A comparative analysis of convolutional neural networks approaches for phytoparasitic nematode identification

Nabila Husna Shabrina, Siwi Indarti, Ryukin Aranta Lika, Rina Maharani

Abstract


Phytoparasitic nematode is a microscopic worm that affects the host plants and causes severe losses in the agricultural sector. Accurate and rapid identification of phytoparasitic nematodes is required to determine proper pest control and management. Hence it has become a necessity to automate the phytoparasitic nematode identification procedure. This study conducts a comparative analysis of 15 popular convolutional neural networks models, namely CoAtNet-0, DenseNet121, DenseNet169, DenseNet201, EfficientNetV2B0, EfficientNetV2B3, EfficientNetV2L, EfficientNetV2M, EfficientNetV2S, InceptionResNetV2, InceptionV3, ResNet101v2, ResNet50v2, VGG19, and Xception to deals with phytoparasitic nematode identification from the microscopic image. The results are compared using several evaluation metrics, namely test accuracy, mean class accuracy, F1 score, precision, and recall. The results show that CoAtNet-0 outperformed other models with 98.06% test accuracy, 97.86% mean class accuracy, 0.9803 F1 score, 0.9818 Precision, and 0.9806 Recall.

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Published: 2023-06-26

How to Cite this Article:

Nabila Husna Shabrina, Siwi Indarti, Ryukin Aranta Lika, Rina Maharani, A comparative analysis of convolutional neural networks approaches for phytoparasitic nematode identification, Commun. Math. Biol. Neurosci., 2023 (2023), Article ID 65

Copyright © 2023 Nabila Husna Shabrina, Siwi Indarti, Ryukin Aranta Lika, Rina Maharani. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Commun. Math. Biol. Neurosci.

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