[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4227":3,"related-4227":83},{"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,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":59,"slug":62,"view_count":63,"doi":64,"paper":65,"created_at":82},4227,"A Graph Attention–Mamba Framework Utilizing Chinese Pangolin Optimization for Cross-Domain Plant Disease Recognition and Severity Estimation","https:\u002F\u002Fdoi.org\u002F10.48084\u002Fetasr.20258","Correct diagnosis and severity evaluation of plant diseases is a prerequisite for precision agriculture and crop management. Existing approaches often struggle to simultaneously model symptom relationships, long-range contextual dependencies, and disease severity estimation under cross-domain conditions. In this paper, an explainable Graph Attention–Mamba framework optimized by the Chinese Pangolin Algorithm (GAM-CPO) is proposed for simultaneous plant disease recognition and severity estimation. The framework is composed of Graph Attention Networks (GANs) to model local symptom relationships, Mamba state-space learning to capture long-range contextual dependencies, and Chinese Pangolin Optimization (CPO) for adaptive parameter tuning. The model was trained on the PlantVillage dataset and cross-domain validated with the PlantDoc dataset. The proposed framework achieved 99.12% accuracy, 99.01% F1-score, and 0.995 AUC on PlantVillage and 95.37% accuracy and 94.93% F1-score on PlantDoc. For severity estimation, the model obtained 0.091 MAE, 0.147 RMSE, and a Kappa score of 0.946. Grad-CAM visualizations further validated the interpretability of the proposed framework. The results demonstrate that GAM-CPO is an accurate, robust, and efficient solution for the intelligent diagnosis and severity assessment of plant diseases.","植物病害的准确诊断与严重程度评估是精准农业和作物管理的前提。现有方法往往难以在跨域条件下同时建模症状关系、长程上下文依赖和病害严重程度估计。本文提出了一种由中华穿山甲算法优化的可解释图注意力-Mamba框架(GAM-CPO)，用于植物病害识别与严重程度估计的同步实现。该框架由用于建模局部症状关系的图注意力网络(GANs)、用于捕获长程上下文依赖的Mamba状态空间学习以及用于自适应参数调优的中华穿山甲优化算法(CPO)组成。模型在PlantVillage数据集上进行训练，并通过PlantDoc数据集进行跨域验证。所提框架在PlantVillage上达到了99.12%的准确率、99.01%的F1分数和0.995的AUC，在PlantDoc上达到了95.37%的准确率和94.93%的F1分数。在严重程度估计方面，模型获得了0.091的MAE、0.147的RMSE和0.946的Kappa分数。Grad-CAM可视化进一步验证了所提框架的可解释性。结果表明，GAM-CPO是一种准确、稳健且高效的植物病害智能诊断与严重程度评估解决方案。",null,"Engineering Technology & Applied Science Research","2026-10-02T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,13,8,1,"提出图注意力与Mamba结合的可解释病害识别与严重度估计框架，跨域验证数据扎实，但属单篇学术论文，产业影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","植物病害识别","病害严重度评估","跨域识别",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,53],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":52},7,true,[],[50,51],"精准农业","precision agriculture",[27],{"code":54,"score":55,"supported":14,"title":56,"abstract":57,"tags":58},"I2",2,[],[],[28,29],[60,61],"PlantVillage PlantDoc 病害识别","GAM-CPO 植物病害","PlantVillagePlantDoc病害识别-4227",3,"10.48084\u002Fetasr.20258",{"doi":64,"openalex_id":66,"authors":67,"venue":10,"cited_by_count":72,"oa_url":73,"card":74,"direction":80,"ingested_from":81},"W7218881201",[68,70],{"name":69,"orcid":9},"S. Suresha",{"name":71,"orcid":9},"I. Manimozhi",0,"https:\u002F\u002Fwww.etasr.com\u002Findex.php\u002FETASR\u002Farticle\u002Fdownload\u002F20258\u002F7214",{"tldr":75,"method":76,"finding":77,"direction":78,"opportunity":79},"提出图注意力-Mamba框架并用中华穿山甲算法优化，实现跨域植物病害识别与严重度估计。","图注意力网络建模局部症状，Mamba捕获长程依赖，CPO调参，PlantVill","PlantVillage准确率99.12%，PlantDoc 95.37%，严重度MAE 0.091","农业人工智能与决策模型","可探索轻量化部署与田间多作物跨域泛化，结合时序数据实现病害动态预警。","智慧农业 \u002F 