Authors : Dr. A H Sharief; Anumala Malavika; Matta Sailaja; Mekala Vujwal; Kantubuktha Jahnavi; Yarraboina Chaitanya Vamsi

Volume/Issue : Volume 10 - 2025, Issue 3 - March

Google Scholar : https://tinyurl.com/ew4kzjcr

Scribd : https://tinyurl.com/9uyfyrv2

DOI : https://doi.org/10.38124/ijisrt/25mar1972

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The Crop Disease Detection System is an innovative solution that addresses challenges in modern agriculture. With the growing global population and increasing pressure on food production, effective crop disease management is crucial. This system harnesses machine learning and image recognition to help farmers, gardeners, and agricultural professionals accurately diagnose plant diseases. By uploading images of affected crops, users can rely on advanced deep learning algorithms to identify specific diseases and receive tailored recommendations for mitigation. Using a CNN-based approach trained on the PlantVillage dataset with transfer learning, the system automates disease detection, reducing dependence on manual inspection. Designed for real-time deployment, it can be integrated into agricultural advisory platforms, offering scalable support across diverse crop types and environmental conditions.

Keywords : Plant Disease Detection, Deep Learning, CNN, Image Classification, Sustainable Agriculture, Smart Farming.

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