Copyright © 2023 The Author(s): This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY-NC 4.0) which permits unrestricted use, distribution, and reproduction in any medium for non-commercial use provided the original author and source are credited. International Journal of Scientific Research in Computer Science, Engineering and Information Technology ISSN : 2456-3307 Available Online at :www.ijsrcseit.com doi : https://doi.org/10.32628/IJSRCSEIT 200 Development of an Automated Crop Disease Detection System Prof. Y. L. Tonape, Rupali Bhujbal, Sarang Kale, Vishal Patil, Renuka Savale Department of B.E. Computer, SBPCOE, Indapur, Maharashtra, India A R T I C L E I N F O A B S T R A C T Article History: Accepted: 10Oct 2023 Published: 30 Oct 2023 Crop diseases pose a significant threat to global food security, necessitating innovative approaches for early detection and intervention. This abstract presents an application that uses advanced machine learning algorithms to accurately identify and monitor diseases in crops. This imagery captures various spectral signatures, which are subsequently processed and analyzed to detect anomalies indicative of crop diseases. Image segmentation techniques are employed to separate healthy and diseased areas within the images, allowing for precise disease mapping. Machine learning plays a pivotal role in such applications by enabling automated disease recognition. Supervised learning models, such as Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), are trained on labeled datasets containing a wide range of crop disease instances. These models learn to distinguish between healthy and infected crops based on the extracted features and spectral signatures, achieving high accuracy and minimizing false positives. Real-time monitoring is a core feature, enabling farmers and agricultural stakeholders to receive timely disease alerts. The system's user-friendly interface, accessible through web applications, provides actionable insights and recommendations for targeted interventions. This empowers farmers with the information needed to implement precision agriculture practices and adopt integrated pest management strategies, optimizing crop yields while minimizing the use of pesticides. By offering early disease detection and predictive modeling capabilities, the system supports sustainable and resilient agriculture, contributing to global food security efforts. Keyword : Crop Diseases, Machine Learning, Image Processing, Crop Management, Data Analysis, User Interface Publication Issue Volume 9, Issue 10 September-October-2023 Page Number 200-210