农业物联网","openalex","2026-10-05T23:30:17.728668Z",{"total":84,"page":22,"page_size":84,"items":85},6,[86,139,191,239,291,347],{"id":87,"title":88,"url":89,"summary":90,"summary_zh":91,"content":9,"source_name":92,"source_url":89,"published_at":93,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":94,"score_detail":95,"sources":99,"tags":101,"primary_topic":103,"secondary_topics":104,"classification":105,"search_phrases":117,"slug":120,"view_count":46,"doi":121,"paper":122,"created_at":138},1617,"MRC-Net: a reliable plant disease classification framework with multi-frequency state-space enhancement and conformal prediction","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1895287","Plant disease image classification is a key task in disease monitoring and precise prevention and control in smart agriculture. However, complex backgrounds, finegrained lesion differences, and model overconfidence still limit recognition performance and practical application reliability. To address these issues, this paper proposes a reliable plant disease recognition framework that integrates multi-frequency selective state-space feature enhancement with conformal-aware reliable prediction. The proposed method adopts Swin-Tiny as the backbone network and uses the MF-SSFE module to jointly model low-frequency leaf structures, high-frequency lesion textures, and cross-region state-space contexts, thereby enhancing disease-related evidence in complex scenarios. Meanwhile, the CARP module is introduced to incorporate the idea of conformal prediction into the classification output process, enabling the model to express uncertainty while providing class predictions. Experimental results show that the proposed method achieves Accuracy values of 0.9891, 0.3889, and 0.9800 on the NGLD, PlantDoc, and PlantVillage datasets, respectively, and AUC values of 0.9995, 0.8690, and 0.9989, respectively, outperforming the comparison methods. Ablation experiments and sensitivity analysis further verify the effectiveness and stability of each module. Model complexity analysis shows that the proposed method maintains acceptable computational overhead while achieving superior recognition performance.","植物病害图像分类是智慧农业中病害监测与精准防控的关键任务。然而，复杂背景、细粒度病斑差异以及模型过度自信等问题仍制约着识别性能与实际应用的可靠性。针对上述问题，本文提出了一种融合多频选择性状态空间特征增强与一致性感知可靠预测的可靠植物病害识别框架。该方法以Swin-Tiny为骨干网络，利用MF-SSFE模块联合建模低频叶片结构、高频病斑纹理及跨区域状态空间上下文，从而增强复杂场景下与病害相关的证据信息。同时，引入CARP模块，将一致性预测的思想融入分类输出过程，使模型在提供类别预测的同时能够表达不确定性。实验结果表明，该方法在NGLD、PlantDoc和PlantVillage数据集上的准确率分别达到0.9891、0.3889和0.9800，AUC值分别达到0.9995、0.8690和0.9989，优于对比方法。消融实验和敏感性分析进一步验证了各模块的有效性与稳定性。模型复杂度分析表明，该方法在保持可接受计算开销的同时，实现了优越的识别性能。","Frontiers in Plant Science","2026-09-03T00:00:00Z",70,{"impact":17,"substance":96,"depth":97,"authority":20,"freshness":46,"relevant":22,"comment":98},20,18,"提出结合多频状态空间与保形预测的植物病害分类框架，在多个数据集上表现优异，兼具可靠性与效率。",[100],{"name":92,"url":89},[27,28,102,29],"深度学习",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":106},{"candidates":107,"agricultural_evidence":47},[108,113],{"code":33,"score":46,"supported":47,"title":109,"abstract":110,"tags":112},[],[27,111],"smart agriculture",[27],{"code":54,"score":63,"supported":14,"title":114,"abstract":115,"tags":116},[],[],[28,29,102],[118,119],"农业人工智能 植物病害识别 智慧农业 深度学习","农业人工智能 植物病害识别","农业人工智能植物病害识别智慧农业深度学习-1617","10.3389\u002Ffpls.2026.1895287",{"doi":121,"openalex_id":123,"authors":124,"venue":92,"cited_by_count":72,"oa_url":89,"card":133,"direction":80,"ingested_from":81},"W7207549269",[125,127,130],{"name":126,"orcid":9},"Shiyao