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All Journal Justek : Jurnal Sains Dan Teknologi Sang Pencerah: Jurnal Ilmiah Universitas Muhammadiyah Buton Infotekmesin Insearch: Information System Research Journal
Dodi Arif
Universitas Gunadarma
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Mathematics Mechanical Engineering Social Sciences Other

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Journal : Insearch: Information System Research Journal

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Identification of 29 Types of Plant Diseases using Deep Learning EfficientNetB3 Adhitio Satyo Bayangkari Karno; Widi Hastomo; Indra Sari Kusuma Wardhana; Sutarno Sutarno; Dodi Arif
Insearch: Information System Research Journal Vol 2, No 02 (2022): Insearch (Information System Research) Journal
Publisher : Fakultas Sains dan Teknologi UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/isrj.v2i02.4389

Abstract

To supply the world's food needs in the midst of the existing food crisis, farmers urgently need to expand crop production. By establishing it simple to recognize the kind of plant disease so that earlier control efforts could be conducted, farmers' harvest failures driven on by disease attacks must be prevented. In this study, one of the Convolutional Neural Network (CNN) architectures known EfficeintNetB3 is applied to generate a classification model for 29 different types of plant diseases. A model is created after 3,170 image data are used for validation and 57,067 image data were utilized for training. 3,171 image data tests were conducted as part of the model testing phase, and the total test results were produced an extraordinarily high accuracy score of 0.99 percentage and an F1-score
Co-Authors Adhitio Satyo Bayangkari Karno Eka Sally Moreta Elliya Sestri Eva Karla Indra Sari Kusuma Wardhana Stevianus Stevianus Sudjiran Sutarno Sutarno Sutarno Sutarno Widi Hastomo