Natural enemies play a vital role in pest suppression and ecological balance within agricultural ecosystems. However, conventional vision-based recognition methods are highly susceptible to illumination variation, occlusion, and background noise in complex field environments, making it difficult to accurately distinguish morphologically similar species. To address these challenges, a multimodal natural enemy recognition and ecological interpretation framework, termed MAVC-XAI, is proposed to enhance recognition accuracy and ecological interpretability in real-world agricultural scenarios. The framework employs a dual-branch spatiotemporal feature extraction network for deep modeling of both visual and acoustic signals, introduces a cross-modal sampling attention mechanism for dynamic inter-modality alignment, and incorporates cross-species contrastive learning to optimize inter-class feature boundaries. Additionally, an explainable generation module is designed to provide ecological visualizations of the model’s decision-making process in both visual and acoustic domains. Experiments conducted on multimodal datasets collected across multiple agricultural regions confirm the effectiveness of the proposed approach. The MAVC-XAI framework achieves an accuracy of 0.938, a precision of 0.932, a recall of 0.927, an F1-score of 0.929, an mAP@50 of 0.872, and a Top-5 recognition rate of 97.8%, all significantly surpassing unimodal models such as ResNet, Swin-T, and VGGish, as well as multimodal baselines including MMBT and ViLT. Ablation experiments further validate the critical contributions of the cross-modal sampling attention and contrastive learning modules to performance enhancement. The proposed framework not only enables high-precision natural enemy identification under complex ecological conditions but also provides an interpretable and intelligent foundation for AI-driven ecological pest management and food security monitoring.

To address prevalent challenges in field-based wheat pest recognition—namely, viewpoint perturbations, sample scarcity, and heterogeneous data distributions—a pest identification framework named CropCLR-Wheat is proposed, which integrates self-supervised contrastive learning with an attention-enhanced mechanism. By incorporating a viewpoint-invariant feature encoder and a diffusion-based feature filtering module, the model significantly enhances pest damage localization and feature consistency, enabling high-accuracy recognition under limited-sample conditions. In 5-shot classification tasks, CropCLR-Wheat achieves a precision of 89.4%, a recall of 87.1%, and an accuracy of 88.2%; these metrics further improve to 92.3%, 90.5%, and 91.2%, respectively, under the 10-shot setting. In the semantic segmentation of wheat pest damage regions, the model attains a mean intersection over union (mIoU) of 82.7%, with precision and recall reaching 85.2% and 82.4%, respectively, markedly outperforming advanced models such as SegFormer and Mask R-CNN. In robustness evaluation under viewpoint disturbances, a prediction consistency rate of 88.7%, a confidence variation of only 7.8%, and a prediction consistency score (PCS) of 0.914 are recorded, indicating strong stability and adaptability. Deployment results further demonstrate the framework’s practical viability: on the Jetson Nano device, an inference latency of 84 ms, a frame rate of 11.9 FPS, and an accuracy of 88.2% are achieved. These results confirm the efficiency of the proposed approach in edge computing environments. By balancing generalization performance with deployability, the proposed method provides robust support for intelligent agricultural terminal systems and holds substantial potential for wide-scale application.