Abstract :

Agriculture Plays A Crucial Role In The Economic Development Of Countries Like India, Where A Significant Portion Of The Population Depends On Farming For Their Livelihood. However, Selecting The Appropriate Crop Based On Soil Nutrients And Environmental Conditions Remains A Major Challenge Due To Variations In Soil Composition, Climate, And Rainfall Patterns. To Address This Issue, This Project Proposes A Crop Recommendation System That Leverages Machine Learning Techniques To Assist Farmers In Making Informed Decisions. The System Utilizes Key Agricultural Parameters Such As Nitrogen (N), Phosphorus (P), Potassium (K), Temperature, Humidity, Soil PH, And Rainfall To Recommend The Most Suitable Crop For Cultivation. A K-Nearest Neighbors (KNN) Algorithm Is Employed To Analyze The Dataset, Which Includes Comprehensive Information On Soil Nutrients And Environmental Conditions. Feature Scaling Using StandardScaler Is Applied To Improve Model Performance And Accuracy. The Developed Model Is Integrated Into A User-friendly Streamlit Web Application, Enabling Users To Input Relevant Data Through An Interactive Interface And Receive Realtime Crop Recommendations. The Trained Model Is Serialized Using Pickle For Efficient Deployment And Reuse. This System Aims To Enhance Agricultural Productivity, Support Sustainable Farming Practices, And Empower Farmers With Data-driven Insights.

Published:

06-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1265-1273

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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