AI Copilot for greenhouse operations
- #Agritech
- #Generative AI
- #Greenhouse
- #Large Language Models
About the Client
GrowerAdviser is an agritech company that uses AI to automate greenhouse operations. The company works with commercial greenhouses worldwide, helping optimize microclimate control, crop growth, and operational efficiency.
Business Challenge
Growers manage dozens of variables daily: temperature, humidity, CO₂ levels, crop growth stage, and external weather conditions. These variables generate high-frequency data (every 5 minutes from multiple sensors), generating massive time-series datasets.
Even experienced growers face challenges:
- interpreting large volumes of numeric data quickly
- factoring in historical and forecasted trends
- making confident, timely climate decisions
Most existing systems rely heavily on manual analysis, which leads to:
- errors and reactive rather than proactive decisions
- team overload during high-growth periods
- suboptimal climate control, affecting yield and plant health
Inaccurate or delayed actions can reduce productivity, increase crop loss, and lead to financial losses. The goal was to develop a solution that simplifies climate control, stabilizes conditions, and improves greenhouse productivity.
Solution Overview
To assist growers in making better climate decisions, Quantum developed a GenAI Copilot that acts as an intelligent assistant within the Grower Adviser platform. Built using large language models (LLM), the solution was designed to process both structured sensor data and unstructured agronomic knowledge.
The Copilot consolidates inputs from greenhouse sensors, weather forecasts, actual crop state, and domain-specific growing guidelines. Time-series data is preprocessed and summarized to reduce complexity while preserving critical patterns. Visual information helps assess crop development and health status, which further informs the AI’s recommendations.
Unlike generic chatbots, this assistant was purpose-built for growers. It delivers clear, actionable suggestions in natural language, tailored to real-time conditions inside the greenhouse.
By combining advanced AI with domain context, the Copilot helps translate overwhelming data into meaningful actions, making climate management more efficient and adaptive.
Implementation
- Built with LangChain and a Domain-Tuned LLM
Developed the Copilot using LangFuse and integrated it with a custom Retrieval-Augmented Generation (RAG) pipeline, combining a general-purpose LLM with a domain-specific greenhouse knowledge base. - Integrated with Climate Computers via API
Connected the agent to commercial greenhouse climate systems (e.g., Priva, Hoogendoorn) using available REST APIs, enabling real-time access to sensor data such as temperature, humidity, and radiation. - Embedded Grower Strategy Engine
Designed a strategy injection module where growers define their crop plan, energy profile, and local knowledge. This guides AI reasoning and ensures decisions are aligned with human intent, not automated rules. - Crop Monitoring via Edge Cameras or Autonomous Drones
Implemented a visual monitoring module using edge-based cameras and optional drone integration to track crop development and detect anomalies. Images are processed using computer vision models fine-tuned for greenhouse conditions. - Secure Cloud-Based Architecture
Deployed the system on a secure cloud infrastructure (GCP), leveraging containerized services (Docker) for scalability and reliability. - Data Privacy and Security
Implemented OAuth 2.0 for user authentication, SSL/TLS encryption for all data exchanges, and role-based access control to protect grower-sensitive data. - Structured Logging and Explainability
All agent decisions are logged and traceable, with explanations generated for every climate adjustment or recommendation, promoting transparency and trust.
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Value delivered
The GenAI Copilot helped simplify greenhouse operations and led to measurable improvements in productivity, decision-making, and team efficiency.
- Reduced manual workload in climate control by over 30%
Less time spent on data interpretation and climate decision-making. - Increased revenue by reducing losses and improving predictability
More precise decisions minimized risk and maximized yield. - Empowered growers of all experience levels
Clear AI-generated suggestions helped junior staff act with confidence. - Shifted from reactive to proactive climate management
Faster decisions based on real-time, historical, and forecasted data.
