[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3122":3,"related-3122":110},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":28,"primary_topic":34,"secondary_topics":40,"classification":41,"search_phrases":78,"slug":81,"view_count":82,"doi":83,"paper":84,"created_at":109},3122,"Advances in Information Sensing and Intelligent Monitoring of Field Crops Throughout the Full Growth Cycle（大田作物全生育期信息感知与智能监测研究进展）","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1852","江苏大学农业工程学院 Tang Ruifan 等在《Agronomy》16(18): 1852 发表综述（2026-09-20 发表）：大田作物在不同生育阶段持续变化、呈现显著空间异质性、需在短作业窗口内进行管理。研究以生育阶段为主线组织文献，通过\"农业需求—可观测变量—感知平台—数据处理方法—验证设计—状态解释—管理或装备输出\"通用链条分析。从卫星遥感、无人机感知、地面与近端感知、田间物联网、机载传感器、多源融合、作物模型与机器学习方法按空间支撑、时间连续性、尺度匹配、田间稳健性、迁移条件、不确定性与操作适用性比较。综述报告作物表型反演、田间环境表征、生物胁迫识别在特定条件下已建立；跨阶段状态继承、一致参考测量、独立验证、监测结果向可执行任务转化仍不充分。提出生命周期导向的信息处理视角，未来应加强跨作物跨区域验证、机理性与数据驱动模型协同、不确定性报告、互操作性和田间反馈。",null,"MDPI Agronomy","2026-09-20T00:00:00Z","论文",25,false,76,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,20,18,13,9,1,"江苏大学团队在核心期刊发表的综述，系统梳理大田作物全生育期感知与监测技术链条，专业深度与信息增量较高，但属学术综述、产业影响有限，适合进入主题聚合而非头条精选。",[24,25],{"name":9,"url":6},{"name":26,"url":27},"Agronomy","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16181852",[29,30,31,32,33],"智慧农业","农业物联网","作物表型","遥感监测","大田作物",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":42,"locked":13,"version":43,"method":44,"evidence":45},"classified","2026-10-22.2","rules",{"candidates":46,"agricultural_evidence":49},[47,54,61,68,73],{"code":35,"score":48,"supported":49,"title":50,"abstract":51,"tags":53},8,true,[],[52,31],"遥感",[31,32],{"code":55,"score":56,"supported":49,"title":57,"abstract":58,"tags":60},"I5",4,[],[59],"物联网",[30],{"code":62,"score":63,"supported":49,"title":64,"abstract":65,"tags":67},"I2",3,[],[66],"机器学习",[],{"code":69,"score":21,"supported":13,"title":70,"abstract":71,"tags":72},"A",[],[],[33],{"code":74,"score":21,"supported":13,"title":75,"abstract":76,"tags":77},"I1",[],[],[29],[79,80],"江苏大学 大田作物 智能监测","Agronomy 作物全生育期 信息感知","江苏大学大田作物智能监测-3122",6,"10.3390\u002Fagronomy16181852",{"doi":83,"openalex_id":85,"authors":86,"venue":26,"cited_by_count":101,"oa_url":27,"card":102,"direction":106,"ingested_from":108},"W7213886235",[87,89,92,94,96,98],{"name":88,"orcid":8},"Ruifan Tang",{"name":90,"orcid":91},"Yapeng Wu","https:\u002F\u002Forcid.org\u002F0009-0008-1808-8959",{"name":93,"orcid":8},"Liming Zhang",{"name":95,"orcid":8},"Youqi Xu",{"name":97,"orcid":8},"Yu Zhang",{"name":99,"orcid":100},"Zhong Tang","https:\u002F\u002Forcid.org\u002F0000-0002-2724-115X",0,{"tldr":103,"method":104,"finding":105,"direction":106,"opportunity":107},"综述大田作物全生育期信息感知与智能监测，按生育阶段梳理技术并指出转化不足。","以生育阶段为主线，比较卫星、无人机、地面物联网、模型与机器学习等方法。","表型反演与胁迫识别已有条件建立，但跨阶段继承、独立验证与可执行转化不足。","农业遥感与作物表型","可研究跨生育阶段状态继承建模、一致参考测量与监测结果向田间作业指令的转化。","openalex","2026-09-22T00:05:38.276747Z",{"total":82,"page":21,"page_size":82,"items":111},[112,195,255,327,374,443],{"id":113,"title":114,"url":115,"summary":116,"summary_zh":117,"content":8,"source_name":118,"source_url":115,"published_at":119,"category":11,"cover_url":8,"hotness":120,"is_selected":13,"score":121,"score_detail":122,"sources":127,"tags":129,"primary_topic":132,"secondary_topics":137,"classification":139,"search_phrases":156,"slug":159,"view_count":160,"doi":161,"paper":162,"created_at":194},2522,"Estimation of Grain Yield and Quality in Awned and Awnless Wheat Genotypes Using UAV and Proximal Multispectral Sensors","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fplants15182819","This