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The problem that often occurs in the Agricultural Experimental Garden of Udayana
University, especially in the field of agricultural crops in holticulture is about plant diseases, this
causes a decrease in production results, so the need for early diagnosis of diseases of plants.
The study focused on California papaya plants. Diseases of this plant often appear on the
leaves and fruits. With advances in technology in the field of image processing can help
problems that occur in the field of agriculture. In this study build an Android application with
CNN (Convolutional Neural Network) method using SqueezeNet architecture. Classifying
diseases in this plant are Anthracnose, and Ringspot Viruse, as well as classifying healthy
papaya. Based on the validation results, the application built using CNN method and
SqueezeNet Architecture, can recognize Anthracnose disease, Ringspot Viruse and Papaya
healthy through leaves with accuracy of 97% while through fruit accuracy reaches 70%.
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