AgriSmart(Krishi-Saathi): crop & seed recommendation system

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  Seed Recommendation

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  Crop Recommendation

Inspiration

Agriculture is one of the most important sectors in our country, and many farmers still depend on traditional methods to select crops. Wrong crop selection can lead to low yield and financial loss. This inspired us to develop AgriSmart, a system that helps farmers choose suitable crops and seeds using machine learning and agricultural data.

What it does

AgriSmart recommends suitable crops and seeds based on soil nutrients and environmental conditions. The user provides values such as Nitrogen, Phosphorus, Potassium, temperature, humidity, pH, and rainfall, and the system predicts the best crop for cultivation.

How we built it

We collected agricultural datasets containing soil and weather parameters. After cleaning and preprocessing the data, we trained machine learning models such as Random Forest and Decision Tree. The trained model analyzes user inputs and provides crop recommendations with good accuracy.

Challenges we ran into

• Finding reliable agricultural datasets.
• Handling missing and inconsistent data.
• Selecting the most accurate machine learning model.
• Improving prediction accuracy.
• Understanding the relationship between soil nutrients and crop growth.

Accomplishments that we're proud of

• Developed a machine learning-based recommendation system.
• Successfully trained models using agricultural data.
• Achieved good prediction accuracy.
• Created a project that can help farmers make better decisions.
• Applied classroom concepts to solve a real-world problem.

What we learned

Through this project, we learned about data preprocessing, machine learning algorithms, model training, and performance evaluation. We also gained practical experience in applying technology to agriculture and working as a team to develop a complete project.

What's next for AgriSmart: Crop and Seed Recommendation System

In the future, we plan to integrate real-time weather data, add fertilizer recommendations, develop a mobile application, and support multiple languages so that more farmers can benefit from the system.

Built With

• data-science-toolkit
• machine-learning
• mongodb
• node.js