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Related Experiment Video

Updated: Apr 15, 2026

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

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

Published on: March 19, 2018

Proposal for Computationally Efficient Fog Computing System for Coffee Berry Borer Detection via Optimized YOLOv26.

Ingrid P Huaman-Pacco¹, Erwin J Sacoto-Cabrera², Vinie Lee Silva-Alvarado³

¹TESLA Laboratory, Universidad Nacional de San Antonio Abad del Cusco, Cusco 08003, Peru.

Sensors (Basel, Switzerland)

|April 14, 2026

Summary

Researchers optimized object detection models for early Coffee Berry Borer detection in Coffea arabica. Model M6 offers the best accuracy-efficiency balance, proving effective for real-time agricultural pest management.

Keywords:

CNN WSN advanced AI fog computing image analysis machine learning object optimization precision agriculture

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Last Updated: Apr 15, 2026

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

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

Published on: March 19, 2018

Methodology to Test Control Agents and Insecticides Against the Coffee Berry Borer Hypothenemus hampei

Methodology to Test Control Agents and Insecticides Against the Coffee Berry Borer Hypothenemus hampei

Published on: March 23, 2022

Area of Science:

Agricultural Science
Computer Vision
Pest Management

Background:

The Coffee Berry Borer is a major threat to global Coffea arabica production.
Early detection of infestation is difficult due to small symptom size and complex field conditions.

Purpose of the Study:

To evaluate optimized object detection architectures for improved accuracy and computational efficiency in identifying Coffee Berry Borer damage.
To find the best balance between detection accuracy and computational cost for pest identification.

Main Methods:

Established baseline models: YOLOv8n (M0), YOLOv11n (M1), and YOLOv26n (M2).
Developed seven variants (M3-M9) by integrating FasterNet, SimSPPF, and EMA.
Utilized Pareto analysis to determine the optimal model configuration.

Main Results:

Model M0 showed the highest detection accuracy (mAP@0.5 = 0.9534).
Model M6 (FasterNet + SimSPPF) achieved the best accuracy-efficiency trade-off (mAP@0.5 = 0.9446, 5.12 GFLOPs).
In situ validation demonstrated a mean F1-score of 0.7255 for detecting infected berries, even with shadows.

Conclusions:

Model M6 is the optimal configuration for detecting Coffee Berry Borer infestation.
The developed models are suitable for real-time agricultural deployment in pest management.
Optimized object detection significantly enhances the ability to manage coffee crop pests.