ISSN No:2250-3676
Scholarly Peer Reviewed and Fully Referred Open Access Multidisciplinary Monthly Research Journal
Automated Cotton Plant Disease Diagnosis Using Image ProcessingID: 1205 Abstract :Cotton Is A Vital Cash Crop In Global Agriculture, But Its Productivity Is Frequently Threatened By Weed Invasion And Plant Diseases. Manual Methods For Identifying Such Issues Are Inefficient And Often Inaccurate. This Paper Introduces An AI-based Classification System Employing Convolutional Neural Networks (CNNs) To Distinguish Between Healthy Cotton Plants, Weeds, And Infected Crops. The Model Demonstrates High Accuracy And Efficiency, Aiming To Assist Farmers In Early Detection And Management. A User-friendly Web Platform Powered By Flask And Integrated With A Chatbot Using Natural Language Processing (NLP) Enables Real-time Image Classification And Agricultural Support. Keywords—Deep Learning, Convolutional Neural Networks, Image Processing, Crop Disease, Artificial Intelligence, Chatbot, Agriculture |
Published:09-6-2025 Issue:Vol. 25 No. 6 (2025) Page Nos:408-412 Section:Articles License:This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. How to Cite1 Mrs. Pethota Swaroopa,2 Basireddy Sreejasri,3 Ginna Vaishnavi,4CH Mahender,5Narla Sai Santosh , Automated Cotton Plant Disease Diagnosis Using Image Processing , 2025, International Journal of Engineering Sciences and Advanced Technology, 25(6), Page 408-412, ISSN No: 2250-3676. |