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                <identifier>oai:data.mendeley.com/tzh9b7f2jz.1</identifier>
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    <dc:creator>Angela, Stephanie</dc:creator>
    <dc:title>Performance Results of Plant Disease Classification Pretrained Models</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>This dataset contains the performance results of five pre-trained deep learning models—EfficientNetV2B0, ResNet50, InceptionV3, DenseNet121, and VGG16—evaluated for plant disease classification tasks. The dataset includes metrics from experiments conducted on both training and testing datasets, focusing on key performance indicators such as accuracy, precision, recall, F1-score, and confusion matrices. The aim is to provide a comprehensive comparison of these models&apos; capabilities for detecting and classifying plant diseases.</dc:description>
    <dc:subject>Artificial Intelligence</dc:subject>
    <dc:subject>Machine Learning</dc:subject>
    <dc:subject>Agricultural Technology</dc:subject>
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    <dc:identifier>doi:10.17632/tzh9b7f2jz.1</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/tzh9b7f2jz.1</dc:identifier>
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    <dc:date>2025-01-08T17:55:18Z</dc:date>
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