Crop yield prediction using data analysis and machine learning

• Predict crop yields based on historical data and environmental factors.
• Analyze agricultural data to improve farming decisions.
• Integrate machine learning insights into farming operations.

Captures incoming data through a webhook. Processes data using a splitter to create manageable chunks. Generates embeddings for effective data representation. Stores processed data in a Supabase vector database. Queries the database for insights and predictions. Utilizes memory and chat functionalities for interactive analysis.

1. 1
   Import the Crop Yield Predictor workflow template.
2. 2
   Configure the webhook to receive data submissions.
3. 3
   Set up the Supabase and OpenAI credentials.
4. 4
   Define the Google Sheets document for logging results.
5. 5
   Test the workflow with sample data submissions.