Here are some key points for your Crop Prediction System that detects temperature, humidity, pH, and other factors:
Objective: Develop a system to predict optimal crop planting and harvesting times based on environmental factors.
Components: Sensors for temperature, humidity, pH, soil moisture, and other relevant factors; data storage and processing unit; machine learning algorithms.
Functionality:
1. Data Collection: Use sensors to continuously monitor temperature, humidity, pH, soil moisture, and other environmental factors.
2. Data Analysis: Process and analyze the collected data to identify patterns and correlations.
3. Prediction Model: Implement machine learning algorithms to predict crop growth patterns and optimal planting/harvesting times.
4. User Interface: Provide a user-friendly interface for farmers to access predictions and recommendations.
Technology Stack: Sensors, data storage solutions, machine learning frameworks (e.g., TensorFlow, scikit-learn), and a user interface platform.
Benefits: Improved crop yields, optimized resource use, and better decision-making for farmers.
