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                <identifier>oai:data.mendeley.com/7z67nyc57w.2</identifier>
                <datestamp>2022-06-27T15:22:30Z</datestamp>
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    <dc:creator>Rajbongshi, Aditya</dc:creator>
    <dc:title>FlowerNet: An extensive rose leaves dataset for disease recognition applying machine learning and deep learning models</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>(1) Plant diseases are the leading cause of decreased agricultural output, which leads to economic losses. Rose is known as both the &quot;Queen of Flowers&quot; and the &quot;King of Flowers.&quot; This implies that it possesses both kingliness (magnificence, status, and power) and queenliness (beauty, grace, and cultural refinement). Rose illnesses, on the other hand, have a detrimental effect on rose production.
(2) Computer vision and image processing have a big influence on detecting numerous illnesses in flowering plants.
(3) The collection comprises images of diseased Rose leaves as well as disease-free Rose leaves, which may be used to construct an automated method for researchers to forecast illnesses in Rose Flowers. The dataset includes two Rose diseases: black spot and downy mildew. Aside from the disease-free leaves, the dataset also includes them.
(4) This section includes two types of datasets: the original dataset (a total of 917 photos) and the augmented dataset (a total of 4342 images). Each picture has a generalized dimension of 512*512 pixels.
(5) The photos were personally taken from the Village of Roses (Golap Gram), Sadullapur Road Birulia Bridge, Dhaka 1216, Bangladesh, with the assistance of domain specialists and a plant study institute.
</dc:description>
    <dc:subject>Computer Vision</dc:subject>
    <dc:subject>Image Processing</dc:subject>
    <dc:subject>Machine Learning</dc:subject>
    <dc:subject>Image Classification</dc:subject>
    <dc:subject>Deep Learning</dc:subject>
    <dc:contributor>Sazzad, Sadia </dc:contributor>
    <dc:contributor>Shakil, Rashiduzzaman </dc:contributor>
    <dc:contributor>Akter, Bonna </dc:contributor>
    <dc:contributor>Kaiser, M Shamim</dc:contributor>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/7z67nyc57w.2</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/7z67nyc57w.2</dc:identifier>
    <dc:rights>Creative Commons Attribution 4.0 International</dc:rights>
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    <dc:relation>https://data.mendeley.com/datasets/7z67nyc57w</dc:relation>
    <dc:date>2022-06-27T15:22:30Z</dc:date>
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