Early Detection of Coffee Leaf Rust Through Convolutional Neural Networks Trained on Low-Resolution Images

TL;DR

This paper presents a deep learning approach using convolutional neural networks trained on low-resolution images, enhanced by a high-pass filter preprocessing technique, to detect coffee leaf rust early with high accuracy in resource-limited settings.

Contribution

It introduces a novel image preprocessing method that improves CNN performance for early disease detection on low-resolution images, addressing resource constraints.

Findings

Achieved over 90% accuracy across all evaluation metrics.

Outperformed other preprocessing techniques and unaltered images.

Effective in resource-limited environments for early disease detection.

Abstract

Coffee leaf rust, a foliar disease caused by the fungus Hemileia vastatrix, poses a major threat to coffee production, especially in Central America. Climate change further aggravates this issue, as it shortens the latency period between initial infection and the emergence of visible symptoms in diseases like leaf rust. Shortened latency periods can lead to more severe plant epidemics and faster spread of diseases. There is, hence, an urgent need for effective disease management strategies. To address these challenges, we explore the potential of deep learning models for enhancing early disease detection. However, deep learning models require extensive processing power and large amounts of data for model training, resources that are typically scarce. To overcome these barriers, we propose a preprocessing technique that involves convolving training images with a high-pass filter to enhance lesion-leaf contrast, significantly improving model efficacy in resource-limited environments. This method and our model demonstrated a strong performance, achieving over 90% across all evaluation metrics--including precision, recall, F1-score, and the Dice coefficient. Our experiments show that this approach outperforms other methods, including two different image preprocessing techniques and using unaltered, full-color images.