[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2860":3,"related-2860":91},{"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":62,"slug":65,"view_count":66,"doi":67,"paper":68,"created_at":90},2860,"Optimization of fish feed formulation and pH regulation in on-demand coupled aquaponic systems: A model-based framework for sustainable nutrient and water management","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112358","Optimization of fish feed formulation and pH regulation in on-demand coupled aquaponic systems: A model-based framework for sustainable nutrient and water management。Computers and Electronics in Agriculture","按需耦合鱼菜共生系统中鱼饲料配方与pH调控的优化：面向可持续养分与水管理的模型框架。《农业中的计算机与电子学》",null,"Computers and Electronics in Agriculture","2026-09-17T00:00:00Z","论文",10,false,73,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,18,14,9,1,"模型驱动的鱼菜共生饲料与pH协同优化研究，方法新颖但属细分领域学术进展，产业影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","水产养殖","循环水养殖","精准投喂","水质调控",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,57],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},3,true,[],[50],"饲料",[],{"code":53,"score":22,"supported":14,"title":54,"abstract":55,"tags":56},"D",[],[],[28],{"code":58,"score":22,"supported":14,"title":59,"abstract":60,"tags":61},"I1",[],[],[27],[63,64],"鱼菜共生 饲料配方 pH调控","循环水养殖 智慧农业 水产养殖 水质调控","鱼菜共生饲料配方pH调控-2860",4,"10.1016\u002Fj.compag.2026.112358",{"doi":67,"openalex_id":69,"authors":70,"venue":10,"cited_by_count":82,"oa_url":6,"card":83,"direction":87,"ingested_from":89},"W7213511453",[71,73,76,79],{"name":72,"orcid":9},"Patrick Nestler",{"name":74,"orcid":75},"Christopher Shaw","https:\u002F\u002Forcid.org\u002F0000-0002-3521-0046",{"name":77,"orcid":78},"Werner Kloas","https:\u002F\u002Forcid.org\u002F0000-0001-8905-183X",{"name":80,"orcid":81},"Stefan Streif","https:\u002F\u002Forcid.org\u002F0000-0002-3398-1226",0,{"tldr":84,"method":85,"finding":86,"direction":87,"opportunity":88},"构建模型框架优化按需耦合鱼菜共生系统的鱼饲料配方与pH调控，实现养分和水可持续管理。","基于模型的优化框架，耦合鱼饲料配方与pH调节，用于按需耦合鱼菜共生系统。","模型框架可优化饲料配方与pH调控，提升养分利用效率并减少水资源消耗。","农业绿色发展与碳","可结合实时传感器数据与机器学习，开发动态自适应调控模型，拓展至多物种共生系统。","openalex","2026-09-18T23:30:01.449988Z",{"total":92,"page":22,"page_size":92,"items":93},6,[94,198,286,357,397,460],{"id":95,"title":96,"url":97,"summary":98,"summary_zh":99,"content":9,"source_name":100,"source_url":97,"published_at":101,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":102,"score_detail":103,"sources":107,"tags":109,"primary_topic":114,"secondary_topics":120,"classification":128,"search_phrases":165,"slug":168,"view_count":82,"doi":169,"paper":170,"created_at":197},5573,"Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128375","Accurate estimation of nitrogen (N) concentration in alfalfa ( Medicago sativa L .) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance ( R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.","准确估算紫花苜蓿（Medicago sativa L.）的氮浓度有助于饲草品质评估和收获期安排。经验性遥感模型常因三维冠层结构的混杂效应而出现过拟合。引入结构参数可以稳定生化反演，但同步开展原位形态学测量难以大规模获取。我们开发了形态感知潜在嵌入网络（Morphology-Aware Latent Embedding Network，MALENet），这是一个将生理与形态机制同高光谱数据相结合的混合框架。