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This study is about a system that uses machine
learning to predict crops and recommend fertilizers. The system
looks at things like the soil and the environment including how
nitrogen, phosphorus and potassium are in the soil as well as the
temperature, humidity and how much it rains. The people who
made this system used a lot of information from the Kaggle Crop
Recommendation dataset, which has 2200 examples. They tried
out a few ways of making predictions, including Random Forest,
Support Vector Machine and XGBoost. They tested these
methods by trying them out times and seeing how well they
worked. The XGBoost model was the best at making predictions
it got the answer 97.1% of the time. The system is now a web
application that farmers can use to make choices, about their
crops and plan what to plant. The Crop Prediction and
Fertilizer Recommendation System is made to help farmers with
this by using the Crop Prediction and Fertilizer
Recommendation System.
Keywords:
Machine Learning, Random Forest, SVM, XGBoost, Crop Prediction, Streamlit.
Cite Article:
"Intelligent Crop Prediction and Fertilizer Recommendation Using Machine Learning Techniques", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a186-a192, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604027.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator