This n8n workflow is designed to automate the analysis of drone-captured crop images to detect crop health issues, store the processed data, and log the results for future reference.
The process begins with a webhook trigger, allowing users to send in drone images or related data via an HTTP POST request. The data flows into a splitter node, which divides the input content into manageable chunks suitable for processing.
Each chunk is then transformed into embeddings using OpenAI’s models, capturing the semantic features of the images or text data. These embeddings are stored in a Supabase vector database for efficient similarity searches.
When a query is needed, the workflow retrieves relevant data from the vector store and processes it through an AI language model (e.g., Anthropic), which applies contextual understanding or analysis based on the embedded data. An agent analyzes the information by referencing the stored data and generates insights or summaries.
Finally, the workflow logs all results into a Google Sheets document, providing a structured record of the analysis for monitoring crop health conditions.
This automation is particularly useful for agriculture professionals and researchers who want to streamline crop health diagnostics using drone imagery, leveraging AI for semantic analysis, and maintaining detailed logs for ongoing crop management and decision-making.
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