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A convolution neural network-based seed classification system

Gulzar, Yonis and Hamid, Yasir and Soomro, Arjumand Bano and Aljuboori, Ali A.Alwan and Journaux, Ludovic (2020) A convolution neural network-based seed classification system. Symmetry, 12 (12). pp. 1-18. ISSN 2073-8994

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Over the last few years, the research into agriculture has gained momentum, showing signs of rapid growth. The latest to appear on the scene is bringing convenience in how agriculture can be done by employing various computational technologies. There are lots of factors that affect agricultural production, with seed quality topping the list. Seed classification can provide additional knowledge about quality production, seed quality control and impurity identification. The process of categorising seeds has been traditionally done based on characteristics like colour, shape and texture. Generally, this is performed by specialists by visually inspecting each sample, which is a very tedious and time-consuming task. This procedure can be easily automated, providing a significantly more efficient method for seed sorting than having them be inspected using human labour. In related areas, computer vision technology based on machine learning (ML), symmetry and, more particularly, convolutional neural networks (CNNs) have been generously applied, often resulting in increased work efficiency. Considering the success of the computational intelligence methods in other image classification problems, this research proposes a classification system for seeds by employing CNN and transfer learning. The proposed system contains a model that classifies 14 commonly known seeds with the implication of advanced deep learning techniques. The techniques applied in this research include decayed learning rate, model check pointing and hybrid weight adjustment. This research applies symmetry when sampling the images of the seeds during data formation. The application of symmetry generates homogeneity with regards to resizing and labelling the images to extract their features. This resulted in 99% classification accuracy during the training set. The proposed model produced results with an accuracy of 99% for the test set, which contained 234 images. These results were much higher than the results reported in related research.

Item Type: Article (Journal)
Additional Information: 7094/86021
Uncontrolled Keywords: seed classification; artificial intelligence; convolutional neural networks; transfer learning; precision agriculture; adjustable learning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Information and Communication Technology
Kulliyyah of Information and Communication Technology

Kulliyyah of Information and Communication Technology > Department of Computer Science
Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User: DR. ALI A. ALWAN AL-JUBOORI
Date Deposited: 10 Dec 2020 11:32
Last Modified: 10 Dec 2020 11:32
URI: http://irep.iium.edu.my/id/eprint/86021

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