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All Journal JURNAL REKAYASA TEKNOLOGI INFORMASI Jurnal Ilmiah Behongang Multica Science and Technology Jurnal Janitra Informatika dan Sistem Informasi DiJITAC : Digital Journal of Information Technology and Communication Jurnal Karya Abdi Masyarakat
Mirwansyah, Dedy
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Health Professions Languange, Linguistic, Communication & Media Library & Information Science Physics Public Health Social Sciences

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Journal : Multica Science and Technology

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FRUIT IMAGE CLASSIFICATION USING DEEP LEARNING ALGORITHM: SYSTEMATIC LITERATURE REVIEW (SLR) Mirwansyah, Dedy; Arief Wibowo
Multica Science and Technology Vol 2 No 2 (2022): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v2i2.356

Abstract

Systematic literature review (SLR) research studies various classification models with deep learning algorithms on fruit with digital images. In recent years, computer vision and processing techniques are increasingly useful in the fruit industry, especially for quality and color inspection, sizing, and shape sorting applications. Research in this area demonstrates the feasibility of using a machine computer vision system to improve product quality. Utilizing deep learning in the field of image processing or digital image processing, Image Processing is used to assist humans in recognizing and/or classifying objects quickly, and precisely, and can process large amounts of data simultaneously. Classifying fruit through a computerized system using deep learning algorithms with CNN, MASK-RCNN, FASTER-RCNN, and SSD models. Developed on the multilayer perceptron (MLP) layer, the algorithm is processed into two-dimensional data, to the image and is capable of classifying images with larger classes.
Co-Authors Alameka, Faza Apriyanur, Andry Arief Wibowo Fahrullah, Fahrullah Fauziah Fauziah Khairullah, Muhammad Azmi Prambudi, Yulva Ramadhany, Viany Riyayatsyah , Riyayatsyah Wijayanto, Syamsul