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The use of cutting-edge technology has become essential in the quickly changing field of agriculture, where uncertainties are caused by shifting soil conditions, erratic weather patterns, and dynamic insect dynamics. With the use of cutting-edge machine learning algorithms, this research aims to address the issues facing contemporary agriculture by providing accurate production predictions. Using an all-encompassing investigation of agricultural data that includes a variety of factors, including meteorological data, soil characteristics, and insect dynamics, the main objective is to transform traditional farming practices. The use of cutting-edge technology has become essential in the quickly changing field of agriculture, where uncertainties are caused by shifting soil conditions, erratic weather patterns, and dynamic insect dynamics. With the use of cutting-edge machine learning algorithms, this research aims to address the issues facing contemporary agriculture by providing accurate production predictions. By means of an all- encompassing investigation of agricultural data that includes a variety of factors, including meteorological data, soil characteristics, and insect dynamics, the main objective is to transform traditional farming practices.

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Predictive Modeling for Optimal Crop Yields

  • 会议论文

  • 发表日期：

    2026年

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  • S. Sravan Kumar,
  • K. Janakiram,
  • T. Govardhan Reddy,
  • T. Kumar,
  • S. Amutha

摘要

The use of cutting-edge technology has become essential in the quickly changing field of agriculture, where uncertainties are caused by shifting soil conditions, erratic weather patterns, and dynamic insect dynamics. With the use of cutting-edge machine learning algorithms, this research aims to address the issues facing contemporary agriculture by providing accurate production predictions. Using an all-encompassing investigation of agricultural data that includes a variety of factors, including meteorological data, soil characteristics, and insect dynamics, the main objective is to transform traditional farming practices. The use of cutting-edge technology has become essential in the quickly changing field of agriculture, where uncertainties are caused by shifting soil conditions, erratic weather patterns, and dynamic insect dynamics. With the use of cutting-edge machine learning algorithms, this research aims to address the issues facing contemporary agriculture by providing accurate production predictions. By means of an all- encompassing investigation of agricultural data that includes a variety of factors, including meteorological data, soil characteristics, and insect dynamics, the main objective is to transform traditional farming practices.