Xie",{"name":128,"orcid":129},"X. H. Zhang","https:\u002F\u002Forcid.org\u002F0009-0003-9957-0002",{"name":131,"orcid":132},"Yang Li","https:\u002F\u002Forcid.org\u002F0000-0002-3006-7420",{"tldr":134,"method":135,"finding":136,"direction":78,"opportunity":137},"提出可靠植物病害分类框架，结合多频状态空间增强与保形预测，提升复杂场景识别精度与可靠性。","Swin-Tiny骨干，MF-SSFE多频特征增强，CARP保形预测模块，在NG","在三个数据集上准确率分别达0.9891、0.3889、0.9800，AUC达0.9995、0.869","可探索将保形预测用于其他农业任务（如产量预测）以量化不确定性，或优化多频特征提取以应对更复杂田间场景。","2026-09-04T23:30:09.854906Z",{"id":140,"title":141,"url":142,"summary":143,"summary_zh":9,"content":9,"source_name":144,"source_url":142,"published_at":145,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":146,"score_detail":147,"sources":151,"tags":153,"primary_topic":156,"secondary_topics":157,"classification":158,"search_phrases":170,"slug":173,"view_count":72,"doi":174,"paper":175,"created_at":190},5539,"Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10473-z","Robust and transparent crop-row detection from RGB field imagery for vision-based agricultural guidance systems。Precision Agriculture","Precision Agriculture","2026-10-22T00:00:00Z",79,{"impact":148,"substance":18,"depth":97,"authority":149,"freshness":13,"relevant":22,"comment":150},16,14,"核心期刊新论文，提出鲁棒且可解释的RGB作物行检测方法，对视觉导航农机具参考价值明确，但属细分技术进展，未达重大突破层级。",[152],{"name":144,"url":142},[27,28,50,154,155],"机器视觉","作物行检测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":159},{"candidates":160,"agricultural_evidence":47},[161,166],{"code":33,"score":162,"supported":47,"title":163,"abstract":164,"tags":165},5,[],[51],[27,50],{"code":54,"score":55,"supported":14,"title":167,"abstract":168,"tags":169},[],[],[28,154],[171,172],"RGB 作物行检测","农业人工智能 作物行检测 智慧农业 机器视觉","RGB作物行检测-5539","10.1007\u002Fs11119-026-10473-z",{"doi":174,"openalex_id":176,"authors":177,"venue":144,"cited_by_count":72,"oa_url":9,"card":9,"direction":9,"ingested_from":81},"W7223117828",[178,181,184,187],{"name":179,"orcid":180},"Esther Gascó","https:\u002F\u002Forcid.org\u002F0000-0003-1798-4483",{"name":182,"orcid":183},"Clara I. López-González","https:\u002F\u002Forcid.org\u002F0000-0002-2755-1692",{"name":185,"orcid":186},"Eva Besada-Portas","https:\u002F\u002Forcid.org\u002F0000-0001-6129-4653",{"name":188,"orcid":189},"Gonzalo Pájares","https:\u002F\u002Forcid.org\u002F0000-0003-0915-6282","2026-10-22T11:20:09.678587Z",{"id":192,"title":193,"url":194,"summary":195,"summary_zh":196,"content":9,"source_name":197,"source_url":194,"published_at":198,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":146,"score_detail":199,"sources":203,"tags":205,"primary_topic":208,"secondary_topics":209,"classification":210,"search_phrases":222,"slug":225,"view_count":72,"doi":226,"paper":227,"created_at":238},5316,"PLANT-GPK v2.7: Spatial Heterogeneity and Predictive Digital Twin for Plant Phenotyping","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23460273","Traditional image-based plant phenotyping assumes spatial homogeneity, estimating a single set of physiological parameters for the entire plant. This paper introduces a novel extension to the PLANT-GPK framework: Spatial Physiological Heatmaps and Predictive Digital Twins. By dividing the plant image into a 3x3 grid and solving the inverse ODE problem for each spatial patch, we reveal the hidden spatial heterogeneity of resource allocation. Furthermore, the recovered parameters are extrapolated to predict the exact day of harvest using a continuous smooth-physics model, providing a powerful prescriptive tool for precision agriculture.","传统的基于图像的植物表型分析假设空间同质性，即对整个植株估计单一组生理参数。本文提出了对PLANT-GPK框架的一种新颖扩展：空间生理热图与预测性数字孪生。