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Advancements in technology have revolutionized modern farming, making precision agriculture indispensable in tackling its challenges. This paper introduces an integrated system aimed at boosting agricultural productivity by merging crop recommendation and disease prediction. Through the utilization of machine learning algorithms and data analytics, the proposed system delivers tailored suggestions for crop selection while simultaneously forecasting potential diseases that may affect the chosen crops. The disease prediction component employs a robust machine learning model trained on a vast dataset of crop diseases, taking into account various factors such as weather patterns, soil health, and historical disease occurrences to anticipate specific threats. By identifying potential diseases early on, farmers can proactively implement preventive measures, thereby mitigating crop losses and minimizing the need for excessive pesticide application. This paper adds to the burgeoning field of precision agriculture, showcasing the practical application of machine learning and data analytics in addressing the intricate challenges encountered by farmers.

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Integrated Agricultural Decision Support System Leveraging Random Forest for Crop Prediction and EfficientNet B0 for Disease Prediction

  • 会议论文

  • 发表日期：

    2025年

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  • Dipmala Salunke,
  • Rutwik Shinde,
  • Tejas Chechar,
  • Ajay Biradar,
  • Kiran Patil,
  • Santosh Borde,
  • Sonali Rangadale,
  • Pallavi Tekade

摘要

Advancements in technology have revolutionized modern farming, making precision agriculture indispensable in tackling its challenges. This paper introduces an integrated system aimed at boosting agricultural productivity by merging crop recommendation and disease prediction. Through the utilization of machine learning algorithms and data analytics, the proposed system delivers tailored suggestions for crop selection while simultaneously forecasting potential diseases that may affect the chosen crops. The disease prediction component employs a robust machine learning model trained on a vast dataset of crop diseases, taking into account various factors such as weather patterns, soil health, and historical disease occurrences to anticipate specific threats. By identifying potential diseases early on, farmers can proactively implement preventive measures, thereby mitigating crop losses and minimizing the need for excessive pesticide application. This paper adds to the burgeoning field of precision agriculture, showcasing the practical application of machine learning and data analytics in addressing the intricate challenges encountered by farmers.