Parallelization of Concise Convolutional Neural Networks for Plant Classification

• Autorzy: Sembiring Arnes , Away Yuwaldi , Arnia Fitri , Muharar Rusdha

Identyfikatory

DOI

10.12911/22998993/156754

• Języki publikacji: EN

Abstrakty

EN

Monitoring the agricultural field is the key to preventing the spread of disease and handling it quickly. The computer-based automatic monitoring system can meet the needs of large-scale and real-time monitoring. Plant classifiers that can work quickly in computer with limited resources are needed to realize this monitoring system. This study proposes convolutional neural network (CNN) architecture as a plant classifier based on leaf imagery. This architecture was built by parallelizing two concise CNN channels with different filter sizes using the addition operation. GoogleNet, SqueezeNet and MobileNetV2 were used to compare the performance of the proposed architecture. The classification performance of all these architectures was tested using the PlantVillage dataset which consists of 38 classes and 14 plant types. The experimental results indicated that the proposed architecture with a smaller number of parameters achieved nearly the same accuracy as the comparison architectures. In addition, the proposed architecture classified images 5.12 times faster than SqueezeNet, 8.23 times faster than GoogleNet, and 9.4 times faster than MobileNetV2. These findings suggest that when implemented in the agricultural field, the proposed architecture can be a reliable and faster plant classifier with fewer resources.

Słowa kluczowe

EN

parallelisation; concise CNN; plant classification; multi-scale CNN; convolutional neural network

• Wydawca: Polskie Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology
• Czasopismo: Journal of Ecological Engineering
• Rocznik: 2023
• Tom: Vol. 24, nr 2
• Strony: 61--71
• Opis fizyczny: Bibliogr. 41 poz., rys., tab.

Twórcy

autor

Sembiring Arnes

• Doctoral Program, School of Engineering, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia

• https://orcid.org/0000-0002-0819-4968

autor

Away Yuwaldi

• Doctoral Program, School of Engineering, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
• Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia

• https://orcid.org/0000-0001-6798-054X

autor

Arnia Fitri

• Doctoral Program, School of Engineering, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
• Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia

• https://orcid.org/0000-0001-6020-1275

autor

Muharar Rusdha

• Doctoral Program, School of Engineering, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
• Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia

• https://orcid.org/0000-0002-5079-4174

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).