IoT Robotic Grape Monitoring System

Complete end-to-end system for autonomous vineyard sensing

Complete IoT-Enabled Robotic Grape Monitoring System

A fully integrated hardware-software system for autonomous, non-destructive grape quality assessment in operational vineyards.

System Overview

This project represents the complete realization of precision viticulture technology - from custom sensor design to deployment-ready robotics. The system autonomously collects hyperspectral data, processes it in real-time, and generates spatially-resolved quality maps.

Hardware Components

Custom Hyperspectral Sensor

  • Push-broom imaging spectrometer
  • 224 spectral bands (400-1000nm)
  • Synchronized with robotic motion for geometric accuracy

Mobile Robot Platform

  • Vineyard-traversable chassis
  • GPS/RTK positioning for geo-registration
  • Onboard computing for real-time processing

IoT Communication Layer

  • Edge processing with cloud synchronization
  • Real-time data streaming and visualization
  • Multi-robot coordination capability

Software Stack

Perception Pipeline

  • OHSLIC for online phenotype segmentation
  • LISA for illumination-invariant quality prediction
  • Calibration and geometric correction modules

Mapping & Analytics

  • Spatial interpolation for continuous quality maps
  • Temporal tracking for within-season monitoring
  • Decision support interface for vineyard managers

Field Deployment

The system has been deployed across multiple commercial vineyards:

  • Autonomous operation: Multi-hour missions without human intervention
  • High-throughput: Process entire vineyard blocks in single sessions
  • Accuracy: Brix prediction R² > 0.85 under field conditions
  • Actionable insights: Quality maps used for selective harvesting decisions

Innovation

This project bridges the gap between laboratory spectroscopy and operational agriculture. Key innovations include:

  • Robustness: Handles real-world variability (lighting, occlusion, wind)
  • Scalability: System design allows fleet deployment
  • Usability: Farm-ready interface for non-technical operators

Related Publication: In-Field Mapping of Grape Yield and Quality with Illumination-Invariant Deep Learning (IEEE IoT Journal, 2025)

Impact

This complete system demonstrates that AI-driven precision agriculture is ready for commercial deployment, moving beyond proof-of-concept to solve real operational challenges in viticulture.