COCONUT DISEASE DETECTION USING DIGITAL IMAGE AND DEEP LEARNING

Author: Hamadie Seed D. Alswaidi; Anthony B. Amilano; Felix Jose I. Awa; Marian Mildred D. Bedural
2022 Computer Science

Coconut diseases cause production problem. In connection with this, the study aimed to gather and collect datasets of healthy coconut leaves and with coconut diseases particularly Graphiola Leaf Spot, Stigmina Leaf Spot, and Whiteflies; create a CNN model for detecting the specified coconut diseases; develop a prototype that supports the CNN model; and determine the level of accuracy of the developed model. The model of the study used the proposed VGG-16 Architecture under CNN Algorithm. The researchers employed quantitative research design for data collection since numerical measurement is needed for accuracy evaluation using confusion matrix. According to the interpreted data the model accuracy in detecting healthy leaves garnered 93%, leaves with whiteflies garnered 86.66%, leaves with Graphiola leaf spot garnered 73.33%, and leaves with Stigmina leaf spot garnered 80%. The overall model garnered 83.33% accuracy rate which implies that CNN algorithm is an effective algorithm for this study. For future researchers, the researchers recommend increasing the number of images per dataset and consider using another algorithm (i.e. KNN), add image capturing feature and solution to detected disease, apply this research to other coconut diseases (Cadang-cadang, Bud rot), and apply this research to other image classification (i.e. corn disease, tomato disease).

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AN INFORMATIVE APPLICATION ON CHICKEN BREED IDENTIFICATION USING IMAGE PROCESSING

Namirah L. Balindong; Arnold S. Sayat Jr.; Mat Andrew V. Tantiado; Rose Ann B. Sabilla; Mary Rose T. Lazaro

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Janet B. Sasaluya; Caren F. Vega; Crystallene R. Lagatic; Louis Carlo SF. Fornoles

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Rose Ann R. Navales; Judy Ann Soliman; Marlou B. Toledo; Filbert O. Tucio

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Jonalyn L. Bugnot; Kim Clarice Garcia; Jude Romar B. Cerdeño; Maria Jessica T. Sayin

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