Smart Seed Classification System based on MobileNetV2 Architecture

Hamid, Yasir and Wani, Sharyar and Soomro, Arjumand Bano and Alwan, Ali A. and Gulzar, Yonis (2022) Smart Seed Classification System based on MobileNetV2 Architecture. In: 2022 2nd International Conference on Computing and Information Technology (ICCIT), 25 - 27 January 2022, Saudi Arabia.

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Abstract

The agricultural transformation in the last decade using artificial intelligence has led to significant gains in productivity and profitability. The traditional machine learning approaches present inherent limitations in extracting features and information from image data. Deep learning techniques, particularly CNN’s, help to overcome these limitations due to their multi-level architecture. Various deep learning applications in agriculture include crop disease identification, fruit classification, and germination rate monitoring. Seed image analysis is considered a significant task for the preservation of biodiversity and sustainability. This research uses MobileNetV2, a deep learning convolutional neural network (DCNNs) for seed classification. This model has been preferred due to its simple architecture and memory-efficient characteristics. A total of 14 different classes of seeds were used for the experimentation. The results indicate accuracies of 98% and 95% on training and test sets, respectively.

Item Type:	Conference or Workshop Item (Other)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
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
Depositing User:	Dr. Sharyar Wani
Date Deposited:	21 Dec 2022 17:27
Last Modified:	21 Dec 2022 17:27
URI:	http://irep.iium.edu.my/id/eprint/101722

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