion. Leveraging historical and real-time data on soil attributes, environmental variables, and crop performance, the framework utilizes decision tree algorithms, random forest classifiers, and regression models to furnish actionable insights for farmers. Through an intuitive interface, the framework facilitates informed decision-making by recommending suitable crops based on soil conditions, predicting crop yields, forecasting rainfall patterns, and proposing optimal fertilizer utilization. The project underscores the pivotal role of data-driven strategies in modern agriculture and endeavors to equip farmers with efficient tools for augmenting crop productivity, resource management, and sustainability in agricultural practices.