Abstract :

The Crop Yield Recommendation System Is An Intelligent Agricultural Support System Developed To Help Farmers Improve Crop Production And Make Better Farming Decisions. Agriculture Plays A Major Role In The Economy, And Crop Productivity Depends On Several Environmental And Soil-related Factors. Traditional Farming Methods Often Rely On Manual Experience, Which May Not Always Provide Accurate Crop Recommendations. This Project Uses Machine Learning Techniques To Analyze Agricultural Data And Recommend Suitable Crops Based On Various Parameters. The System Considers Factors Such As Soil Type, Temperature, Humidity, Rainfall, PH Level, And Nutrient Values Including Nitrogen, Phosphorus, And Potassium. By Processing These Inputs, The Model Predicts The Most Suitable Crop For Cultivation In A Particular Region Or Season. The Proposed System Helps Farmers Reduce Crop Failure And Improve Overall Yield Productivity. Various Machine Learning Algorithms Such As Decision Tree, Random Forest, And Support Vector Machine Can Be Used For Accurate Prediction And Analysis. The Dataset Used In This Project Is Collected From Agricultural And Environmental Sources. Data Preprocessing Techniques Are Applied To Clean And Normalize The Dataset Before Training The Model. The System Provides Fast And Reliable Recommendations Through A User-friendly Interface. Farmers Can Easily Enter Soil And Climate Details To Receive Crop Suggestions Instantly. The Project Also Supports Sustainable Farming Practices By Encouraging Proper Crop Selection According To Environmental Conditions. This System Minimizes Unnecessary Resource Usage Such As Water And Fertilizers. The Proposed Solution Improves Decision-making And Increases Agricultural Efficiency. It Can Be Implemented As A Web Application Or Mobile Application For Easy Access. The Crop Yield Recommendation System Demonstrates How Artificial Intelligence And Machine Learning Can Support Modern Smart Farming And Contribute To Agricultural Development.

Published:

22-5-2026

Issue:

Vol. 26 No. 5 (2026)

Page Nos:

1324-1331

Section:

Articles

License:

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

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