AI-Enhanced Crop Yield Prediction Workflow

This n8n workflow automates the process of predicting crop yields using AI models and data storage solutions. It begins with a webhook trigger that receives input data, which is then split into manageable parts using a character-based text splitter. These parts are embedded into vector representations via Hugging Face’s embedding models and stored in a Supabase vector database for efficient retrieval.

When a prediction is needed, the workflow queries the stored embeddings from Supabase, processes the data through an AI-powered chat model, and leverages an agent to generate insights or predictions based on the input data. The results, along with relevant logs, are then saved into Google Sheets for record-keeping and further analysis.

This setup is useful for agricultural organizations or research institutions aiming to automate crop yield forecasting by integrating AI, data storage, and collaboration tools. It provides a scalable, data-driven approach to improve decision-making and resource planning.