Agnovis Ai

Inspiration 🌱

Farmers work tirelessly to feed the world, yet crop diseases and pest attacks can destroy months of hard work. We built Agnovis AI to bring the power of AI to agriculture and help farmers protect their crops before it's too late.

What it does

Agnovis AI uses AI-powered image analysis to detect crop diseases and identify pests. It also aims to support farmers with crop-specific guidance, management recommendations, weather information, and crop health tracking.

How we built it

We used Python, FastAPI, React, TypeScript, and SQLite, integrating a MobileNetV3 model for disease classification and a YOLO model for pest detection. We connected the AI models to a user-friendly interface designed to make crop diagnosis more accessible.

Challenges we ran into

Integrating different AI models, managing crop-specific classifications, ensuring reliable predictions, and connecting the frontend with the backend were some of our biggest challenges.

Accomplishments that we're proud of

We are proud to turn an agricultural problem into a working AI-powered solution. Bringing disease detection, pest identification, and crop health support together in one platform is a meaningful step toward smarter farming.

What we learned

We learned how to integrate deep learning models into a full-stack application, build APIs, test model integration, and solve real-world problems through technology. Most importantly, we learned that technology matters most when it helps people.

What's next for Agnovis AI 🚀

We aim to improve prediction reliability, expand crop and disease coverage, strengthen regional-language support, and provide more personalized guidance. Our long-term vision is to make reliable agricultural AI accessible to every farmer, regardless of location or technical knowledge.