  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.