study aimed to evaluate the complementary potential of UAV-based and proximal multispectral sensing using the Plant-O-Meter (POM) sensor for the assessment of wheat grain yield and quality across different phenological stages and two growing seasons. The analysis was based on three vegetation indices, the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), and Normalized Difference Red Edge Index (NDRE), and included two morphologically distinct genotype groups, awned and awnless. The study included nine awned and nine awnless genotypes. Vegetation indices showed pronounced seasonal dynamics, with higher values during intensive vegetative development and a decline during later growth stages. The strongest relationship with grain yield was observed for GNDVI-A during the heading to beginning of flowering stage (BBCH 51–61) in Season I (r = 0.85), whereas in Season II, the strongest relationship was observed for NDRE-A during the flag leaf stage (BBCH 37–39) (r = 0.80). Awned genotypes generally showed stronger VI–yield relationships, while associations with grain quality parameters varied among genotype groups and seasons. Linear mixed-effects models showed that both VI-related effects and genotype variability contributed to the variation in grain yield and quality. For yield, marginal R2 was 0.43 in Season I and 0.50 in Season II, while conditional R2 was 0.78 and 0.64, respectively. For protein and wet gluten content, model performance was more variable, with marginal R2 values ranging from 0.30 to 0.39 for protein and from 0.31 to 0.33 for wet gluten. Genotype-level LOGO cross-validation further indicated variation in model performance when genotypes not included in model development were evaluated. Overall, the results indicate that relationships between multispectral vegetation indices and wheat grain yield and quality depend on sensing method, phenological stage, genotype characteristics, and growing season. The findings provide an exploratory basis for the application of multispectral sensing in wheat phenotyping and assessment of grain yield and quality, while further validation across broader genetic and environmental conditions is required.","本研究旨在评估基于无人机与近地多光谱传感（采用Plant-O-Meter（POM）传感器）在不同物候期和两个生长季中对小麦籽粒产量和品质评估的互补潜力。分析基于三个植被指数，即归一化差异植被指数（NDVI）、绿色归一化差异植被指数（GNDVI）和归一化差异红边指数（NDRE），并纳入两个形态学上不同的基因型组，即有芒和无芒。研究包括9个有芒基因型和9个无芒基因型。植被指数表现出明显的季节性动态变化，在旺盛营养发育期数值较高，在生长后期下降。与籽粒产量关系最强的是抽穗至开花初期（BBCH 51–61）第I季的GNDVI-A（r = 0.85），而在第II季，关系最强的是旗叶期（BBCH 37–39）的NDRE-A（r = 0.80）。有芒基因型总体上表现出更强的植被指数-产量关系，而与籽粒品质参数的关联因基因型组和季节而异。线性混合效应模型表明，植被指数相关效应和基因型变异均对籽粒产量和品质的变异有贡献。对于产量，第I季的边际R²为0.43，第II季为0.50，而条件R²分别为0.78和0.64。对于蛋白质和湿面筋含量，模型表现更为多变，蛋白质的边际R²范围为0.30至0.39，湿面筋为0.31至0.33。基因型水平的LOGO交叉验证进一步表明，在评估未纳入模型开发的基因型时，模型表现存在变异。总体而言，结果表明多光谱植被指数与小麦籽粒产量和品质之间的关系取决于传感方法、物候期、基因型特征和生长季。研究结果为多光谱传感在小麦表型分析及籽粒产量和品质评估中的应用提供了探索性依据，但仍需在更广泛的遗传和环境条件下进一步验证。","Plants","2026-09-14T00:00:00Z",10,75,{"impact":123,"substance":124,"depth":125,"authority":19,"freshness":20,"relevant":21,"comment":126},15,21,17,"该研究利用无人机与近地多光谱传感器评估有芒\u002F无芒小麦产量与品质，方法新颖、数据跨两季，对智慧农业遥感育种有参考价值，但属探索性论文，产业影响有限。",[128],{"name":118,"url":115},[29,130,131,31,32],"产量预测","小麦育种",{"code":133,"slug":134,"name":135,"path":136,"parent_code":69},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat",[138],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":42,"locked":13,"version":43,"method":44,"evidence":140},{"candidates":141,"agricultural_evidence":49},[142,146,152],{"code":133,"score":123,"supported":49,"title":143,"abstract":144,"tags":145},[134],[135,134],[131],{"code":35,"score":147,"supported":49,"title":148,"abstract":150,"tags":151},14,[149],"multispectral",[149],[130,31,32],{"code":74,"score":21,"supported":13,"title":153,"abstract":154,"tags":155},[],[],[29],[157,158],"产量预测 