Agrigrade-AI

Inspiration

Inspiration

We wanted to help farmers and local vendors make better decisions about product quality and fair pricing using AI.

What it does

Agrigrade-AI analyzes crop images, grades product quality, and provides market-based price recommendations. It also connects farmers/vendors with potential buyers.

How we built it

We built Agrigrade-AI using React for the frontend, Spring Boot microservices for the backend, Python/FastAPI for AI processing, and Oracle Database for data management.

Challenges we ran into

We faced challenges in AI model training, image quality, integrating multiple services, database connectivity, and connecting the frontend with backend APIs.

Accomplishments that we're proud of

We successfully developed an integrated platform with AI-based quality grading, price recommendation, role-based dashboards, and microservice architecture.

What we learned

We learned about AI model development, React, Spring Boot microservices, REST APIs, database integration, Git/GitHub, and full-stack system integration.

What's next for Agrigrade-AI

We plan to improve AI accuracy, add more crops, provide real-time market price data, improve buyer-farmer matching, and deploy the platform for real-world use.

What it does

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for Agrigrade-AI

Built With

• docker
• fastapi
• java
• jwt
• machine-learning
• microservices
• oracle-database
• python
• react
• rest-api
• spring-boot
• tensorflow
• yolo