My Work & Portfolio

This project focused on developing an advanced Leaf Disease Detection system using deep learning. The goal was to create a scalable and accurate solution to help farmers identify crop diseases early, enabling timely intervention to prevent significant losses. By leveraging state-of-the-art computer vision and neural network models, the system can analyze leaf images and classify diseases with high precision.

The core of the solution is a Convolutional Neural Network (CNN) trained on a large dataset of leaf images, covering a wide range of common agricultural diseases. The model was optimized for performance to ensure it could be deployed on various platforms, including mobile devices and web applications, making it accessible to farmers in the field.

Challenge

The primary challenge was to build a model that was not only accurate but also robust enough to handle real-world variations in lighting, image quality, and leaf appearance. The dataset required extensive cleaning and augmentation to ensure the model could generalize well to new, unseen images. Additionally, optimizing the model for efficient performance on low-power devices was a key technical hurdle.

• Create a highly accurate and reliable disease classification model.
• Ensure the model performs well under diverse and challenging environmental conditions.
• Develop a user-friendly interface that allows farmers to easily upload images and receive instant diagnoses.

Solution & Result

The final solution is a powerful AI-driven tool that achieved a 95% accuracy rate in disease detection across multiple plant species. The system was deployed as a responsive web application that provides farmers with instant, actionable insights. The project successfully demonstrated the potential of deep learning to revolutionize agriculture by providing an accessible and affordable tool for crop management, ultimately contributing to improved yields and food security.