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.

Social information search is an important form of social learning under uncertainty, yet little is known about how state anxiety and intolerance of uncertainty (IU) jointly shape adolescents’ active search behavior. Across two experiments, 180 high school students were assigned to an easy-arithmetic, difficult-arithmetic, or no-task control condition and then completed a social gambling task under risk (Experiment 1) or ambiguity (Experiment 2). Mixed-effects hurdle negative-binomial models separately examined search initiation and search length after initiation. In both experiments, the easy-arithmetic task reduced state anxiety, whereas the difficult-arithmetic task did not significantly increase it; therefore, the findings primarily concern the easy-arithmetic condition. Experimental condition, IU, and their interactions did not reliably predict search initiation. Under risk, the association between IU and search length differed between the easy- and difficult-arithmetic conditions; however, no between-condition differences in search length were significant at either relatively low or high IU. Under ambiguity, participants with relatively low IU in the easy-arithmetic condition searched more than those in the control condition, whereas no comparable difference emerged among participants with relatively high IU. These findings suggest that search initiation and continuation may reflect partly distinct processes and that IU may condition adolescents’ search persistence.