# Multi-Mode Multi-Feature Joint Intelligent Identification Methods for Nematodes
## 1 Introduction  
The automated recognition of plant nematodes is of significant value for pest control, soil ecology, biogeography, habitat conservation, and climate maintenance [1, 2, 3]. However, accurate identification is challenging, owing to their high phenotypic diversity [4] and ambiguous diagnostic features [5]. The traditional methods of plant nematode identification are based on manually identifying morphological and molecular characteristics by manual recognition [6, 7, 8, 9]. However, the time-consuming nature of manual identification makes it unsuitable for dealing with large-scale ecological data and challenging for non-professionals. Therefore, developing automated methods to identify nematodes efficiently and reliably is crucial.  
Automated classification approaches in biology combine computer-vision algorithms and machine-learning techniques [10, 11]. The development of convolutional neural networks (CNN) [12, 13, 14] to extract features automatically has greatly improved diagnostic accuracy [15, 16], as it replaced the tedious and imprecise traditional methods of manual feature extraction. Nematologists have demonstrated that automated solutions for identifying plant diseases using images and machine learning can significantly increase diagnosticaccuracy [17]. In recent years, researchers have gradually shifted to using CNN to identify plant nematodes [18; 19; 20; 21; 22]. [PERSON] al. [20] applied computer vision algorithms to identify _G. pallida_ and _G. rostochiensis_ with an accuracy of 91%. They used the promising novel approach of the automated morphological identification of nematodes optimized to recognize only _Globodera_ species. [PERSON] al. [21] proposed NemaNet, a neural network with specialized configurations for classifying the overall nematode structure, with an accuracy of 98.82%. [PERSON] et al. [22] proposed an online nematode recognition system that combines digital image-processing methods and neural networks for classification. This model serves as a valuable tool for those without nematology experience. However, the recognition accuracy needs to be improved for genera with similar morphologies. Because these methods may be limited by the dataset and diversity of nematodes, they lack universality, generalization, and interpretability.  
Cutting-edge machine learning techniques can identify plant nematodes with high accuracy; however, improving their accuracy and interpretability remains challenging. Here, we summarize the difficulties in classifying plant nematodes to point out potential directions for further improvement: (1) The inter-specific similarity of nematodes is exceptionally high. Owing to biodiversity and other reasons, the female and male worms of nematodes have different characteristics, making it challenging to determine whether they belong to the same nematode species. (2) It is difficult to match the key features found by neural networks and experts because of the limited interpretability of neural networks. (3) There is a lack of datasets with multiple categories, features, and hierarchies; however, training neural networks relies on a large amount of data.  
This study uses deep learning technology to design a multimodal, multi-feature joint recognition method for plant nematodes. Drawing on the recognition model of nematode experts, we combine target recognition and classification models to simulate the recognition process performed by experts and construct a network model structure with interpretability. The multi-part features for the nematodes are combined for recognition to form an automatic decision identification system for plant nematodes.  
The main contributions of this study to improving plant nematode identification methods are as follows:  
1. We propose a method for extracting the key features of plant nematodes.
2. We propose a multi-feature joint training scheme to improve the recognition accuracy of plant nematodes.
3. We construct an interpretable, multimodal, and intelligent decision-making model for plant nematodes.