农业技术转移决策支持系统（Decision Support System for Agrotechnology Transfer，DSSAT）与PROSAIL辐射传输模型耦合，生成了结构信息丰富的合成高光谱先验数据。在此基础上，预训练了一个带有挤压—激励（Squeeze-and-Excitation，SE）注意力机制的卷积神经网络（convolutional neural network，CNN），将生态生理学知识提炼为稠密潜在表征。随后，将这一机制性导出的嵌入与在不同田间条件下采集的经验性高光谱测量数据相融合。利用河西走廊多年田间样方进行的评估显示，相较于传统机器学习和深度学习模型（LASSO-ANN：R² = 0.61；CNN：R² = 0.66），预测性能有所提升（R² = 0.71，RMSE = 0.30%）。特征归因表明，MALENet利用近红外多次散射（归因占比58.3%）作为结构代理，以校准氮浓度反演，抵御冠层结构的混杂效应。尽管受样本量和PROSAIL一维冠层假设的限制，这些发现表明，物理引导的深度学习有潜力利用结构信息，在精准饲草管理中进行农业性状反演。","European Journal of Agronomy","2026-10-21T00:00:00Z",80,{"impact":104,"substance":105,"depth":19,"authority":20,"freshness":13,"relevant":22,"comment":106},16,22,"该研究将作物模型与辐射传输模型耦合生成结构先验，并结合无人机高光谱数据反演苜蓿氮素，方法新颖、田间验证扎实，对精准饲草管理有参考价值，值得进入每日精选。",[108],{"name":100,"url":97},[27,110,111,112,113],"农业人工智能","农业遥感","氮素监测","紫花苜蓿",{"code":115,"slug":116,"name":117,"path":118,"parent_code":119},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[121,127],{"code":122,"slug":123,"name":124,"path":125,"parent_code":126},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":129},{"candidates":130,"agricultural_evidence":47},[131,138,145,151,161],{"code":122,"score":19,"supported":47,"title":132,"abstract":134,"tags":137},[133],"hyperspectral",[135,136,133],"遥感","remote sensing",[111],{"code":115,"score":139,"supported":47,"title":140,"abstract":142,"tags":144},15,[141],"alfalfa",[143,141],"苜蓿",[113],{"code":33,"score":13,"supported":47,"title":146,"abstract":147,"tags":150},[],[148,143,149],"饲草","forage",[113],{"code":152,"score":13,"supported":47,"title":153,"abstract":154,"tags":160},"I2",[],[155,156,157,158,159],"深度学习","机器学习","machine learning","deep learning","decision support",[110],{"code":58,"score":22,"supported":14,"title":162,"abstract":163,"tags":164},[],[],[27],[166,167],"DSSAT PROSAIL 苜蓿 氮素","无人机 高光谱 苜蓿","DSSATPROSAIL苜蓿氮素-5573","10.1016\u002Fj.eja.2026.128375",{"doi":169,"openalex_id":171,"authors":172,"venue":100,"cited_by_count":82,"oa_url":97,"card":191,"direction":195,"ingested_from":89},"W7223154576",[173,175,177,179,181,184,186,189],{"name":174,"orcid":9},"Shuai Fu",{"name":176,"orcid":9},"Yunhao Li",{"name":178,"orcid":9},"Chunli Miao",{"name":180,"orcid":9},"Jie Liu",{"name":182,"orcid":183},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":185,"orcid":9},"Yongjia Hu",{"name":187,"orcid":188},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":190,"orcid":9},"吴彩霞",{"tldr":192,"method":193,"finding":194,"direction":195,"opportunity":196},"构建MALENet融合DSSAT-PROSAIL结构先验与无人机高光谱，提升苜蓿氮浓度估算精度。","DSSAT耦合PROSAIL生成合成高光谱先验，预训练CNN-SE网络，再融合田","MALENet精度优于传统机器学习和深度学习模型，R²达0.71，近红外多次散射作为结构代理贡献58","农业遥感与作物表型","可探索将作物模型结构先验迁移至其他牧草或作物生化参数反演，并缓解PROSAIL一维冠层假设局限。","2026-10-22T11:20:39.622844Z",{"id":199,"title":200,"url":201,"summary":202,"summary_zh":203,"content":9,"source_name":204,"source_url":201,"published_at":205,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":206,"score_detail":207,"sources":211,"tags":213,"primary_topic":218,"secondary_topics":219,"classification":220,"search_phrases":238,"slug":241,"view_count":22,"doi":242,"paper":243,"created_at":285},4878,"The