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Related Experiment Video

Updated: Oct 30, 2025

Double-Staining Method to Detect Pectin in Plant-Fungus Interaction

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Published on: February 4, 2022

Coffee Disease Visualization and Classification.

Milkisa Yebasse¹, Birhanu Shimelis², Henok Warku³

¹Department of Computer Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea.

Plants (Basel, Switzerland)

|July 2, 2021

Summary

This study visualizes coffee plant diseases using deep learning, improving classification accuracy from 77% to 98%. Visualization techniques help models focus on disease regions, enhancing trust and performance in plant disease detection.

Keywords:

Grad-CAM Score-CAM coffee disease classification coffee disease visualization deep learning

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Area of Science:

Agricultural Science
Computer Science
Artificial Intelligence

Background:

Deep learning excels at image classification, including plant disease detection.
Current deep learning models for plant diseases often act as black boxes, lacking transparency.
Trust in automated disease classification is limited due to potential reliance on irrelevant image features.

Purpose of the Study:

To develop and evaluate visualization techniques for understanding deep learning models in coffee disease classification.
To gain insights into the regions of coffee plant images that deep learning models focus on during classification.
To improve the accuracy and reliability of coffee disease classification through visualization-guided approaches.

Main Methods:

Employed visualization methods including Grad-CAM, Grad-CAM++, and Score-CAM.
Applied these techniques to a dataset of Robusta coffee leaf images.
Developed a guided classification approach informed by visualization insights.

Main Results:

Visualization techniques successfully highlighted disease-specific regions in coffee leaf images.
Identified misclassifications by analyzing model attention areas.
The guided approach significantly improved classification accuracy to 98% from 77% (naïve approach).

Conclusions:

Visualization methods offer crucial insights into deep learning model behavior for plant disease detection.
A guided approach, informed by visualization, substantially enhances coffee disease classification accuracy.
This work promotes more transparent and trustworthy AI applications in agriculture.