Abstract
The Cotton Disease Prediction System represents a groundbreaking approach in agricultural management, addressing the persistent threats posed by diseases to this pivotal global cash crop. With the imperative need for timely disease detection to safeguard yield and quality, this research introduces a novel methodology leveraging transfer learning with Convolutional Neural Networks (CNNs). By analyzing high-resolution images of cotton leaves, the system utilizes a user-friendly web interface developed using HTML and CSS in Visual Studio Code. The backend functionality, orchestrated through Jupyter Notebook, enables real-time processing and predictions. Through rigorous testing, the system demonstrates remarkable robustness and accuracy, offering farmers a proactive tool for disease management. By seamlessly integrating frontend and backend components, this system not only enhances efficiency but also empowers farmers with actionable insights, heralding a new era in precision agriculture.
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