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Title
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Cotton leaf diseases recognition using deep learning and genetic algorithm
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Sub-Title |
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Subject |
Plants diseases; data augmentation; deep learning; features fusion; features selection
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Sub-Subject |
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Author |
Muhammad Rizwan Latif, Muhamamd Attique Khan, Muha
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Publish Year |
2021 |
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Supervisor |
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Diss#. |
https://doi.org/10.32604/cmc.2021.017364 |
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Pages |
2917–2932. |
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Text Language |
English |
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Accession |
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Library Section |
Research Article |
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Abstract |
Globally,Pakistan ranks 4th in cotton production, 6th as an importer
of raw cotton, and 3rd in cotton consumption. Nearly 10% of GDP and
55% of the country’s foreign exchange earnings depend on cotton products.
Approximately 1.5 million people in Pakistan are engaged in the cotton value
chain. However, several diseases such as Mildew, Leaf Spot, and Soreshine
affect cotton production. Manual diagnosis is not a good solution due to
several factors such as high cost and unavailability of an expert. Therefore, it
is essential to develop an automated technique that can accurately detect and
recognize these diseases at their early stages. In this study, a new technique
is proposed using deep learning architecture with serially fused features and
the best feature selection. The proposed architecture consists of the following
steps: (a) a self-collected dataset of cotton diseases is prepared and labeled
by an expert; (b) data augmentation is performed on the collected dataset
to increase the
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