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Phytoparasitic nematodes (or phytonematodes) are causing severe damage tocrops and generating large-scale economic losses worldwide. In soybean crops,annual losses are estimated at 10.6% of world production. Besides, identifyingthese species through microscopic analysis by an expert with taxonomy knowledgeis often laborious, time-consuming, and susceptible to failure. In thisperspective, robust and automatic approaches are necessary for identifyingphytonematodes capable of providing correct diagnoses for the classification ofspecies and subsidizing the taking of all control and prevention measures. Thiswork presents a new public data set called NemaDataset containing 3,063microscopic images from five nematode species with the most significant damagerelevance for the soybean crop. Additionally, we propose a new ConvolutionalNeural Network (CNN) model defined as NemaNet and a comparative assessment withthirteen popular models of CNNs, all of them representing the state of the artclassification and recognition. The general average calculated for each model,on a from-scratch training, the NemaNet model reached 96.99% accuracy, whilethe best evaluation fold reached 98.03%. In training with transfer learning,the average accuracy reached 98.88\%. The best evaluation fold reached 99.34%and achieve an overall accuracy improvement over 6.83% and 4.1%, forfrom-scratch and transfer learning training, respectively, when compared toother popular models.

NemaNet: A convolutional neural network model for identification of nematodes soybean crop in brazil