Abstract :

Agriculture Remains The Backbone Of Food Security And Economic Stability In Many Countries, Yet It Faces Growing Challenges Such As Climate Variability, Soil Degradation, Pest Outbreaks, And Inefficient Resource Utilization. Traditional Farming Practices Often Rely On Experience-based Decision Making, Which Can Lead To Inconsistent Yields And Excessive Use Of Water, Fertilizers, And Pesticides. In Recent Years, Machine Learning Techniques Have Been Applied To Smart Agriculture To Address These Challenges By Enabling Data-driven Decision Support Systems. However, Most Advanced Models Function As Black Boxes, Offering High Prediction Accuracy But Little Transparency Or Interpretability, Which Limits Farmer Trust And Real-world Adoption. This Research Proposes An XAIpowered Smart Agriculture Framework Integrated With Support Vector Machine (SVM) Models To Enhance Food Productivity While Ensuring Explainability And Trust. The System Leverages Agricultural Datasets Comprising Soil Parameters, Climatic Conditions, Crop Health Indicators, And Historical Yield Data To Predict Optimal Crop Productivity Outcomes. SVM Is Employed Due To Its Robustness, Efficiency With Limited Datasets, And Strong Generalization Capability In High-dimensional Feature Spaces. To Address The Transparency Gap, Explainable Artificial Intelligence (XAI) Techniques Are Incorporated To Provide Clear Explanations Of Model Predictions, Enabling Farmers And Agricultural Experts To Understand Why Specific Recommendations Are Generated. The Proposed Approach Improves Decisionmaking Related To Crop Selection, Irrigation Scheduling, Fertilizer Application, And Pest Management While Maintaining Interpretability. By Combining Predictive Accuracy With Explainability, The System Enhances Farmer Confidence, Promotes Sustainable Farming Practices, Reduces Resource Wastage, And Ultimately Increases Agricultural Productivity. This Study Demonstrates How The Integration Of XAI And SVM Can Transform Smart Agriculture Into A More Transparent, Efficient, And Farmer-centric Solution For Addressing Modern Food Production Challenges.

Published:

01-5-2026

Issue:

Vol. 26 No. 5 (2026)

Page Nos:

13-17

Section:

Articles

License:

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

How to Cite