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An automated leaf disease diagnosis framework utilizing an extended Gaussian kernel density estimation for symptom clustering and a ResNet50 classifier achieves up to 98% accuracy in identifying coffee leaf diseases while reducing reliance on large-scale training datasets. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

April 10, 2023PlantsOpen Access

Automatic Clustering and Classification of Coffee Leaf Diseases Based on an Extended Kernel Density Estimation Approach

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Authors

RHReem Ibrahim HasanUniversity of Information Technology and Communications SYSuhaila Mohd Yusuf MRMohd Shafry Mohd RahimUniversity of Technology Malaysia

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Hasan et al. (2023) studied this question.

synapsesocial.com/papers/6a083ddcab15ea61dee8bcd5 https://doi.org/10.3390/plants12081603

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April 10, 2023PlantsOpen Access

Automatic Clustering and Classification of Coffee Leaf Diseases Based on an Extended Kernel Density Estimation Approach

View Full Paper

Ask AI

Bookmark

Authors

RHReem Ibrahim HasanUniversity of Information Technology and Communications SYSuhaila Mohd Yusuf MRMohd Shafry Mohd RahimUniversity of Technology Malaysia

Discussion

Member takes

Overview

Key Points

Key points are not available for this paper at this time.

Cite This Study

Hasan et al. (2023) studied this question.

synapsesocial.com/papers/6a083ddcab15ea61dee8bcd5 https://doi.org/10.3390/plants12081603

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