通过将植物图像划分为3×3网格并针对每个空间区块求解逆常微分方程（ODE）问题，我们揭示了资源分配中隐藏的空间异质性。此外，利用连续光滑物理模型对所恢复的参数进行外推，以预测确切的收获日期，为精准农业提供了强有力的规范性工具。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-19T00:00:00Z",{"impact":97,"substance":200,"depth":97,"authority":17,"freshness":201,"relevant":22,"comment":202},22,9,"该论文提出植物表型空间异质性建模与预测性数字孪生方法，方法新颖、结论有实用价值，但属细分领域预印本成果，未达重大突破层级。",[204],{"name":197,"url":194},[27,28,206,50,207],"数字孪生","植物表型",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":211},{"candidates":212,"agricultural_evidence":47},[213,218],{"code":33,"score":96,"supported":47,"title":214,"abstract":216,"tags":217},[215],"digital twin",[50,206,51],[206,27,50],{"code":54,"score":22,"supported":14,"title":219,"abstract":220,"tags":221},[],[],[28],[223,224],"PLANT-GPK 植物表型","空间生理热图 数字孪生","PLANT-GPK植物表型-5316","10.5281\u002Fzenodo.23460273",{"doi":226,"openalex_id":228,"authors":229,"venue":197,"cited_by_count":72,"oa_url":194,"card":232,"direction":236,"ingested_from":81},"W7222885652",[230],{"name":231,"orcid":9},"MOHAMMED TAHAR SAIM",{"tldr":233,"method":234,"finding":235,"direction":236,"opportunity":237},"将植物图像分3x3网格反演ODE参数，构建空间生理热图与预测数字孪生。","基于PLANT-GPK框架，分块求解逆ODE问题，并用连续平滑物理模型外推。","揭示了植物资源分配的空间异质性，可预测精确收获日。","农业遥感与作物表型","可探索更细粒度网格与多模态表型融合，提升数字孪生预测精度与田间适用性。","2026-10-19T23:30:19.414490Z",{"id":240,"title":241,"url":242,"summary":243,"summary_zh":9,"content":9,"source_name":244,"source_url":242,"published_at":245,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":246,"score_detail":247,"sources":249,"tags":251,"primary_topic":253,"secondary_topics":254,"classification":255,"search_phrases":271,"slug":274,"view_count":55,"doi":275,"paper":276,"created_at":290},5082,"Entomopathogenic nematode recent applications in precision agriculture: Methods, challenges, and future perspectives","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jip.2026.108765","Entomopathogenic nematode recent applications in precision agriculture: Methods, challenges, and future perspectives。Journal of Invertebrate Pathology","Journal of Invertebrate Pathology","2026-10-14T00:00:00Z",67,{"impact":17,"substance":148,"depth":19,"authority":149,"freshness":21,"relevant":22,"comment":248},"核心期刊综述，聚焦昆虫病原线虫在精准农业中的应用方法与挑战，专业性强但属细分领域，产业影响有限。",[250],{"name":244,"url":242},[27,28,50,252],"生物防治",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":256},{"candidates":257,"agricultural_evidence":47},[258,262,267],{"code":33,"score":20,"supported":47,"title":259,"abstract":260,"tags":261},[51],[51],[27,50],{"code":263,"score":22,"supported":14,"title":264,"abstract":265,"tags":266},"F4",[],[],[252],{"code":54,"score":22,"supported":14,"title":268,"abstract":269,"tags":270},[],[],[28],[272,273],"昆虫病原线虫 精准农业","Journal of Invertebrate Pathology 线虫","昆虫病原线虫精准农业-5082","10.1016\u002Fj.jip.2026.108765",{"doi":275,"openalex_id":277,"authors":278,"venue":244,"cited_by_count":72,"oa_url":9,"card":9,"direction":80,"ingested_from":81},"W7221746877",[279,282,285,287],{"name":280,"orcid":281},"Alperen Kaan Bütüner","https:\u002F\u002Forcid.org\u002F0000-0002-2121-3529",{"name":283,"orcid":284},"Hilal Erdoğan","https:\u002F\u002Forcid.org\u002F0000-0002-0387-2600",{"name":286,"orcid":9},"İsmail Alper Susurluk",{"name":288,"orcid":289},"Edwin E. Lewis","https:\u002F\u002Forcid.org\u002F0000-0002-8825-1562","2026-10-16T23:30:09.556034Z",{"id":292,"title":293,"url":294,"summary":295,"summary_zh":9,"content":9,"source_name":144,"source_url":294,"published_at":245,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":296,"score_detail":297,"sources":299,"tags":301,"primary_topic":305,"secondary_topics":306,"classification":307,"search_phrases":329,"slug":332,"view_count":22,"doi":333,"paper":334,"created_at":346},4973,"Barley head detection using UAV imagery and YOLOv10","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10468-w","Barley head detection using UAV imagery and YOLOv10。