Crop Recommendation System

Data-Driven Crop Recommendation System Using IoT Sensors and Streamlit Dashboard

Challenge

Farmers often make crop selection decisions based on experience, intuition, or historical patterns. However, changing weather conditions, nutrient imbalances, and unpredictable soil moisture make traditional methods unreliable. Without data-backed guidance, farmers risk choosing crops unsuited for current land and climate conditions—leading to reduced yield, wasted resources, and financial loss. A simple, real-time decision-support tool was needed to help farmers identify the most suitable crop for their farm conditions.

Our Solution

We developed a crop recommendation prototype that integrates IoT-based sensing with a clean, interactive Streamlit dashboard. The system collects real-time environmental and soil data and uses it to recommend the optimal crop to plant.

Key elements included:

• NPK Nutrient Sensors

  Sensors measured nitrogen, phosphorus, and potassium levels to understand soil fertility and nutrient composition.

• Soil Moisture & Temperature Monitoring

  Continuous measurement of moisture levels and ambient temperature ensured an accurate assessment of crop compatibility.

• Rainfall & Weather Inputs

  Field rainfall data was incorporated to align recommendations with local seasonal conditions.

• Machine Learning Crop Recommendation Model

  A trained model analyzed all collected variables—NPK, soil moisture, rainfall, and temperature—to output the most suitable crop options.

• Streamlit Dashboard for Visualization

  A user-friendly interface displayed sensor data trends, field conditions, and recommended crops.

  The dashboard allowed farmers or researchers to view insights instantly and understand why a certain crop was suggested.

This prototype brought together IoT sensing, data analytics, and smart recommendations into a single, accessible tool.

Impact

The system demonstrated how real-time data can transform crop planning decisions. Farmers can choose crops that match their soil and weather conditions, optimizing yield potential while reducing the risk of crop failure. The dashboard made the insights easy to interpret, bridging the gap between raw data and practical choices.

This approach also supports sustainable farming—encouraging crop diversity, proper nutrient management, and efficient water use.

Outcome

The project successfully produced a working prototype capable of real-time field data collection and intelligent crop recommendation. It showcased the potential for scaling into a smartphone app or a full digital farming assistant. As an independent initiative, it highlighted how modern analytics and IoT can empower farmers with precise, science-backed decision-making tools.