[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2774":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"view_count":32,"doi":33,"paper":34,"created_at":57},2774,"SpatioFormer: spatial perception enhancement for lightweight agricultural pest and disease detection","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1925867","Introduction In precision agriculture, accurate and efficient detection of crop pests and diseases is crucial. However, existing models in complex environments are prone to insufficient spatial perception and attenuation of disease texture features, making it difficult to balance recognition accuracy and lightweighting. Methods To address this, this study proposes a lightweight spatial perception enhancement hybrid architecture, SpatioFormer. First, a Pixel-level Detail Retrieval (PDR) mechanism is designed. This mechanism leverages cross-layer dynamic routing to facilitate the fusion of deep semantic features with shallow texture features, significantly enhancing the capability to capture disease features. Second, we design a Spatially Adaptive Modulation Attention (SA-SHMA) mechanism, which utilizes large-kernel depthwise convolution to capture contextual information and combines dynamic modulation maps for fine-grained focusing, efficiently recovering spatial details, and suppressing background noise. Furthermore, this paper introduces a Context-Guided Asymmetric Gated Linear Unit (CGA-GLU), which utilizes an asymmetric design focusing on the gating branch and incorporates contextual information for guidance, enhancing the inter-channel representation capability with minimal computational overhead. Results Finally, extensive experiments on the PDDD and Tomato-Village datasets validated the effectiveness of the proposed model. The proposed model achieves a Top-1 accuracy of 81.05% on the PDDD dataset and an AP 50 of 61.53% on the Tomato-Village dataset, with testing latency on edge devices being highly competitive among existing models. Discussion Compared to existing lightweight hybrid models, SpatioFormer effectively recovers shallow spatial details and precisely suppresses complex background noise under an extremely low parameter budget. Consequently, it achieves a superior balance between practical disease localization capability and inference latency on resource-constrained edge devices.","引言 在精准农业中，准确高效地检测作物病虫害至关重要。然而，复杂环境下的现有模型容易出现空间感知不足和病害纹理特征衰减的问题，难以兼顾识别精度与轻量化。方法 为解决这一问题，本研究提出了一种轻量级空间感知增强混合架构——SpatioFormer。首先，设计了像素级细节检索（Pixel-level Detail Retrieval，PDR）机制。该机制利用跨层动态路由，促进深层语义特征与浅层纹理特征的融合，显著增强了对病害特征的捕捉能力。其次，设计了空间自适应调制注意力（Spatially Adaptive Modulation Attention，SA-SHMA）机制，该机制利用大核深度卷积捕获上下文信息，并结合动态调制图进行细粒度聚焦，高效恢复空间细节并抑制背景噪声。此外，本文引入了上下文引导非对称门控线性单元（Context-Guided Asymmetric Gated Linear Unit，CGA-GLU），其采用聚焦门控分支的非对称设计，并融入上下文信息进行引导，以极小的计算开销增强了通道间表征能力。结果 最后，在PDDD和Tomato-Village数据集上的大量实验验证了所提模型的有效性。所提模型在PDDD数据集上取得了81.05%的Top-1准确率，在Tomato-Village数据集上取得了61.53%的AP 50，其在边缘设备上的测试延迟在现有模型中极具竞争力。讨论 与现有轻量级混合模型相比，SpatioFormer在极低的参数预算下有效恢复了浅层空间细节，并精确抑制了复杂背景噪声。因此，它在实际病害定位能力与资源受限边缘设备上的推理延迟之间实现了更优的平衡。",null,"Frontiers in Plant Science","2026-09-16T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,18,13,9,1,"提出轻量化空间感知增强架构，在边缘设备上兼顾检测精度与推理延迟，方法新颖、实验扎实，对农业病虫害智能识别有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","边缘计算","番茄","病虫害检测",0,"10.3389\u002Ffpls.2026.1925867",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":48,"card":49,"direction":55,"ingested_from":56},"W7213437661",[37,39,42,44,46],{"name":38,"orcid":9},"Wenbo Ma",{"name":40,"orcid":41},"Hao Sun","https:\u002F\u002Forcid.org\u002F0000-0002-6983-8149",{"name":43,"orcid":9},"Kun Zhou",{"name":45,"orcid":9},"Meichun Wang",{"name":47,"orcid":9},"Rui Fu","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1925867\u002Fpdf",{"tldr":50,"method":51,"finding":52,"direction":53,"opportunity":54},"提出轻量混合架构SpatioFormer，提升复杂环境下农作物病虫害检测的空间感知能力。","设计PDR跨层动态路由、SA-SHMA大核注意力与CGA-GLU门控，在PDDD","在极低参数量下恢复浅层空间细节并抑制背景噪声，边缘设备延迟具竞争力。","农业人工智能与决策模型","可探索将空间感知增强机制迁移至多作物多病害场景，并研究边缘端实时部署的能效优化。","智慧农业 \u002F 农业物联网","openalex","2026-09-17T23:30:14.148727Z"]