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The goal of this chapter is to create a machine learning-based system for seasonal pricing patterns and dynamic pricing (Deksnyte and Lydeka, Int J Bus Soc Sci 3(23), 2012) of vegetables in wholesale markets across the country. Using machine learning algorithms, the system optimizes prices by taking advantage of quantity, seasonal, and regional aspects. Regression analysis and tree-based machine learning techniques are used by the system to create customized predictions for various crops, analyze historical data, and use models for price forecasting (Roland et al., Possible methods for price forecasting, 2016). Planning and profit-maximizing techniques work together to maximize resources, cut waste, and promote sustainable farming methods

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AgriPredict: Machine Learning-Based Agricultural Commodity Price Prediction

  • 会议论文

  • 发表日期：

    2025年

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  • Ashwani Kumar,
  • Prathamesh Kusalkar,
  • Sanket Naitam,
  • Harsh Pande,
  • S. N. Ghotkar,
  • S. S. Kumbhar,
  • Shanu Kumar,
  • Sanket Jiwane

摘要

The goal of this chapter is to create a machine learning-based system for seasonal pricing patterns and dynamic pricing (Deksnyte and Lydeka, Int J Bus Soc Sci 3(23), 2012) of vegetables in wholesale markets across the country. Using machine learning algorithms, the system optimizes prices by taking advantage of quantity, seasonal, and regional aspects. Regression analysis and tree-based machine learning techniques are used by the system to create customized predictions for various crops, analyze historical data, and use models for price forecasting (Roland et al., Possible methods for price forecasting, 2016). Planning and profit-maximizing techniques work together to maximize resources, cut waste, and promote sustainable farming methods