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An agriculture is a fundamental component of India’s economic growth and employment, with more than 1.78 million square kilometers of arable land under active cultivation. Despite being a global leader in agricultural production, India’s farm productivity remains below optimal levels due to limited land resources and suboptimal crop selection. This research explores the potential of a machine learning-driven crop recommendation system to address these challenges. By analyzing various soil properties, climate conditions, and other influential factors, this system aims to provide data-driven crop selection advice customized to specific environmental conditions. Utilizing machine learning, deep learning, and ensemble learning algorithms, this research conducts a comprehensive review of existing techniques to assess their effectiveness and identify suitable models for a robust recommendation system. The goal is to enhance agricultural productivity through optimized crop selection, thereby supporting Indian farmers in making informed cultivation decisions that align with local conditions.

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Crop Recommendation Systems: Insights, Trends, and Methodological Approaches

  • 会议论文

  • 发表日期：

    2025年

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  • Hetvi Desai,
  • Hardikkumar Jayswal,
  • Nilesh Dubey,
  • Dipika Damodar,
  • Ashwin Makwana,
  • Jitendra Chaudhari,
  • Amit Nayak

摘要

An agriculture is a fundamental component of India’s economic growth and employment, with more than 1.78 million square kilometers of arable land under active cultivation. Despite being a global leader in agricultural production, India’s farm productivity remains below optimal levels due to limited land resources and suboptimal crop selection. This research explores the potential of a machine learning-driven crop recommendation system to address these challenges. By analyzing various soil properties, climate conditions, and other influential factors, this system aims to provide data-driven crop selection advice customized to specific environmental conditions. Utilizing machine learning, deep learning, and ensemble learning algorithms, this research conducts a comprehensive review of existing techniques to assess their effectiveness and identify suitable models for a robust recommendation system. The goal is to enhance agricultural productivity through optimized crop selection, thereby supporting Indian farmers in making informed cultivation decisions that align with local conditions.