Build a Crop Yield Predictor with n8n & LangChain

In this guide, you will learn how to design a scalable and explainable crop yield prediction workflow using n8n, LangChain, Supabase as a vector store, Hugging Face embeddings, and Google Sheets. The article walks through the end-to-end architecture, key n8n nodes, configuration recommendations, and automation best practices for agricultural prediction and logging.

Use case overview: automated crop yield prediction

Modern agricultural operations generate large volumes of data, from soil sensors and weather feeds to field notes and historical yield records. Turning this data into consistent, auditable yield predictions requires a repeatable pipeline that can ingest, enrich, and reason over both structured and unstructured information.

By combining n8n for workflow orchestration with LangChain for LLM-based reasoning, you can implement a crop yield predictor that:

• Automates the ingestion of field data from webhooks or CSV exports
• Transforms notes and telemetry into embeddings using Hugging Face models
• Stores contextual vectors in Supabase for semantic retrieval
• Uses a LangChain agent to generate yield predictions with explanations
• Logs outputs into Google Sheets for traceability and downstream analytics

The result is a robust, explainable prediction pipeline that can be extended, audited, and integrated with broader agritech workflows.

Solution architecture

The n8n workflow for this crop yield predictor is built around a sequence of specialized nodes and external services that work together to ingest, index, retrieve, and reason over data.

Core building blocks

• Webhook – Ingests field data, telemetry, or batch payloads via HTTP POST.
• Text Splitter – Splits long text into manageable chunks for embedding.
• Embeddings (Hugging Face) – Converts text chunks into numerical vector representations.
• Vector Store (Supabase) – Persists embeddings and metadata for later retrieval.
• Query & Tool – Performs semantic search on the vector store and exposes it as a tool to the agent.
• Memory & Agent (LangChain / OpenAI) – Uses context, tools, and conversation memory to generate predictions.
• Google Sheets – Records predictions, explanations, and metadata for monitoring and auditing.

This architecture is modular, so you can later swap components such as the embedding model or LLM without redesigning the entire pipeline.

Detailed workflow in n8n

1. Webhook: ingesting field data

The entry point to the system is an n8n Webhook node configured to accept HTTP POST requests. It should receive structured JSON data that captures all relevant agronomic context, for example:

• field_id
• soil_moisture
• rainfall_past_30d
• temperature_avg
• planting_date
• variety
• historical_yields (optional)
• notes (free-text observations)

This webhook can be connected to sensor platforms, mobile data collection apps, or scheduled exports from farm management systems. Standardizing the payload structure at this stage greatly simplifies downstream automation.

2. Text preparation and splitting

Many field reports contain unstructured notes, observations, or historical comments. Before generating embeddings, the workflow uses a Text Splitter node to segment these long texts into smaller chunks.

Recommended configuration:

• Type: character-based splitter
• chunkSize: typically 350-500 characters
• chunkOverlap: typically 30-80 characters

These ranges help preserve local context while avoiding overly long sequences that can degrade embedding quality. For numeric or structured telemetry, you can convert values into short labeled sentences (for example, “Average soil moisture is 18 percent”) before splitting, which often improves semantic representation.

3. Generating embeddings with Hugging Face

Once the text is split, an Embeddings node configured with a Hugging Face model generates vector embeddings for each chunk. Hugging Face provides a wide range of models suitable for general semantic tasks and domain-specific contexts.

Best practices:

• Store the Hugging Face API key in n8n credentials, not inline in the node.
• Evaluate different embedding models if you require higher domain sensitivity.
• Balance latency and accuracy by choosing smaller models for high-throughput ingestion and larger models for more precise semantic understanding.

4. Persisting vectors in Supabase

The resulting embeddings are written to a Supabase vector table using a Vector Store integration. Configure the table and index for this use case, for example:

• indexName: crop_yield_predictor

Alongside each embedding, store rich metadata such as:

• field_id
• timestamp
• season
• crop_type
• geolocation
• source (for example, “sensor”, “manual_note”)

This metadata enables filtered semantic queries, such as restricting retrieval to a specific field, season, or geographic region. It also improves traceability and supports more targeted predictions.

5. Query & Tool: semantic retrieval for predictions

When a new prediction is requested, the workflow issues a semantic search against the Supabase vector store. In n8n, this is typically modeled as a Query node whose output is wrapped as a tool for the LangChain agent.

Configuration recommendations:

• top_k: for example, 5 closest vectors
• Return similarity scores alongside the text chunks
• Apply metadata filters, such as metadata.field_id, when available

The retrieved chunks provide the agent with relevant historical notes, comparable conditions, and recent telemetry. Similarity scores can be used by the agent to weigh evidence when forming the final yield estimate.

