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April 13, 2026Open Access

Ai-Driven Crop Recommendation System

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Authors

MRM Uday Kanth ReddyMKModem KalpanaGUGowni Ushasree

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Overview

Demonstrates an AI crop recommendation system that enhances productivity in smallholder farming, suggesting improved agricultural outcomes.

Key Points

  • The research aims to create an AI-driven system that assists smallholder farmers in making informed crop choices based on environmental data.
  • Developed an AI-based crop recommendation system using machine learning techniques.
  • Analyzed soil and climate factors such as temperature, humidity, rainfall, soil pH, nitrogen, phosphorus, and potassium.
  • Trained models like Random Forest, Decision Tree, and SVM on a prepared agricultural dataset.
  • Assessed model performance using accuracy and confusion matrix analysis.
  • Designed a user-friendly interface for farmers to receive crop recommendations based on input values.
  • The Random Forest model outperformed other models in accuracy and predictive performance.
  • Farmers using the system received tailored crop recommendations based on their specific soil and climate conditions.
  • The system promotes sustainable farming practices and increased productivity.

Cite This Study

Reddy et al. (2026) studied this question.

synapsesocial.com/papers/69dc89183afacbeac03eacf3https://doi.org/10.5281/zenodo.19511053
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  1. 1AI-based Smart Crop Recommendation System for Sustainable Agricultural Production: A Data-driven Approach to Minimize Resource Use and Maximize Yield2025
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  3. 3Crop Sense AI: Data-Driven Crop Recommendation Using ML And Deep Learning2026
  4. 4Predictive Crop Selection Using Data-Driven Machine Learning Models2025
  5. 5Intelligent Crop Recommendation System Using Machine Learning And Deep Learning For Precision Agriculture2026