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A Convolution Neural Network-Based Seed Classification System

Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Information Security and Engineering Technology, AbuDhabi Polytechnic College, Abu Dhabi 111499, UAE
Department of Computer Science, Faculty of Information and Communication Technology, International Islamic University Malaysia, Gombak, Selangor 53100, Malaysia
AgroSup Informatic Laboratory, Burgundy University, 21078 Dijon, France
Authors to whom correspondence should be addressed.
Symmetry 2020, 12(12), 2018;
Received: 6 November 2020 / Revised: 1 December 2020 / Accepted: 4 December 2020 / Published: 7 December 2020
(This article belongs to the Section Computer and Engineering Science and Symmetry)
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 checkpointing 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. View Full-Text
Keywords: seed classification; artificial intelligence; convolutional neural networks; transfer learning; precision agriculture; adjustable learning seed classification; artificial intelligence; convolutional neural networks; transfer learning; precision agriculture; adjustable learning
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MDPI and ACS Style

Gulzar, Y.; Hamid, Y.; Soomro, A.B.; Alwan, A.A.; Journaux, L. A Convolution Neural Network-Based Seed Classification System. Symmetry 2020, 12, 2018.

AMA Style

Gulzar Y, Hamid Y, Soomro AB, Alwan AA, Journaux L. A Convolution Neural Network-Based Seed Classification System. Symmetry. 2020; 12(12):2018.

Chicago/Turabian Style

Gulzar, Yonis; Hamid, Yasir; Soomro, Arjumand B.; Alwan, Ali A.; Journaux, Ludovic. 2020. "A Convolution Neural Network-Based Seed Classification System" Symmetry 12, no. 12: 2018.

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