Precision Agriculture",75,{"impact":148,"substance":96,"depth":19,"authority":149,"freshness":21,"relevant":22,"comment":298},"将YOLOv10用于无人机大麦穗检测，方法新颖、数据可靠，属细分领域技术进展，值得精选。",[300],{"name":144,"url":294},[27,302,28,303,304],"无人机","遥感","大麦",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":308},{"candidates":309,"agricultural_evidence":47},[310,315,320,324],{"code":33,"score":311,"supported":47,"title":312,"abstract":313,"tags":314},4,[],[51],[27],{"code":316,"score":22,"supported":14,"title":317,"abstract":318,"tags":319},"A5",[],[],[304],{"code":54,"score":22,"supported":14,"title":321,"abstract":322,"tags":323},[],[],[28],{"code":325,"score":22,"supported":14,"title":326,"abstract":327,"tags":328},"I3",[],[],[303],[330,331],"无人机 大麦 穗检测","YOLOv10 大麦 遥感","无人机大麦穗检测-4973","10.1007\u002Fs11119-026-10468-w",{"doi":333,"openalex_id":335,"authors":336,"venue":144,"cited_by_count":72,"oa_url":9,"card":9,"direction":9,"ingested_from":81},"W7221758985",[337,340,343],{"name":338,"orcid":339},"Ameer Tamoor Khan","https:\u002F\u002Forcid.org\u002F0000-0001-6838-992X",{"name":341,"orcid":342},"Saiful Azim","https:\u002F\u002Forcid.org\u002F0000-0002-2686-7150",{"name":344,"orcid":345},"Signe Marie Jensen","https:\u002F\u002Forcid.org\u002F0000-0002-7738-5231","2026-10-15T23:30:02.421798Z",{"id":348,"title":349,"url":350,"summary":351,"summary_zh":9,"content":9,"source_name":352,"source_url":350,"published_at":353,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":200,"score_detail":354,"sources":356,"tags":358,"primary_topic":360,"secondary_topics":361,"classification":362,"search_phrases":373,"slug":376,"view_count":72,"doi":377,"paper":378,"created_at":395},4659,"Platform for Smart Agriculture -Research and Technological Innovation PlaSmA Research Group (PRG) Platform for Smart Agriculture -Research and Technological Innovation","https:\u002F\u002Fdoi.org\u002F10.13140\u002Frg.2.2.22742.61769","Platform for Smart Agriculture -Research and Technological Innovation PlaSmA Research Group (PRG) Platform for Smart Agriculture -Research and Technological Innovation。OpenAlex","OpenAlex","2026-10-10T00:00:00Z",{"impact":63,"substance":55,"depth":55,"authority":84,"freshness":201,"relevant":22,"comment":355},"仅有标题与来源、无实质摘要内容的论文条目，信息增量极低，不建议进入每日精选。",[357],{"name":352,"url":350},[27,28,359],"科研平台",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":363},{"candidates":364,"agricultural_evidence":47},[365,369],{"code":33,"score":17,"supported":47,"title":366,"abstract":367,"tags":368},[111],[111],[27],{"code":54,"score":22,"supported":14,"title":370,"abstract":371,"tags":372},[],[],[28],[374,375],"农业人工智能 智慧农业 科研平台","农业人工智能 智慧农业","农业人工智能智慧农业科研平台-4659","10.13140\u002Frg.2.2.22742.61769",{"doi":377,"openalex_id":379,"authors":380,"venue":9,"cited_by_count":72,"oa_url":350,"card":9,"direction":80,"ingested_from":81},"W7221337880",[381,383,385,387,389,391,393],{"name":382,"orcid":9},"Ing José Antonio",{"name":384,"orcid":9},"Valles Romero",{"name":386,"orcid":9},"Gerardo Manuel",{"name":388,"orcid":9},"Alonzo Medina",{"name":390,"orcid":9},"Emilio Raymundo Morales Maldonado",{"name":392,"orcid":9},"Ing José",{"name":394,"orcid":9},"Antonio Valles","2026-10-11T23:30:07.700109Z"]