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Development of a Real-Time AI-Powered Plant Monitoring Web Application for AgTech
  1. case
  2. Development of a Real-Time AI-Powered Plant Monitoring Web Application for AgTech

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Development of a Real-Time AI-Powered Plant Monitoring Web Application for AgTech

instinctools.com
Agriculture
Information technology

Business Challenges in Agricultural Monitoring

The client faced challenges related to labor shortages, manual plant monitoring processes, and lack of real-time data visualization. Existing systems failed to automate early detection of plant health issues (mildew, botrytis, pests), THC compliance tracking, and data-driven harvest predictions, leading to financial losses and inefficiencies.

About the Client

An innovative AgTech startup offering automated AI-powered plant monitoring services for large-scale greenhouses specializing in medical cannabis production.

Project Goals

  • Streamline plant care processes through automation
  • Replace manual labor with AI-powered sensors and robotics
  • Enable real-time health and compliance tracking for 10,000–15,000 plants
  • Provide actionable analytics for harvest predictions
  • Ensure compliance with THC regulations (≤0.3%) to minimize crop destruction

Core System Functionalities

  • Interactive farm map with 10,000+ plant cells (zoomable up to 500%)
  • Real-time health status tracking (mildew, botrytis, pests)
  • THC/CBD cannabinoid level visualization
  • Environmental parameter overlay (temperature, humidity)
  • Area-based statistics selection and filtering
  • OAuth 2.0 authentication (Google/Microsoft)
  • Automated plant relocation tracking via BlueIOT tags

Preferred Technologies

D3.js for data visualization
FastAPI (Python) for backend
React.js for frontend
PostgreSQL for database
Redis for caching
draw.io for configuration visualization

Integrations Required

  • BlueIOT® plant identification tags
  • Hugin robot data collection system
  • Odin AI analytics engine
  • OAuth 2.0 authentication providers

Non-Functional Requirements

  • Support 1,000+ concurrent frontend requests
  • Scalable architecture for 15,000+ plants
  • Real-time data updates (sub-1 minute latency)
  • Secure authentication and data handling
  • High availability (99.9% uptime)

Expected Business Impact

The solution will reduce labor costs by automating plant monitoring, minimize financial losses through early disease detection and THC compliance tracking, and enable data-driven harvest decisions. Scalable architecture will support expansion to multi-greenhouse farms while maintaining real-time performance.

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