LLS-SevEst - Late leaf spot severity estimator. A machine learning approach to assessing Nothopassalora personata in peanut.

Abstract

ABSTRACT Late leaf spot (LLS), caused by Nothopassalora personata , is the most damaging foliar disease in peanut production worldwide, leading to significant yield losses if not properly managed. Accurate disease severity assessment is crucial for evaluating fungicide efficacy and implementing effective management strategies. This study aimed to develop and validate an automated image analysis model, LLS-SevEst , for quantifying LLS severity in peanut leaves. A dataset of 190 scanned leaf images was analyzed using three approaches: a fixed threshold-based segmentation, morphological preprocessing, and K-means clustering. Exploratory analyses revealed distinct brightness patterns between healthy and diseased tissues, guiding the development of classification functions. The threshold-based model yielded high false positive rates due to its inability to account for natural leaf variation, while the morphological preprocessing method improved segmentation marginally but still required manual adjustments. The K-means clustering approach achieved superior segmentation by objectively differentiating healthy tissue, lesions, and background, and showed high potential for automated, reproducible disease severity estimation. Future work should focus on integrating deep learning and expanding the dataset to improve model robustness and adaptability to other foliar pathosystems.

Code and data availability

The paper's 190 scanned peanut leaf images and the LLS-SevEst analysis code are not publicly deposited; the DATA AVAILABILITY statement says datasets are available from the corresponding author on reasonable request. No authors' public URL for code or images is provided.

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