作物表型 小麦育种 智慧农业","产量预测 作物表型","产量预测作物表型小麦育种智慧农业-2522",2,"10.3390\u002Fplants15182819",{"doi":161,"openalex_id":163,"authors":164,"venue":118,"cited_by_count":101,"oa_url":115,"card":189,"direction":106,"ingested_from":108},"W7212560710",[165,168,171,174,177,180,183,186],{"name":166,"orcid":167},"Irina Marina","https:\u002F\u002Forcid.org\u002F0000-0002-5894-363X",{"name":169,"orcid":170},"Vesna Kandić","https:\u002F\u002Forcid.org\u002F0000-0003-1999-2030",{"name":172,"orcid":173},"Marko Kostić","https:\u002F\u002Forcid.org\u002F0000-0001-9446-994X",{"name":175,"orcid":176},"Nataša Ljubičić","https:\u002F\u002Forcid.org\u002F0000-0001-5982-9401",{"name":178,"orcid":179},"Biljana Bošković","https:\u002F\u002Forcid.org\u002F0000-0003-4977-3170",{"name":181,"orcid":182},"Kosta Gligorević","https:\u002F\u002Forcid.org\u002F0000-0001-5783-4637",{"name":184,"orcid":185},"Miloš Pajić","https:\u002F\u002Forcid.org\u002F0000-0002-8905-3293",{"name":187,"orcid":188},"Milan Dražić","https:\u002F\u002Forcid.org\u002F0000-0002-0416-5174",{"tldr":190,"method":191,"finding":192,"direction":106,"opportunity":193},"用无人机与近地多光谱传感器评估有芒\u002F无芒小麦产量与品质，比较不同生育期和年份的植被指数关系。","无人机与Plant-O-Meter多光谱传感器，NDVI\u002FGNDVI\u002FNDRE，","GNDVI与NDRE在抽穗至开花期和旗叶期与产量相关性最强，有芒基因型关系更强，模型对品质预测较弱。","可探索多源遥感融合与基因型分层建模，提升跨年份、跨环境的小麦产量与品质预测泛化能力。","2026-09-15T23:30:17.124343Z",{"id":196,"title":197,"url":198,"summary":199,"summary_zh":8,"content":8,"source_name":200,"source_url":8,"published_at":201,"category":11,"cover_url":8,"hotness":120,"is_selected":13,"score":202,"score_detail":203,"sources":206,"tags":208,"primary_topic":212,"secondary_topics":213,"classification":221,"search_phrases":242,"slug":245,"view_count":21,"doi":8,"paper":246,"created_at":254},5535,"基于无人机多光谱图像与VGG21模型的小麦渍害调控效果识别","https:\u002F\u002Ffinance.sina.cn\u002F2026-10-16\u002Fdetail-inivmprm8363597.d.html","江苏省农科院农业信息研究所联合中国农科院环发所与湖北省农科院粮作所，在小麦拔节至抽穗、抽穗至灌浆两个阶段设置对照、渍水胁迫、硅肥与氨基酸调控试验，用大疆精灵4采集冠层多光谱影像。VGG21模型对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","智慧农业（中英文）2026-10-21","2026-10-21T00:00:00Z",82,{"impact":18,"substance":204,"depth":18,"authority":147,"freshness":120,"relevant":21,"comment":205},22,"省级科研机构联合攻关，方法新颖、数据扎实，对渍害遥感识别与调控评估有实质参考价值，值得入选。",[207],{"name":200,"url":198},[29,209,31,210,211],"农业遥感","无人机多光谱","小麦渍害",{"code":133,"slug":134,"name":135,"path":136,"parent_code":69},[214,215],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"code":216,"slug":217,"name":218,"path":219,"parent_code":220},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":42,"locked":13,"version":43,"method":44,"evidence":222},{"candidates":223,"agricultural_evidence":49},[224,229,233,238],{"code":133,"score":225,"supported":49,"title":226,"abstract":227,"tags":228},11,[135],[135],[],{"code":35,"score":225,"supported":49,"title":230,"abstract":231,"tags":232},[210],[],[31,209,210],{"code":216,"score":48,"supported":49,"title":234,"abstract":236,"tags":237},[235],"渍害",[],[],{"code":74,"score":21,"supported":13,"title":239,"abstract":240,"tags":241},[],[],[29],[243,244],"江苏省农科院 小麦渍害 多光谱","VGG21 小麦 