NemaNet : a convolutional neural network model for identification of soybean nematodes

Abade, André; Porto, Lucas Faria; Ferreira, Paulo Afonso; Vidal, Flávio de Barros

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Campo DC	Valor	Idioma
dc.contributor.author	Abade, André	-
dc.contributor.author	Porto, Lucas Faria	-
dc.contributor.author	Ferreira, Paulo Afonso	-
dc.contributor.author	Vidal, Flávio de Barros	-
dc.date.accessioned	2022-02-12T02:56:33Z	-
dc.date.available	2022-02-12T02:56:33Z	-
dc.date.issued	2022-01	-
dc.identifier.citation	ABADE, André et al. NemaNet: a convolutional neural network model for identification of soybean nematodes. Biosystems Engineering, v. 213, p. 39-62, jan. 2022. DOI: https://doi.org/10.1016/j.biosystemseng.2021.11.016. Disponível em: https://www.sciencedirect.com/science/article/abs/pii/S153751102100283X. Acesso em: 11 fev. 2022.	pt_BR
dc.identifier.uri	https://repositorio.unb.br/handle/10482/42868	-
dc.language.iso	Inglês	pt_BR
dc.publisher	Elsevier	pt_BR
dc.rights	Acesso Restrito	pt_BR
dc.title	NemaNet : a convolutional neural network model for identification of soybean nematodes	pt_BR
dc.type	Artigo	pt_BR
dc.subject.keyword	Plantas - doenças e pragas	pt_BR
dc.subject.keyword	Fitonematóide	pt_BR
dc.subject.keyword	Nematoda em plantas	pt_BR
dc.subject.keyword	Deep Learning	pt_BR
dc.subject.keyword	Redes neurais convolucionais	pt_BR
dc.rights.license	© 2021 IAgrE. Published by Elsevier Ltd. All rights reserved.	pt_BR
dc.identifier.doi	https://doi.org/10.1016/j.biosystemseng.2021.11.016	pt_BR
dc.relation.publisherversion	https://www.sciencedirect.com/science/article/abs/pii/S153751102100283X	pt_BR
dc.description.abstract1	Phytoparasitic nematodes (or phytonematodes) are causing severe damage to crops and generating large-scale economic losses worldwide. In soybean crops, annual losses are estimated at 10.6% of the world production. Besides, the identification of these species through microscopic analysis by an expert with taxonomic knowledge is often laborious, time-consuming, and susceptible to failure. From this perspective, robust and automatic approaches are necessary for identifying phytonematodes that are capable of providing correct diagnoses for the classification of species and subsidizing of all control and prevention measures. This work presents a new public data set called NemaDataset containing 3063 microscopic images from five nematode species with the most significant damage relevance for the soybean crop. Additionally, we propose a new Convolutional Neural Network (CNN) model defined as NemaNet and present a comparative assessment with thirteen popular models of CNNs, all of them representing state-of-the art classification and recognition. The general average was calculated for each model, on a from-scratch training; the NemaNet model reached 96.76% accuracy, while the best evaluation fold reached 98.04%. When training with transfer learning was performed, the average accuracy reached 98.82%. The best evaluation fold reached 99.35%, and overall accuracy improvements of over 6.83% and 4.1%, for from-scratch and transfer learning training, respectively, compared to other popular models were achieved.	pt_BR
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