Abstract :

Agricultural Productivity Is Influenced By A Complex Combination Of Soil Characteristics, Climatic Conditions, Environmental Variations, And Cultivation Practices, Making Accurate Agricultural Decision-making A Challenging Task. Conventional Farming Approaches Often Depend On Historical Information, Manual Observations, And Generalized Recommendations, Which May Not Adequately Reflect Changing Field Conditions. This Paper Presents An Adaptive Learning-Based System For Precision Crop Yield Forecasting Using Data Intelligence, An Intelligent Agricultural Decision-support Framework That Integrates Machine Learning And Data Analytics To Support Crop Selection, Yield Forecasting, And Fertilizer Management. The Proposed System Processes Agricultural Parameters Including Nitrogen, Phosphorus, Potassium, Soil PH, Temperature, Humidity, Rainfall, And Historical Agricultural Information To Generate Field-oriented Recommendations And Predictions. A Machine-learning-based Crop Recommendation Module Identifies Suitable Crops, While The Crop Yield Prediction Module Estimates Expected Agricultural Production. A Fertilizer Recommendation Module Analyzes Nutrient Requirements And Soil Conditions To Support Appropriate Fertilizer Selection. The Framework Further Incorporates An Adaptive Learning Mechanism Involving Continuous Data Collection, Model Retraining, And Performance Evaluation, Enabling The System To Accommodate Newly Available Agricultural Information. The Implementation Uses Python And The Django Framework, With Random Forest Serving As A Primary Machine-learning Approach. Experimental Results Reported For The Developed System Demonstrate High Predictive Performance For Crop Recommendation And Effective Fertilizer Recommendation, While The Integrated Framework Provides A Unified Interface For Agricultural Decision Support. The Proposed Approach Aims To Improve Cultivation Planning, Resource Utilization, Productivity, And Sustainable Farming By Providing Data-driven Recommendations That Can Adapt To Changing Agricultural Conditions.

Published:

17-8-2026

Issue:

Vol. 26 No. 8 (2026)

Page Nos:

937-955

Section:

Articles

License:

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

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