硅肥调控","江苏省农科院小麦渍害多光谱-5535",{"doi":8,"openalex_id":8,"authors":247,"venue":8,"cited_by_count":101,"oa_url":8,"card":248,"direction":106,"ingested_from":253},[],{"tldr":249,"method":250,"finding":251,"direction":106,"opportunity":252},"用无人机多光谱图像和VGG21模型识别小麦渍害后硅肥与氨基酸调控效果。","大疆精灵4采集冠层多光谱影像，构建VGG21深度学习分类模型。","VGG21对硅肥调控识别正确率超91%，精度96.42%、召回率89.53%。","可扩展多生育期、多调控组合与轻量化模型，探索渍害胁迫等级与调控效果的同步识别。","agent","2026-10-22T00:07:10.070916Z",{"id":256,"title":257,"url":258,"summary":259,"summary_zh":260,"content":8,"source_name":261,"source_url":258,"published_at":262,"category":11,"cover_url":8,"hotness":120,"is_selected":13,"score":263,"score_detail":264,"sources":266,"tags":268,"primary_topic":272,"secondary_topics":273,"classification":275,"search_phrases":298,"slug":301,"view_count":101,"doi":302,"paper":303,"created_at":326},5367,"Integration of multi-source UAV remote sensing features and derived features for winter wheat aboveground biomass estimation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102656","Accurate estimation of winter wheat ( Triticum aestivum L.) aboveground biomass (AGB) is important for crop growth monitoring and field management. However, unmanned aerial vehicle (UAV)-based AGB estimation remains challenging due to variations in canopy conditions across growth stages, and different remote sensing features can capture complementary characteristics related to AGB estimation. This study integrated multi-source UAV-derived features, including vegetation indices (VIs), texture features (TFs), light detection and ranging (LiDAR)-derived features, and derived features (DFs) generated from the original feature sets, with four machine learning algorithms for winter wheat AGB estimation. Results showed that TFs provided better prediction performance than other single-source feature sets. Compared with single-source feature sets, the integration of VIs, TFs, LiDAR-derived features, and DFs further improved AGB estimation, and the random forest (RF) model achieved the best performance among the evaluated models (R² = 0.923, RMSE = 1.731 Mg ha⁻¹). Incremental R² analysis based on a stage-only baseline model showed that the RF model explained additional variation in AGB beyond growth-stage information, with an incremental R² of 0.479. Stage-specific evaluation indicated that model performance varied among growth stages, with the highest accuracy observed at the booting stage (R² = 0.632, RMSE = 1.425 Mg ha⁻¹). Feature contribution analysis based on the full-feature fusion model using all growth-stage data identified canopy coverage and several DFs as important predictors, while stage-specific analyses showed differences in dominant predictors across growth stages. The integration of multi-source UAV-derived features and DFs with machine learning provides an effective approach for winter wheat AGB estimation, while model performance remains strongly dependent on growth stage.","准确估算冬小麦（Triticum aestivum L.）地上生物量（aboveground biomass, AGB）对作物生长监测和田间管理具有重要意义。然而，由于不同生育期冠层条件存在差异，且不同遥感特征能够捕捉与AGB估算相关的互补信息，基于无人机（unmanned aerial vehicle, UAV）的AGB估算仍面临挑战。本研究将多源无人机衍生特征，包括植被指数（vegetation indices, VIs）、纹理特征（texture features, TFs）、激光雷达（light detection and ranging, LiDAR）衍生特征以及由原始特征集生成的衍生特征（derived features, DFs），与四种机器学习算法相结合，用于冬小麦AGB估算。结果表明，TFs的预测性能优于其他单一来源特征集。与单一来源特征集相比，融合VIs、TFs、LiDAR衍生特征和DFs进一步提高了AGB估算精度，其中随机森林（random forest, RF）模型在所评估的模型中表现最佳（R² = 0.923，RMSE = 1.731 Mg ha⁻¹）。基于仅含生育期信息的基线模型进行的增量R²分析表明，RF模型在生育期信息之外解释了AGB的额外变异，增量R²为0.479。分生育期评估表明，模型性能在不同生育期之间存在差异，其中在孕穗期观察到最高精度（R² = 0.632，RMSE = 1.425 Mg ha⁻¹）。基于使用全部生育期数据的全特征融合模型进行的特征贡献分析识别出冠层覆盖度和若干DFs为重要预测因子，而分生育期分析则表明不同生育期的主导预测因子存在差异。