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,

• author = "Oliver John Alajas and Ronnie {Concepcion II} and Argel Bandala and Edwin Sybingco and Elmer Dadios and Christan Hail Mendigoria and Heinrick Aquino",
• title = "Grape Phaeomoniella chlamydospora Leaf Blotch Recognition and Infected Area Approximation Using Hybrid Linear Discriminant Analysis and Genetic Programming",
• booktitle = "2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)",
• year = "2022",
• address = "Boracay Island, Philippines",
• month = "01-04 " # dec,
• month = dec,
• 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",
• ISSN = "2770-0682",
• isbn13 = "978-1-6654-6493-2",
• DOI = "doi:10.1109/HNICEM57413.2022.10109613",
• size = "6 pages",
• 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.",
• 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

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