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Agriculture is a critical sector in our nation, with India ranking as the second-largest producer of rice and wheat globally. The selection of an appropriate crop for cultivation plays a significant role in farming, taking into account soil type and nutrient availability. Crucial factors influencing the growth and health of plants include soil conditions, which vary due to changes in climate, soil pH, and Nigrogen, Phosphorus, Potassium (NPK) values. The cultivation of rice in South India and wheat in North India illustrates how the choice of crop varies by region. Failure to choose a suitable crop can lead to decreased production turnover. The objective is to assist farmers in selecting the most appropriate crop by considering factors such as soil type, climate, and geographic location. Climate factors, including temperature, humidity, state, and district, are part of the area considerations. To achieve this predictive outcome, algorithms such as Support Vector Machine and Random Forest algorithms are formulated. Subsequently, the optimal algorithm is chosen based on its accuracy and precision parameters.

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Harvesting Growth: Leveraging Random Forests for Advancing Agricultural Productivity with Machine Learning

  • 会议论文

  • 发表日期：

    2025年

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  • G. A. K. S. Rajeev Kumar,
  • Pavan Kumar Vadrevu,
  • Chandra Sekhar Kolli,
  • Ramesh Naidu Goda,
  • B. Ravi Kumar

摘要

Agriculture is a critical sector in our nation, with India ranking as the second-largest producer of rice and wheat globally. The selection of an appropriate crop for cultivation plays a significant role in farming, taking into account soil type and nutrient availability. Crucial factors influencing the growth and health of plants include soil conditions, which vary due to changes in climate, soil pH, and Nigrogen, Phosphorus, Potassium (NPK) values. The cultivation of rice in South India and wheat in North India illustrates how the choice of crop varies by region. Failure to choose a suitable crop can lead to decreased production turnover. The objective is to assist farmers in selecting the most appropriate crop by considering factors such as soil type, climate, and geographic location. Climate factors, including temperature, humidity, state, and district, are part of the area considerations. To achieve this predictive outcome, algorithms such as Support Vector Machine and Random Forest algorithms are formulated. Subsequently, the optimal algorithm is chosen based on its accuracy and precision parameters.