plant_Disease_Detection

homepage
features
disease detaction and its treatments
contact
our visionaries

Project Title: KISANMITRA: Plant Disease Detection with AI

About the Project:

Plants play a crucial role in supplying food globally.Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process due to the fact that symptoms are not always apparent, either through visual inspection or computer analysis. It can be an unreliable method of identifying and preventing the spread of plant diseases. Emergence of accurate techniques in the field of leaf based image classification has shown impressive results. Our model uses ML algorithm for identifying between healthy and diseased leaf. It gives us a clear way to detect the disease present in plants in a colossal scale.

Our project aims to revolutionize agriculture by leveraging cutting-edge technologies to detect infectious diseases in plants at an early stage through image analysis of their leaves. By harnessing the power of Artificial Intelligence (AI), Machine Learning (ML), and Image Processing (IP), we've developed an innovative algorithm that offers a prompt and accurate solution for disease identification in plants.

Key Features:

Early Detection: Our system enables early identification of infectious diseases in plants, empowering farmers to take timely actions to manage and contain these diseases before they cause extensive damage.
Reduction of Pesticide Use:* By accurately diagnosing plant diseases, we reduce the need for indiscriminate pesticide use. This contributes to a significant decrease in the environmental footprint of agriculture and promotes sustainable farming practices.
Inclusivity: We're committed to leveling the playing field for smallholder farmers by providing them with a user-friendly and cost-effective tool for diagnosing and managing plant diseases. This technology enhances food security and empowers vulnerable communities in the agriculture sector.

Tech Stack:

Frontend: We've built our user interface using React.js, CSS, and Material-UI (MUI) for a responsive and intuitive user experience.
Backend: Our backend utilizes data augmentation techniques, Convolutional Neural Networks (CNN), TensorFlow, and TensorFlow Datasets to process and analyze plant leaf images effectively. We've also integrated Postman and FastAPI to ensure seamless communication and data handling.
Authentication: We've implemented Auth0 for a secure and user-friendly login interface, enhancing the overall user experience.

Event Sponsors:

We would like to express our gratitude to GitHub and Postman, our event sponsors, for their support in making this project possible. Their contributions have been invaluable to our success.