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Poor water use and late illness detection force farmers to struggle, which hurts their crops and squanders resources. This study presents a computerized precision farming system combining machine learning, humidity sensors, and robot automation to improve irrigation and disease control. The technology operates in two phases: first, humidity sensors calculate the ideal watering amount by assessing soil moisture and applying a Random Forest model. Second, disease detection sensors identify agricultural diseases and forecast disease outbreaks using a Gradient Boosting Regressor (GBR), thereby guiding robot pesticide spraying. By integrating machine learning with real-time environmental monitoring, the technology minimizes human interaction while assuring exact irrigation and tailored pesticide administration. This strategy avoids the usage of pesticides and excessive water consumption while enhancing resource efficiency and crop health. The proposed system is scalable and adaptive to varied farming situations, making it a good alternative for modern precision agriculture. By combining predictive analytics and automation, the model enables data-driven decision-making in farming, supporting sustainable agricultural practices and boosting overall output.

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An Integrated Machine Learning Model for Automated Drip Irrigation and Crop Disease Management Using Robotics

  • 会议论文

  • 发表日期：

    2026年

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  • Uttam Patole,
  • Manish Shrivastava,
  • Archana Ugale,
  • Abhishek Deshmukh

摘要

Poor water use and late illness detection force farmers to struggle, which hurts their crops and squanders resources. This study presents a computerized precision farming system combining machine learning, humidity sensors, and robot automation to improve irrigation and disease control. The technology operates in two phases: first, humidity sensors calculate the ideal watering amount by assessing soil moisture and applying a Random Forest model. Second, disease detection sensors identify agricultural diseases and forecast disease outbreaks using a Gradient Boosting Regressor (GBR), thereby guiding robot pesticide spraying. By integrating machine learning with real-time environmental monitoring, the technology minimizes human interaction while assuring exact irrigation and tailored pesticide administration. This strategy avoids the usage of pesticides and excessive water consumption while enhancing resource efficiency and crop health. The proposed system is scalable and adaptive to varied farming situations, making it a good alternative for modern precision agriculture. By combining predictive analytics and automation, the model enables data-driven decision-making in farming, supporting sustainable agricultural practices and boosting overall output.