Título :	NemaNet : a convolutional neural network model for identification of soybean nematodes
Autor :	Abade, André Porto, Lucas Faria Ferreira, Paulo Afonso Vidal, Flávio de Barros
Assunto::	Plantas - doenças e pragas Fitonematóide Nematoda em plantas Deep Learning Redes neurais convolucionais
Fecha de publicación :	ene-2022
Editorial :	Elsevier
Citación :	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.
Abstract:	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.
Licença::	© 2021 IAgrE. Published by Elsevier Ltd. All rights reserved.
DOI:	https://doi.org/10.1016/j.biosystemseng.2021.11.016
metadata.dc.relation.publisherversion:	https://www.sciencedirect.com/science/article/abs/pii/S153751102100283X
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