Abstract :

Agriculture Significantly Contributes To Food Production And The Economy, But Crop Diseases Can Lead To Severe Yield And Quality Losses If Not Detected Early. Traditional Manual Inspection Methods Are Time-consuming, Costly, And Prone To Inaccuracies. This Paper Presents An AI-Driven Crop Disease Prediction And Management System Leveraging Deep Learning And Convolutional Neural Networks (CNN) To Automate Disease Detection From Crop Leaf Images. The System Is Trained On The Kaggle Crop Disease Dataset And Deployed Via A User-friendly Web Application Developed Using Python, Flask, HTML, And CSS. Users Upload Leaf Images, Which The System Analyzes To Accurately Predict Diseases. It Also Provides Detailed Information On Disease Symptoms, Treatment Options, And Preventive Measures, Empowering Farmers To Manage Crop Health Efficiently. This Solution Offers A Cost-effective, Scalable Tool For Early Disease Detection, Enhancing Agricultural Productivity And Supporting Smart Farming Practices Through AI-driven Insights.

Published:

29-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

1428-1435

Section:

Articles

License:

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

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