6. Memory and LangChain agent orchestration

The reasoning layer is implemented through a LangChain Agent node integrated with a large language model such as OpenAI Chat. The agent is configured with:

• The LLM model to use for prediction
• The vector store query as a tool
• A memory buffer that retains a sliding window of recent interactions

A typical memory configuration is a sliding window that stores the last 5 interactions. This allows the agent to maintain context across multiple requests for the same field or during iterative analysis.

Prompt engineering and agent behavior

Designing the prediction prompt

The agent prompt should clearly instruct the model on how to use retrieved evidence, how to combine numeric telemetry with textual notes, and how to format its output. A conceptual example:

You are an agronomy assistant. Based on the retrieved field notes and telemetry, provide a predicted yield (tons/ha), a confidence score (0-100%), and 2 concise recommendations to improve yield. Cite the most relevant evidence snippets.

Key design guidelines:

• Ask for a point estimate and a confidence score to make outputs easier to compare over time.
• Require short, actionable recommendations instead of generic advice.
• Explicitly request citations or references to retrieved snippets to keep the model grounded in data.

Example n8n parameters

For a starting configuration, the following settings are commonly effective:

• Text Splitter: chunkSize=400, chunkOverlap=40
• Embeddings node: a compatible Hugging Face embedding model set via n8n credentials
• Supabase Insert: indexName=crop_yield_predictor
• Query: top_k=5, filter by metadata.field_id where applicable
• Memory: sliding window buffer of the last 5 interactions

Logging and observability with Google Sheets

To ensure traceability and support evaluation, the final step in the workflow appends predictions to a Google Sheets document. Each row can include:

• field_id
• predicted_yield
• confidence
• notes or explanation from the model
• timestamp
• Links or identifiers for the underlying source vectors or records

This sheet serves as an audit log and a simple analytics layer, enabling quick performance checks and downstream integration with BI tools or additional workflows.

Implementation best practices

Credential management and security

• Store Hugging Face, Supabase, and OpenAI keys in n8n credentials rather than hard-coding them in nodes.
• Use separate credentials for development and production environments.
• Apply the principle of least privilege when configuring API keys and database access.

Metadata and indexing strategy

Careful metadata design significantly improves the usefulness of your vector store. Consider indexing:

• Season and crop type
• Field or farm identifiers
• Geolocation or region
• Data source and quality indicators

This enables more precise retrieval, for example querying only similar fields in the same climate zone or variety when generating a prediction.

Retrieval configuration

• Start with top_k=5 and adjust based on observed model performance.
• Inspect similarity scores and retrieved snippets during early testing to ensure relevance.
• Refine filters and metadata if the agent frequently receives irrelevant or noisy context.

Monitoring, evaluation, and iteration

To ensure the crop yield predictor improves over time, use the Google Sheets log to compare predicted yields with actual outcomes. You can compute metrics such as:

• Mean Absolute Error (MAE)
• Root Mean Squared Error (RMSE)

Based on these metrics, iterate on the following aspects:

• Prompt design and output format
• Chunking strategy in the Text Splitter
• Choice of embedding model and LLM
• Metadata filters and retrieval parameters

The agent’s cited evidence is particularly useful for diagnosing where the model is relying on incomplete, outdated, or misleading data.

Security, privacy, and compliance considerations

Farm and field data may be subject to privacy or data residency requirements. When using Supabase and external LLM providers:

• Leverage Supabase features such as row-level security and encrypted storage.
• Restrict access to vector tables via scoped API keys.
• Mask or remove personally identifiable information before generating embeddings when required.
• Review provider terms for data retention and model training on your inputs.

Design your workflow so that sensitive attributes are either excluded from embeddings or handled using anonymization techniques where appropriate.

Scaling and cost optimization

Both embedding generation and LLM calls contribute to operational costs. To scale efficiently:

• Batch webhook payloads for scheduled embedding jobs instead of embedding each record individually in real time when latency is not critical.
• Cache embeddings for documents that do not change to avoid reprocessing.
• Use smaller embedding and LLM models for bulk preprocessing, reserving larger models for high-value or final predictions.

Monitoring request volumes and response times will help you tune the balance between performance, accuracy, and cost.

End-to-end value and extensibility

With this n8n and LangChain workflow, you obtain a reproducible pipeline for crop yield prediction that is:

• Explainable – predictions are backed by retrieved context and logged explanations.
• Searchable – Supabase vector storage keeps historical knowledge accessible for future queries.
• Auditable – Google Sheets provides a human-readable record aligned with machine reasoning.

From here, you can extend the solution by:

• Adding dashboards for agronomy teams
• Triggering alerts via SMS or email when predicted yields fall below thresholds
• Integrating predictions with irrigation scheduling, input ordering, or other operational systems

Next steps

Deploy this crop yield prediction workflow in your n8n instance, configure secure credentials, and start logging predictions in Google Sheets. As you collect more data, refine prompts, models, and retrieval strategies to improve accuracy and reliability. If you need to adapt the workflow to your specific data sources or agronomic practices, treat this implementation as a reference architecture that can be customized to your environment.