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Accurately predicting crop production is a challenging and intricate procedure that considers several factors, such as soil properties, climate, and geographic location. To get an accurate crop yield estimate, it is essential to find the relationships between these variables and agricultural productivity utilizing large datasets and sophisticated algorithms. In this study, we propose to estimate agricultural productivity and provide crop recommendations using machine learning models, namely Random Forest and Decision Tree algorithms. These models limit the amount of nutrients that crop rotation loses to the soil by accounting for several factors such as area, temperature, rainfall, and more. This has allowed farmers to cultivate and choose crops with competence. By considering several variables, including annual temperature, rainfall, soil type, and composition, our suggested crop suggestion system provides farmers with tailored recommendations for the optimal crop selection and yield.

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Farming in the Digital Age: A Machine Learning Enhanced Crop Yield Prediction and Recommendation System

  • 会议论文

  • 发表日期：

    2025年

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  • Arti Sonawane,
  • Akanksha Ranade,
  • Apurva Kolte,
  • Siddharth Daundkar,
  • Shreyas Rajage

摘要

Accurately predicting crop production is a challenging and intricate procedure that considers several factors, such as soil properties, climate, and geographic location. To get an accurate crop yield estimate, it is essential to find the relationships between these variables and agricultural productivity utilizing large datasets and sophisticated algorithms. In this study, we propose to estimate agricultural productivity and provide crop recommendations using machine learning models, namely Random Forest and Decision Tree algorithms. These models limit the amount of nutrients that crop rotation loses to the soil by accounting for several factors such as area, temperature, rainfall, and more. This has allowed farmers to cultivate and choose crops with competence. By considering several variables, including annual temperature, rainfall, soil type, and composition, our suggested crop suggestion system provides farmers with tailored recommendations for the optimal crop selection and yield.