将多源无人机衍生特征和DFs与机器学习相结合，为冬小麦AGB估算提供了一种有效方法，但模型性能仍强烈依赖于生育期。","Smart Agricultural Technology","2026-10-17T00:00:00Z",77,{"impact":16,"substance":204,"depth":18,"authority":19,"freshness":48,"relevant":21,"comment":265},"该研究融合无人机多源遥感特征与衍生特征，用随机森林估算冬小麦地上生物量，方法新颖、结论可靠，对作物长势监测有参考价值。",[267],{"name":261,"url":258},[29,269,32,270,271],"无人机","冬小麦","生物量估算",{"code":133,"slug":134,"name":135,"path":136,"parent_code":69},[274],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":42,"locked":13,"version":43,"method":44,"evidence":276},{"candidates":277,"agricultural_evidence":49},[278,284,289,294],{"code":133,"score":279,"supported":49,"title":280,"abstract":282,"tags":283},26,[281,134],"winter wheat",[135,281,134],[270],{"code":35,"score":16,"supported":49,"title":285,"abstract":287,"tags":288},[286],"remote sensing",[52,286],[271,32],{"code":62,"score":82,"supported":49,"title":290,"abstract":291,"tags":293},[],[66,292],"machine learning",[],{"code":74,"score":21,"supported":13,"title":295,"abstract":296,"tags":297},[],[],[29],[299,300],"无人机 多源遥感 冬小麦 生物量","冬小麦 地上生物量 随机森林","无人机多源遥感冬小麦生物量-5367","10.1016\u002Fj.atech.2026.102656",{"doi":302,"openalex_id":304,"authors":305,"venue":261,"cited_by_count":101,"oa_url":258,"card":321,"direction":106,"ingested_from":108},"W7222806066",[306,308,310,312,315,317,319],{"name":307,"orcid":8},"Guangtao Wang",{"name":309,"orcid":8},"Sumei Yao",{"name":311,"orcid":8},"Yanzheng Xie",{"name":313,"orcid":314},"Anzhen Qin","https:\u002F\u002Forcid.org\u002F0000-0002-2550-0015",{"name":316,"orcid":8},"Xiangdong Chen",{"name":318,"orcid":8},"Yuhao Zhang",{"name":320,"orcid":8},"Lina Jiang",{"tldr":322,"method":323,"finding":324,"direction":106,"opportunity":325},"融合无人机多源遥感特征与衍生特征，用机器学习估算冬小麦地上生物量。","无人机植被指数、纹理、LiDAR特征及衍生特征结合四种机器学习算法建模。","多源特征融合优于单一特征，随机森林最优（R²=0.923），精度随生育期变化。","可探索生育期自适应建模与特征选择，提升跨期生物量估算的稳健性与可迁移性。","2026-10-20T23:30:03.106267Z",{"id":328,"title":329,"url":330,"summary":331,"summary_zh":8,"content":8,"source_name":332,"source_url":8,"published_at":333,"category":11,"cover_url":8,"hotness":120,"is_selected":13,"score":14,"score_detail":334,"sources":336,"tags":338,"primary_topic":339,"secondary_topics":340,"classification":343,"search_phrases":362,"slug":365,"view_count":101,"doi":8,"paper":366,"created_at":373},5296,"雷添杰研究员团队《基于无人机多光谱图像和VGG21模型的小麦渍害调控效果识别》","https:\u002F\u002Ffinance.sina.com.cn\u002Froll\u002F2026-10-16\u002Fdoc-inivmprm8363597.shtml","《智慧农业(中英文)》2026年第8卷第4期刊载梁万杰、刘晓君、雷添杰等论文,在小麦拔节—抽穗和抽穗—灌浆两个生长阶段进行对照、渍水胁迫、硅肥调控、氨基酸调控控制试验;利用大疆精灵4多光谱无人机采集小麦冠层多光谱图像建立数据集,基于VGG21模型构建识别模型,发现硅肥调控样本识别性能最好,识别精度最高,具良好鲁棒性和可扩展性,可为小麦渍害综合防控提供理论与技术支持。","智慧农业(中英文)(2026,8(4))","2026-10-16T00:00:00Z",{"impact":16,"substance":124,"depth":125,"authority":147,"freshness":48,"relevant":21,"comment":335},"核心期刊论文，方法新颖、结论可靠，对小麦渍害防控有实用价值，值得进入每日精选。",[337],{"name":332,"url":330},[29,209,31,210,211],{"code":133,"slug":134,"name":135,"path":136,"parent_code":69},[341,342],{"code":216,"slug":217,"name":218,"path":219,"parent_code":220},{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":42,"locked":13,"version":43,"method":44,"evidence":344},{"candidates":345,"agricultural_evidence":49},[346,350,354,358],{"code":133,"score":225,"supported":49,"title":347,"abstract":348,"tags":349},[135],[135],[],{"code":216,"score":225,"supported":49,"title":351,"abstract":352,"tags":353},[235],[235],[],{"code":35,"score":225,"supported":49,"title":355,"abstract":356,"tags":357},[210],[],[31,209,210],{"code":74,"score":56,"supported":49,"title":359,"abstract":360,"tags":361},[],[29],[29],[363,364],"雷添杰 