Agrivision

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Inspiration

We were inspired by the challenges farmers face when identifying crop diseases and plant health problems. Many issues can become serious if they are not detected early. We wanted to create a simple solution where a farmer could upload a crop image and quickly get useful information about its condition.

What it does

AgriVision AI is an AI-powered crop health assistant. Users can upload an image of a crop or plant, and the system analyzes it to identify possible diseases or health problems.

It provides:

• Crop/plant analysis
• Possible disease detection
• Health and severity assessment
• Treatment and prevention suggestions
• AI agriculture chatbot
• Previous scan history
• Simple and responsive interface

How we built it

We built AgriVision AI using React, TypeScript, Tailwind CSS, Node.js, and Express.js. We integrated a vision-capable Gemini AI model to analyze crop images and generate useful agricultural insights.

We focused on creating a clean interface where users can upload an image, receive the AI analysis, and understand the results easily.

Challenges we ran into

One of our main challenges was getting reliable results from different crop images. Image quality, lighting, background, and the visibility of symptoms can affect AI analysis.

Another challenge was presenting AI results in a simple way. Instead of showing only a disease name, we wanted to provide understandable information about severity and possible next steps.

Accomplishments that we're proud of

We are proud of turning an AI image-analysis concept into a working agriculture-focused application. We successfully connected image analysis with an easy-to-use interface and added an AI assistant to make the platform more interactive.

We are also proud that AgriVision AI focuses on a real-world problem and has the potential to be useful beyond a hackathon project.

What we learned

We learned how to integrate multimodal AI into a real-world application and how to design prompts and responses for image-based analysis.

We also learned that building a useful AI product is not just about getting a prediction—it is about presenting the result clearly and giving users information they can actually understand and use.

What's next for AgriVision

We plan to expand AgriVision AI by supporting more crops and diseases, adding regional-language support, weather-based crop insights, location-based recommendations, and better support for users with limited internet connectivity.

Our long-term goal is to make AgriVision AI a practical digital farming assistant that helps farmers detect crop problems earlier and make better-informed decisions.

Built With

• ai
• google
• studio