# Multi-Mode Multi-Feature Joint Intelligent Identification Methods for Nematodes
## 4 Discussions  
The successful application of deep-learning techniques in various fields has promoted the automated intelligent identification of plant nematodes, although the corresponding research is in its preliminary exploration stage. Notably, most current automated nematode identification methods are based on single features and limited datasets, resulting in low classification accuracies [17; 18; 19; 20; 21; 22; 23]. Here in, we directly create a multi-feature plant nematode dataset from nematode samples and develop a standard procedure for image preparation. The proposed multi-mode, multi-feature nematode identification method can capture key features, and appropriate \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Task** & \multicolumn{2}{c}{**23 Species**} & \multicolumn{2}{c}{**Task**} & \multicolumn{2}{c}{**11 Species**} \\ \hline
**Model** & **Single** & **Doublet** & **Model** & **Doublet** & **Triplet** \\ \hline MobileNet & 73.45 & 94.16 & MobileNet & 94.96 & 97.00 \\ ShuffleNet & 73.73 & 94.54 & ShuffleNet & 95.06 & 96.98 \\ VGG16 & 85.79 & 95.66 & VGG16 & 96.92 & 97.89 \\ ResNet101 & 79.02 & 95.10 & ResNet101 & 96.21 & 97.16 \\ DenseNet121 & 76.88 & 95.20 & DenseNet121 & 96.56 & 97.12 \\ Xception & 77.96 & 94.47 & Xception & 95.67 & 96.39 \\ ConvNeXt & 76.82 & 93.91 & ConvNeXt & 95.28 & 96.82 \\ NemaNet & 72.15 & 94.74 & NemaNet & 95.68 & 96.78 \\ ViT & 72.13 & 94.02 & ViT & 95.14 & 96.83 \\ EfficientNetB6 & 84.10 & 96.74 & EfficientNetB6 & 97.54 & 98.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of multi-feature joint classification tasks. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Task** & **Accuracy of 23 Species Classification Task (\%)** \\ \hline
**Model** & **Original Model** & **Expert Model** \\ \hline MobileNet & 64.12 & 73.45 \\ ShuffleNet & 65.45 & 73.73 \\ VGG16 & 79.02 & 85.79 \\ ResNet101 & 70.15 & 79.02 \\ DenseNet121 & 71.95 & 76.88 \\ Xception & 69.78 & 77.96 \\ ConvNeXt & 67.75 & 76.82 \\ NemaNet & 65.67 & 72.15 \\ ViT & 65.15 & 72.13 \\ EfficientNetB6 & 78.93 & 84.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the classical and expert models on different classification tasks. cropping sizes are proposed. Notably, the intelligent decision-making expert identification system can perform classification with from single to triple features as input, the greater the number of features, the better the accuracy. The multi-feature joint training model proposed in this article simulates the recognition logic of domain experts and features strong interpretability. The experimental results reveal that the expert model exhibits high accuracy. Simultaneously, expert models can combine samples from different parts to reduce the number of samples required for model training. The proposed approach significantly outperforms existing nematode identification systems and achieves new state-of-the-art accuracy. Moreover, the method reduces the time cost of image recognition to the millisecond level and can be completely applied to nematode identification scenarios to assist practitioners in this field. The advent of deep learning technologies has addressed several difficulties that are associated with image classification; however, issues in online nematode image classification persist. In this study, the problems encountered in nematode classification are considered examples to explore feasible solutions and promote the application of deep-learning technology to the field of nematodes. However, the scope of this study can be further expanded in the following respects: First, the expert model is unsuitable for nematode classification tasks involving genera with very similar morphological characteristics (such as _P. loosiLof_ and _P. caffeine_). Second, the landmarks of different nematode types are different. The proposed expert classification model constructs the joint identification model using the head, female tail, and male tail. Hence, this approach cannot entirely describe the characteristics of plant nematodes.