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April 17, 2026Open Access

Crop Sense AI: Data-Driven Crop Recommendation Using ML And Deep Learning

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Authors

MHMrs. K. HarikaRPRowthu Kavyanjali PriyaMVMadeti Vineetha

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Overview

Demonstrates a crop recommendation system using AI techniques, improving yields in agriculture.

Key Points

  • The aim is to develop an intelligent system that recommends suitable crops based on soil and environmental conditions.
  • Implemented machine learning and deep learning algorithms including Decision Tree and Random Forest.
  • Analyzed agricultural parameters such as nitrogen, phosphorus, potassium, rainfall, and soil pH.
  • Trained predictive models using a publicly available agricultural dataset.
  • Evaluated model performance using accuracy, precision, recall, and F1-score.
  • Ensemble and deep learning models achieved high prediction accuracy for crop recommendations.
  • The system provides real-time crop suggestions based on user-inputted soil and environmental parameters.
  • Facilitates precision agriculture by improving crop yield and decision-making for farmers.

Cite This Study

Harika et al. (2026) studied this question.

synapsesocial.com/papers/69e1cf625cdc762e9d85847ehttps://doi.org/10.5281/zenodo.19593021
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Also Consider

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  1. 1Intelligent Crop Recommendation System Using Machine Learning And Deep Learning For Precision Agriculture2026
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  3. 3Predictive Crop Selection Using Data-Driven Machine Learning Models2025
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  5. 5Leveraging nonlinear deep learning models for intelligent crop recommendation in precision agriculture2026