Classification of diseases in snake plants using convolutional neural network

Kensa  Athalia; Tiffany; Kevin Adhi Dhamma Setiawan; Bertrand Ferrari; Chairisni Lubis

doi:10.47709/cnahpc.v6i1.3201

Authors

Kensa Athalia Universitas Tarumanagara
Tiffany Universitas Tarumanagara
Kevin Adhi Dhamma Setiawan Universitas Tarumanagara
Bertrand Ferrari Universitas Tarumanagara
Chairisni Lubis Universitas Tarumanagara

DOI:

10.47709/cnahpc.v6i1.3201

Keywords:

Convolutional Neural Network, Image Classification, VGG19, Lidah Mertua

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Abstract

snake plant has an important role in human life, as well as in increasing the aesthetic value of the environment. Limited knowledge about diseases in snake plants has a crucial result in improper handling and control when the plant is attacked by disease. Advances in deep learning technology and Convolutional Neural Network (CNN) have presented high opportunities with their advantages in recognizing patterns and features from image data. This research will use a CNN model with VGG-19 architecture to classify diseases in the leaves of the snake plant. It is expected that by using the pre-trained VGG-19 model, the model can recognize complex visual patterns in snake plants. Diseases to be classified include several types of diseases that often attack snake plants such as anthracnose, rust, water soaked lesion, and healthy plants for comparison. The highest value of training accuracy reached a value of 98.08%, validation accuracy of 94.02%, and testing accuracy reached 94%.

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