Abstract :

Agricultural Productivity Is Highly Influenced By Variations In Soil Characteristics, Climatic Conditions, And Market Dynamics, Making Crop Selection A Challenging Task For Farmers. Traditional Decision-making Methods Often Depend On Personal Experience, Which May Not Always Produce Optimal Outcomes Under Changing Environmental Conditions. This Work Presents An Intelligent Decision Support Framework That Combines Machine Learning And Deep Learning Techniques To Assist Farmers In Selecting Suitable Crops While Estimating Expected Yield And Future Market Prices. The Proposed Framework Processes Agricultural Datasets Containing Soil Nutrients, Seasonal Information, Weather Parameters, And Soil Properties Through Data Cleaning, Feature Preparation, And Training Procedures Before Generating Predictions. A Random Forest Model Is Employed To Identify The Most Appropriate Crops For A Given Set Of Agricultural Conditions, Whereas A Long Short-Term Memory (LSTM) Network Is Utilized To Estimate Future Crop Prices Based On Historical Trends. The Platform Further Integrates User Registration, Secure Authentication, Dataset Management, And An Interactive Recommendation Interface That Allows Users To Obtain Crop Suggestions Using Either Complete Soil Parameters Or Limited Input Values. In Addition, The System Provides Location-based Seed Purchase Information To Help Farmers Identify Nearby Markets Offering The Recommended Crop Varieties. Experimental Observations Indicate That The Proposed Framework Delivers Reliable Recommendations By Integrating Crop Suitability Analysis, Yield Estimation, And Price Forecasting Within A Unified Environment. The Developed Platform Serves As A Practical Decisionsupport Solution That Enables Informed Agricultural Planning, Reduces Resource Wastage, And Supports Sustainable Farming Practices Through Data-driven Recommendations.

Published:

30-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

1342-1348

Section:

Articles

License:

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

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