ACADSTAFF UGM

CREATION
Title : Deep learning models for automatic identification of plant-parasitic nematodes
Author :

Nabila Husna Shabrina (1) Ryukin Aranta Lika (2) Prof. Dr. Ir. Siwi Indarti, M.P. (3)
Date : 4 2023
Keyword : Augmentations,Classifications,Deep learning,Nematode,Optimization Augmentations,Classifications,Deep learning,Nematode,Optimization
Abstract : Plant-parasitic nematodes cause various diseases that can be fatal to the infected plants. It causes losses to the agricultural industry, such as crop failure and poor crop quality. Developing an accurate nematode classification system is vital for pest identification and control. Deep learning classification techniques can help speed up Nematode identification as it can perform tasks directly from images. In the present study, four state-of-the-art deep learning models (ResNet101v2, CoAtNet-0, Effi- cientNetV2B0, and EfficientNetV2M) were evaluated in plantparasitic nematode classification from microscopic image. The models were trained using a combination of three different optimizers (Adam, SGD, dan RMSProp) and several data augmentation with image transformations, such as image flip, blurring, noise addition, brightness, and contrast adjustment. The performance of the trained models was varied. Regarding test accuracy, EfficientNetV2B0 and EfficientNetV2M using RMSProp and brightness augmentation give the best result of 97.94% However, the overall performance of EfficientNetV2M was superior, with 98.66% mean class accuracy, 97.99? score, 98.26% average precision, and 97.94% average recall
Group of Knowledge :
Original Language : English
Level : Internasional
Status :
Published
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1 1-s2_0-S2589721723000016-main.pdf
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2 Cek Similarity - Deep learning models for automatic identification of plant-parasitic nematodes.pdf
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3 Full Dokumen - Deep learning models for automatic identification of plant-parasitic nematodes.pdf
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4 SURAT KETERANGAN PUBLIKASI_Nabila_Deep learning for automatic identification of plant-parasitic nematode.pdf
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