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Updated: Oct 22, 2025

The Hawaii Protocol for Scientific Monitoring of Coffee Berry Borer: a Model for Coffee Agroecosystems Worldwide

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Published on: March 19, 2018

A Cyber-Physical Data Collection System Integrating Remote Sensing and Wireless Sensor Networks for Coffee Leaf Rust

David Velásquez^1,2,3, Alejandro Sánchez¹, Sebastián Sarmiento¹

¹RID on Information Technologies and Communications Research Group, Universidad EAFIT, Carrera 49 No. 7 Sur-50, Medellín 050022, Colombia.

Sensors (Basel, Switzerland)

|August 28, 2021

Summary

Early diagnosis of Coffee Leaf Rust (CLR) is crucial for crop protection. A new cyber-physical system integrating remote sensing and wireless networks effectively collects data for training machine learning models to detect CLR.

Keywords:

coffee leaf rust cyber-physical system internet of things mechatronic design remote sensing technological integration wireless sensor networks

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Area of Science:

Agricultural Science
Computer Science
Environmental Science

Background:

Coffee Leaf Rust (CLR) is a persistent fungal disease impacting global coffee production since the 1980s.
Early CLR diagnosis is vital for minimizing crop damage and safeguarding farmer profitability.

Purpose of the Study:

To develop and validate a cyber-physical data-collection system for early Coffee Leaf Rust diagnosis.
To enable automated, reliable data gathering for machine learning model training.

Main Methods:

Integrated Remote Sensing and Wireless Sensor Networks (WSN) for data acquisition on a test coffee crop.
Utilized Red-Green-Blue (RGB) and multi-spectral cameras (RE, RGN) for multi-type data collection.
Implemented a real-time data-visualization dashboard for monitoring collection routines.

Main Results:

Successfully developed a system capable of automatic data collection, structuring, and storage.
Generated a three-month dataset suitable for training CLR diagnosis machine learning models.
Validated the system's ability to collect, store, and transfer reliable coffee crop data.

Conclusions:

The developed cyber-physical system effectively supports early Coffee Leaf Rust diagnosis.
The system provides a reliable foundation for creating advanced CLR detection machine learning models.
This technology offers a strategic approach to mitigate the economic impact of CLR on coffee farming.