AI-Powered Crop Recommendation System

• 

  Chickpea Recommendation

Inspiration

Agriculture is the backbone of many economies, yet farmers struggle with choosing the right crops. Inspired by the need for data-driven farming, we built an AI-powered Crop Recommendation System to help maximize yield, reduce waste, and promote sustainability.

What it does

Our system analyzes soil parameters like Nitrogen, Phosphorus, Potassium, pH, temperature, humidity, and rainfall to recommend the most suitable crops. Farmers input their soil data and receive instant AI-driven suggestions for better decision-making.

Built With

Python, scikit-learn, Streamlit, Random Forest Classifier, Streamlit Cloud We used a Crop Recommendation Dataset and trained a Random Forest Classifier for accurate predictions. The backend is built in Python with scikit-learn, and the frontend is a user-friendly Streamlit web app. The model is deployed for real-world use on Streamlit Cloud.

Challenges we ran into

Data preprocessing, model tuning, and UI design were key challenges. Optimizing accuracy, ensuring usability for non-technical users, and smooth deployment were critical hurdles we overcame.

Accomplishments that we're proud of

Achieved over 90% accuracy, built a fully functional web app, and created a scalable solution that can integrate real-time weather data and fertilizer recommendations.

What we learned

Data-driven agriculture improves food security and sustainability. We gained expertise in AI model building, deployment, and user-friendly tech solutions for farmers.

What's next for AI-Powered Crop Recommendation System

We plan to integrate real-time weather updates, offer fertilizer and pest management suggestions, develop a mobile app, add multilingual support, and create a farmer community for shared insights.

🚀 With AI-powered insights, we aim to revolutionize agriculture for a smarter, more sustainable future! 🌿

Built With

• python
• random-forest-classifier
• scikit-learn