Abstract :

Agriculture Serves As The Cornerstone Of Global Food Security, Sustaining Billions Of People Across The World.and Driving Economic Growth. However, Plant Diseases May Negatively Affect Crop Production, Leading To Financial Difficulties For Farmers. For These Reasons, Consequently, Crop Yields Are Severely Compromised, Pushing Farmers Into Economic Hardship. Therefore, Identifying Plant Diseases At An Early Stage Is Critical For Effective Crop Management And Enhancing Overall Agricultural Output. In Recent Years, Rapid Progress In Artificial Intelligence And Deep Learning Technologies Has Paved The Way For The Creation Of Automated Image-based Systems Capable Of Recognizing Plant Diseases. The Study Introduces An AI-powered Web-based Application AgriDiagnose That Utilizes CNN To Identify Disease In Potato Plants. The Model’s Results Are Then Returned To The User Through A Simple Web Interface, Along With Recommended Treatments. The Method Demonstrates How Deep Learning Can Benefit Agriculture Through Improved Early Detection Of Disease And Assisting Farmers In Making Better Decisions Regarding Their Crops. Farmers Now Have Access To More Digital Technologies Than Ever Before. This Has Led To The Creation Of Automated Smart Systems That Help Farmers Keep Track Of Their Crops And Provide Help With Diagnosing Problems With Those Crops. Research Is Currently Being Conducted Into The Use Of Pictures Taken Of Plant Leaves Using Modern Smartphones, Which Allow Farmers To Capture Highquality Images Of Their Crops Directly Within Their Fields. Agricultural Support Systems Can Provide Both Real-time Data Analysis And The Ability To Suggest Optimum Choices To Users By Applying Deep Learning Models Together With Web-enabled Applications. By Using These Systems, Manual Verification Steps For Crop Disease Diagnosis Are Reduced, And The Rate At Which Accurate Diagnoses Are Made Increases.

Published:

25-6-2026

Issue:

Vol. 26 No. 6 (2026)

Page Nos:

1494-1497

Section:

Articles

License:

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

How to Cite