小麦渍害 无人机多光谱","VGG21 小麦渍害 识别模型","雷添杰小麦渍害无人机多光谱-5296",{"doi":8,"openalex_id":8,"authors":367,"venue":8,"cited_by_count":101,"oa_url":8,"card":368,"direction":106,"ingested_from":253},[],{"tldr":369,"method":370,"finding":371,"direction":106,"opportunity":372},"用无人机多光谱图像和VGG21模型识别小麦渍害调控效果。","大疆精灵4采集冠层多光谱图像，构建VGG21识别模型。","硅肥调控样本识别性能最好、精度最高，模型鲁棒可扩展。","可探索多生育期多光谱特征与不同调控措施的耦合机制，并扩展到其他作物渍害监测。","2026-10-19T00:07:48.071055Z",{"id":375,"title":376,"url":377,"summary":378,"summary_zh":379,"content":8,"source_name":380,"source_url":377,"published_at":333,"category":11,"cover_url":8,"hotness":120,"is_selected":13,"score":381,"score_detail":382,"sources":385,"tags":387,"primary_topic":390,"secondary_topics":391,"classification":392,"search_phrases":412,"slug":415,"view_count":21,"doi":416,"paper":417,"created_at":442},5162,"Detection of Eulecanium giganteum Infestation in Jujube Orchards Based on UAV Multispectral Remote Sensing","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fhorticulturae12101286","The application of remote sensing technology to monitor agricultural pests and diseases provides an effective approach for rapid and non-destructive assessment of crop health. This study evaluated a UAV multispectral remote sensing framework integrating vegetation indices and SPAD information for assessing Eulecanium giganteum infestation in jujube orchards. Initially, 24 vegetation indices were extracted from canopy multispectral imagery, and Pearson correlation analysis was used for feature screening, resulting in a refined subset of 19 vegetation indices. Seven machine learning algorithms were evaluated during model development, with Grid Search combined with tree-level grouped 7-fold cross-validation for hyperparameter selection. Vegetation indices were used as predictor variables, while SPAD value and infestation severity were treated as response variables in the regression and classification tasks, respectively. During internal model development, the CatBoost-19opt model achieved an R2 of 0.81 and an RMSE of 1.84 for SPAD estimation. On the independent test set consisting of 58 jujube trees, the previously selected model achieved an R2 of 0.84 and an RMSE of 1.56. During internal model development, incorporating measured SPAD values improved pest classification performance, with XGBoost-19sopt achieving an OA of 0.89 and a Kappa coefficient of 0.84. On the independent test set, the measured-SPAD configuration achieved an OA of 0.88, whereas both the VI-only and VI combined with predicted-SPAD configurations achieved an OA of 0.71. Thus, incorporating predicted SPAD did not improve overall classification accuracy relative to using vegetation indices alone. Further model optimization and additional validation across different orchards, growing seasons, and phenological stages are required before the approach can be considered for operational large-scale monitoring.","遥感技术在农业病虫害监测中的应用，为作物健康的快速、无损评估提供了有效途径。本研究评估了一种集成植被指数与SPAD信息的无人机多光谱遥感框架，用于评估枣园中枣球蜡蚧（Eulecanium giganteum）的危害情况。首先，从冠层多光谱影像中提取了24个植被指数，并采用Pearson相关分析进行特征筛选，最终得到包含19个植被指数的精简子集。在模型开发过程中评估了7种机器学习算法，采用网格搜索结合树级分组7折交叉验证进行超参数选择。以植被指数作为预测变量，分别将SPAD值和危害程度作为回归任务和分类任务的响应变量。在内部模型开发中，CatBoost-19opt模型在SPAD估算中取得了R²为0.81、RMSE为1.84的结果。在由58株枣树构成的独立测试集上，先前选定的模型取得了R²为0.84、RMSE为1.56的结果。在内部模型开发中，引入实测SPAD值提升了病虫害分类性能，XGBoost-19sopt取得了OA为0.89、Kappa系数为0.84的结果。在独立测试集上，实测SPAD配置取得了OA为0.88的结果，而仅使用植被指数以及植被指数结合预测SPAD的配置均取得了OA为0.71的结果。因此，相对于仅使用植被指数，引入预测SPAD并未提升整体分类精度。