  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 world production. Besides, identifying
these species through microscopic analysis by an expert with taxonomy knowledge
is often laborious, time-consuming, and susceptible to failure. In this
perspective, robust and automatic approaches are necessary for identifying
phytonematodes capable of providing correct diagnoses for the classification of
species and subsidizing the taking of all control and prevention measures. This
work presents a new public data set called NemaDataset containing 3,063
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 a comparative assessment with
thirteen popular models of CNNs, all of them representing the state of the art
classification and recognition. The general average calculated for each model,
on a from-scratch training, the NemaNet model reached 96.99% accuracy, while
the 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%, for
from-scratch and transfer learning training, respectively, when compared to
other popular models.
