Maize seed variety identification model using image processing and deep learning

Gebeyehu, Seffi; Shibeshi, Zelalem S

Title: Maize seed variety identification model using image processing and deep learning
Creator: Gebeyehu, Seffi
Creator: Shibeshi, Zelalem S
Date Issued: 2024
Date: 2024
Type: text
Type: article
Identifier: http://hdl.handle.net/10962/429191
Identifier: vital:72566
Identifier: DOI: 10.11591/ijeecs.v33.i2.pp990-9985
Description: Maize is Ethiopia’s dominant cereal crop regarding area coverage and production level. There are different varieties of maize in Ethiopia. Maize varieties are classified based on morphological features such as shape and size. Due to the nature of maize seed and its rotation variant, studies are still needed to identify Ethiopian maize seed varieties. With expert eyes, identification of maize seed varieties is difficult due to their similar morphological features and visual similarities. We proposed a hybrid feature-based maize variety identification model to solve this problem. For training and testing the model, images of each maize variety were collected from the adet agriculture and research center (AARC), Ethiopia. A multi-class support vector machine (MCSVM) classifier was employed on a hybrid of handcrafted (ie, gabor and histogram of oriented gradients) and convolutional neural network (CNN)-based feature selection techniques and achieved an overall classification accuracy of 99%.
Format: 8 pages
Format: pdf
Language: English
Relation: Indonesian Journal of Electrical Engineering and Computer Science
Relation: Gebeyehu, S. and Shibeshi, Z.S., 2024. Maize seed variety identification model using image processing and deep learning. Indonesian Journal of Electrical Engineering and Computer Science, 33(2), pp.990-998
Relation: Indonesian Journal of Electrical Engineering and Computer Science volume 33 number 2 990 998 2024 2502-4760
Rights: Publisher
Rights: Use of this resource is governed by the terms and conditions of Indonesian Journal of Electrical Engineering and Computer Science Statement (https://ijeecs.iaescore.com/index.php/IJEECS/about/editorialPolicies#openAccessPolicy)

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Shibeshi, Zelalem Sintayehu (Dr)

RU Department of Computer Science

SDG 09. Industry, Innovation and Infrastructure

SDG 15. Life On Land

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