在该方法可被视为适用于业务化大规模监测之前，仍需进一步的模型优化，并在不同枣园、生长季和物候期开展额外验证。","Horticulturae",73,{"impact":383,"substance":204,"depth":125,"authority":19,"freshness":20,"relevant":21,"comment":384},12,"方法扎实、数据完整的单果园研究，但作者自述尚未达到大规模业务化监测水平，属细分领域进展，可入精选但非重大突破。",[386],{"name":380,"url":377},[29,269,32,388,389],"病虫害防治","枣树",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":13,"version":43,"method":44,"evidence":393},{"candidates":394,"agricultural_evidence":49},[395,399,403,408],{"code":35,"score":279,"supported":49,"title":396,"abstract":397,"tags":398},[286,149],[52,210,286,149],[32],{"code":62,"score":82,"supported":49,"title":400,"abstract":401,"tags":402},[],[66,292],[],{"code":404,"score":63,"supported":49,"title":405,"abstract":406,"tags":407},"B2",[],[389],[],{"code":74,"score":21,"supported":13,"title":409,"abstract":410,"tags":411},[],[],[29],[413,414],"无人机 多光谱 枣园 虫害","Eulecanium giganteum 遥感 监测","无人机多光谱枣园虫害-5162","10.3390\u002Fhorticulturae12101286",{"doi":416,"openalex_id":418,"authors":419,"venue":380,"cited_by_count":101,"oa_url":377,"card":437,"direction":106,"ingested_from":108},"W7222575708",[420,422,424,427,430,432,434],{"name":421,"orcid":8},"Yueteng Chao",{"name":423,"orcid":8},"Yulong Wang",{"name":425,"orcid":426},"Zhuoyang Xu","https:\u002F\u002Forcid.org\u002F0000-0003-0344-3424",{"name":428,"orcid":429},"Ruohong He","https:\u002F\u002Forcid.org\u002F0009-0000-7404-9112",{"name":431,"orcid":8},"Rina Yangdao",{"name":433,"orcid":8},"Hongqiang Dong",{"name":435,"orcid":436},"Ping Li","https:\u002F\u002Forcid.org\u002F0009-0007-4465-1023",{"tldr":438,"method":439,"finding":440,"direction":106,"opportunity":441},"用无人机多光谱植被指数与SPAD估算枣园大球蚧发生程度。","24个植被指数筛选19个，7种机器学习+网格搜索与分组交叉验证。","CatBoost估SPAD R²达0.84；实测SPAD提升分类，预测SPAD无增益。","可探索预测SPAD失效原因及跨果园、物候期泛化与多源数据融合监测。","2026-10-17T23:30:28.025984Z",{"id":444,"title":445,"url":446,"summary":447,"summary_zh":448,"content":8,"source_name":449,"source_url":446,"published_at":450,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":263,"score_detail":451,"sources":453,"tags":458,"primary_topic":462,"secondary_topics":463,"classification":464,"search_phrases":506,"slug":509,"view_count":101,"doi":510,"paper":511,"created_at":533},4814,"Wearable Sensors for Smart Agriculture: From Sensing Mechanisms to Crop Phenotyping and Harvesting Applications","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16202210","Global food security faces severe pressures from population growth, climate change and resource shortages. Traditional agricultural monitoring relies on manual sampling and offline laboratory testing, which involve considerable time delays and cannot realize continuous, in situ dynamic tracking of crop physiological status, field environment and agricultural product quality. Among the various sensing platforms for agriculture, flexible wearable sensors represent an important branch, featuring light weight, conformability, low power consumption and real-time response. It should be noted that rigid-substrate and silicon-based sensors remain indispensable in many agricultural applications, and this review focuses specifically on flexible wearable sensors while acknowledging the broader landscape of agricultural sensing technologies. Through conformal contact with plant surfaces or wearable integration, they can convert physical, chemical and biological signals of agroecosystems into readable electrical\u002Foptical signals via specific sensing transduction mechanisms. This review systematically categorizes wearable sensors for smart agriculture into two main