Multi-Mode Multi-Feature Joint Intelligent Identification Methods for Nematodes

Abstract

The identification of plant nematodes is crucial in the fields of pest control, soil ecology, and biogeography. The automated recognition of plant nematodes based on deep-learning technology can significantly improve the accuracy and efficiency of their recognition. In this study, we devised a method for the multi-mode, multi-feature identification of plant nematodes using deep-learning techniques which emulated the recognition logic of domain experts. Beginning with a multi-featured plant nematode dataset, we not only designed key feature extraction strategies to address the problem of weak key feature points and small inter-specific differences in plant nematodes but also proposed a multi-feature joint training scheme and constructed a neural network structure with interpretability. Finally, an intelligent decision-making expert identification system for plant nematodes was implemented, and its performance was tested on the multi-feature plant nematode dataset. The results indicate that our model achieves an accuracy of up to 96.74% in identifying 23 species across two-body parts, which is 17.5% higher than the single-part feature identification. The accuracy of identifying 11 species in three-body parts reached 98.46%, an improvement of 1.24% over that of the two-part feature identification. Our novel model demonstrates that the accuracy of the expert system can be increased by incorporating more nematode feature parts.

Keywords

• biodiversity
• plant nematodes
• nematode identification
• deep learning
• convolutional neural networks