Grape Phaeomoniella chlamydospora Leaf Blotch Recognition and Infected Area Approximation Using Hybrid Linear Discriminant Analysis and Genetic Programming
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- @InProceedings{Alajas:2022:HNICEM,
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author = "Oliver John Alajas and Ronnie {Concepcion II} and
Argel Bandala and Edwin Sybingco and Elmer Dadios and
Christan Hail Mendigoria and Heinrick Aquino",
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title = "Grape Phaeomoniella chlamydospora Leaf Blotch
Recognition and Infected Area Approximation Using
Hybrid Linear Discriminant Analysis and Genetic
Programming",
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booktitle = "2022 IEEE 14th International Conference on Humanoid,
Nanotechnology, Information Technology, Communication
and Control, Environment, and Management (HNICEM)",
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year = "2022",
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address = "Boracay Island, Philippines",
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month = "01-04 " # dec,
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month = dec,
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keywords = "genetic algorithms, genetic programming, Support
vector machines, SVM, Image segmentation,
Visualization, Image recognition, Computational
modelling, Pipelines, Process control, image
processing, plant disease detection, machine learning,
computer vision, soft computing, black measles",
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ISSN = "2770-0682",
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isbn13 = "978-1-6654-6493-2",
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DOI = "doi:10.1109/HNICEM57413.2022.10109613",
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size = "6 pages",
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abstract = "Grapes, scientifically called Vitis vinifera, are
vulnerable against Phaeomoniella chlamydospora, the
microorganism that causes Esca (black measles) to the
leaves, trunks, cordons, and fruit of a young vineyard.
Manual visual examination via the naked eye can prove
to be challenging especially if done in large-scale
vineyards. To address this issue, merging the use of
computer vision, image processing, and machine learning
was employed as a means of performing blotch
identification and leaf blotch area prediction. The
dataset is made up of 543 images, comprised of healthy
and Esca infected leaves which were captured by an RGB
camera. Images were preprocessed and segmented to
isolate the diseased pixels and compute the ground
truth pixel area. Desirable leaf signatures (G, B,
contrast, H, R, S, a*, b*, Cb, and Cr) derived from the
feature extraction process using a classification tree.
The LDA12 was able to accurately distinguish the
healthy from the blotch-infected leaves with a whopping
98.77percent accuracy compared to NB, KNN, and SVM. The
MGSR12, with an R2 of 0.9208, topped other models such
as RTree, GPR, and RLinear. The hybrid
CTree-LDA12-MGSR12 algorithm proved to be ideal in
performing leaf health classification and blotched area
assessment of grape phenotypes which is important in
plant disease identification and fungal spread
prevention.",
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notes = "Also known as \cite{10109613}",
- }
Genetic Programming entries for
Oliver John Y Alajas
Ronnie S Concepcion II
Argel A Bandala
Edwin Sybingco
Elmer Jose P Dadios
Christan Hail Mendigoria
Heinrick L Aquino
Citations