types, optical and electrical, and elaborates their intrinsic sensing principles, summarizes typical application scenarios covering crop growth monitoring, post-harvest harvesting and agricultural product safety detection, and introduces the full-flow signal preprocessing, feature extraction and intelligent analysis technologies matched with wearable sensing data. Finally, the bottlenecks restricting large-scale field deployment of current wearable sensors are summarized, and the development directions of multi-modal integrated wearable sensing, biomimetic nanosensors inspired by natural plant sensing systems, and artificial intelligence (AI)-assisted agricultural monitoring platforms are prospected, aiming to provide a theoretical reference for the design optimization and popularization of agricultural wearable sensing equipment.","全球粮食安全面临人口增长、气候变化和资源短缺的严峻压力。传统农业监测依赖人工采样和离线实验室检测，存在较大的时间延迟，无法实现对作物生理状态、田间环境和农产品品质的连续、原位动态追踪。在各类农业传感平台中，柔性可穿戴传感器是一个重要分支，具有轻量、共形、低功耗和实时响应等特点。需要指出的是，刚性基底和硅基传感器在许多农业应用中仍不可或缺，本综述聚焦于柔性可穿戴传感器，同时承认农业传感技术的更广泛格局。通过与植物表面的共形接触或可穿戴集成，柔性可穿戴传感器能够借助特定的传感转导机制，将农业生态系统的物理、化学和生物信号转化为可读的电信号\u002F光信号。本综述将面向智慧农业的可穿戴传感器系统性地分为光学和电学两大类，阐述其内在传感原理，总结涵盖作物生长监测、采后收获和农产品安全检测的典型应用场景，并介绍与可穿戴传感数据匹配的全流程信号预处理、特征提取和智能分析技术。最后，总结了制约当前可穿戴传感器大规模田间部署的瓶颈，并展望了多模态集成可穿戴传感、受自然植物传感系统启发的仿生纳米传感器以及人工智能（AI）辅助农业监测平台的发展方向，旨在为农业可穿戴传感设备的设计优化与推广应用提供理论参考。","Agriculture","2026-10-11T00:00:00Z",{"impact":18,"substance":17,"depth":18,"authority":19,"freshness":48,"relevant":21,"comment":452},"系统综述柔性可穿戴传感在智慧农业中的机理与应用，方法梳理扎实，对农业信息化领域有参考价值。",[454,455],{"name":449,"url":446},{"name":456,"url":457},"MDPI Agriculture 16(20) 2210 2026-10-11","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F20\u002F2210",[29,459,31,460,461],"农业人工智能","农产品安全","可穿戴传感器",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":13,"version":43,"method":44,"evidence":465},{"candidates":466,"agricultural_evidence":49},[467,472,477,484,490,495,500],{"code":74,"score":123,"supported":49,"title":468,"abstract":470,"tags":471},[469],"smart agriculture",[29,469],[29],{"code":35,"score":20,"supported":49,"title":473,"abstract":475,"tags":476},[474],"crop phenotyping",[],[31],{"code":478,"score":82,"supported":49,"title":479,"abstract":480,"tags":483},"A8",[],[481,482],"粮食安全","food security",[],{"code":216,"score":82,"supported":49,"title":485,"abstract":486,"tags":489},[],[487,488],"气候变化","climate change",[],{"code":62,"score":56,"supported":49,"title":491,"abstract":492,"tags":494},[],[493],"artificial intelligence",[459],{"code":55,"score":56,"supported":49,"title":496,"abstract":497,"tags":499},[],[498],"农业传感",[461],{"code":501,"score":63,"supported":49,"title":502,"abstract":503,"tags":505},"H6",[],[504],"农业生态",[],[507,508],"可穿戴传感器 智慧农业","作物表型 传感器","可穿戴传感器智慧农业-4814","10.3390\u002Fagriculture16202210",{"doi":510,"openalex_id":512,"authors":513,"venue":449,"cited_by_count":101,"oa_url":446,"card":527,"direction":532,"ingested_from":108},"W7221545913",[514,516,519,521,523,525],{"name":515,"orcid":8},"Liuyang Qiu",{"name":517,"orcid":518},"Xindong Wang","https:\u002F\u002Forcid.org\u002F0000-0002-8101-7300",{"name":520,"orcid":8},"Xinxin Wu",{"name":522,"orcid":8},"Jie Yu",{"name":524,"orcid":8},"Hao Li",{"name":526,"orcid":8},"Xinai Zhang",{"tldr":528,"method":529,"finding":530,"direction":106,"opportunity":531},"综述柔性可穿戴传感器在智慧农业中的传感机制、作物表型监测与采收应用。","综述光学与电学两类柔性可穿戴传感器原理、应用及信号智能分析技术。","可穿戴传感器可实现作物生理、环境与品质的连续原位监测，但田间规模化部署仍受限。","多模态集成、仿生纳米传感与AI辅助平台是突破田间规模化监测瓶颈的关键方向。","智慧农业 \u002F 农业物联网","2026-10-13T23:30:12.054798Z"]