CNN Method to Identify the Banana Plant Diseases based on Banana Leaf Images by Giving Models of ResNet50 and VGG-19

CNN Method to Identify the Banana Plant Diseases based on Banana Leaf Images by Giving Models of ResNet50 and VGG-19

banana disease identification; data processing, machine learning; convolutional neural networks

Identify banana plant diseases using machine learning with the CNN method to make it easier to identify diseases in banana
plants through leaf images. It employs the CNN method, incorporating ResNet50 because ResNet50 is one of the best models
and a suitable model for the dataset used, and the VGG-19 model is used because VGG-19 was one of the winning models of
the 2014 ImageNet Challenge and is a model that also fits the dataset used. The research objectives encompass dataset
processing, model architecture development, evaluation, and result reporting, all aimed at enhancing disease identification in
banana plants. The ResNet50 model achieved an impressive 94% accuracy, with 88% precision, 91% recall, and an 89% F1-
score, while the VGG-19 model demonstrated strong performance with 91% accuracy, surpassing prior research and
highlighting the effectiveness of these models in identifying banana plant diseases through leaf images. In conclusion, the
ResNet50 model's exceptional accuracy positions it as the preferred model for CNN-based disease identification in banana
plants, offering significant advancements and insights for agricultural practices. Future research opportunities include
exploring alternative CNN models, architectural variations, and more extensive training datasets to enhance disease
identification accuracy

Ilham Rahmana Syihad, Muhammad Rizal, Zamah Sari, Yufis Azhar

http://jurnal.iaii.or.id

Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association

December 2023

Sri Wahyuni

ISSN Media Electronic: 2580-0760

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