LLS-SevEst – Late leaf spot severity estimator. A machine learning approach to assessing Nothopassalora personata in peanut
Late leaf spot (LLS), caused by Nothopassalora personata, is the most damaging foliar disease in peanut production worldwide. Accurate disease severity assessment is crucial for evaluating and implementing effective management strategies. This study aimed to develop and validate an automated image a...
| Main Authors: | , , , |
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| Format: | info:ar-repo/semantics/artículo |
| Language: | Inglés |
| Published: |
Ediciones INTA
2025
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.12123/23494 https://doi.org/10.58149/2xz3-6879 |
| Summary: | Late leaf spot (LLS), caused by Nothopassalora personata, is the most damaging foliar disease in peanut production worldwide. Accurate disease severity assessment is crucial for evaluating 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 provided relatively better segmentation performance under the specific conditions tested and showed high potential for automated and reproducible disease severity estimation. This work should be considered a proof-of-concept, and further research is required to develop a robust and generalizable tool for LLS severity estimation. |
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