innovative application of plasma technology in food and medicine homology resources","https:\u002F\u002Fdoi.org\u002F10.26599\u002Ffmhr.2026.9740001","Abstract Food and medicine homology resources are increasingly constrained by climate-driven production instability, chemical-residue control, postharvest deterioration, and the demand for function-oriented products. Low-temperature plasma (LTP), also referred to in the literature as cold plasma or non-thermal plasma, is a non-equilibrium physical technology that generates reactive oxygen species (ROS), reactive nitrogen species (RNS), charged particles, electric fields, and ultraviolet (UV) photons under ambient or near-ambient bulk-temperature conditions. This review establishes an end-to-end LTP application framework for plant-, animal-, and microbial-derived food and medicine homology resources. Different from single-sector reviews, the present framework distinguishes substrate-specific regulatory logic across the whole value chain. In plant-derived resources, LTP mainly acts through physical signal-mediated stress priming, secondary metabolism regulation, microbiome modulation, postharvest quality maintenance, detoxification, sensory improvement, and waste valorization. In animal-derived resources, its application logic is centered on environmental biosecurity, feed molecular modification, protein and heme chemistry, and low-loss preservation. In microbial-derived resources, LTP is closely associated with mutagenesis breeding, cell-wall remodeling, community selection, metabolic reprogramming, and biomanufacturing efficiency. The main bottlenecks remain the absence of standardized parameter databases, incomplete understanding of plasma-biomolecule interactions, insufficient scale-up of continuous equipment, and inadequate safety evaluation systems for food and traditional medicinal materials. Future research should prioritize substrate-specific parameter standardization, multi-omics-based mechanistic elucidation, and integration with smart agriculture, precision nutrition, circular economy, and AI-assisted process control. Overall, LTP should be regarded not only as a sterilization or preservation tool, but also as a programmable physical-regulatory platform for full-chain quality formation and high-value utilization of food and medicine homology resources.","食药同源资源日益受到气候驱动的生产不稳定、化学残留控制、采后劣变以及功能导向产品需求的制约。低温等离子体（low-temperature plasma, LTP），在文献中亦称冷等离子体或非热等离子体，是一种非平衡物理技术，可在环境或近环境体相温度条件下产生活性氧（ROS）、活性氮（RNS）、带电粒子、电场和紫外（UV）光子。本综述构建了面向植物源、动物源和微生物源食药同源资源的端到端LTP应用框架。与单一领域综述不同，本框架区分了全价值链中基于基质特异性的调控逻辑。在植物源资源中，LTP主要通过物理信号介导的胁迫 priming、次生代谢调控、微生物组调节、采后品质维持、脱毒、感官改善和废弃物增值化发挥作用。在动物源资源中，其应用逻辑集中于环境生物安全、饲料分子修饰、蛋白质与血红素化学以及低损耗保鲜。在微生物源资源中，LTP与诱变育种、细胞壁重塑、群落筛选、代谢重编程和生物制造效率密切相关。主要瓶颈仍在于标准化参数数据库的缺失、等离子体-生物分子相互作用认识不完整、连续化设备放大不足以及食品和传统药材安全评价体系不完善。未来研究应优先推进基质特异性参数标准化、基于多组学的机制阐明，以及与智慧农业、精准营养、循环经济和人工智能辅助过程控制的融合。总体而言，LTP不仅应被视为一种杀菌或保鲜工具，更应被视为一个可编程的物理调控平台，用于食药同源资源的全链条品质形成和高值化利用。","Food & Medicine Homology Resources","2026-10-13T00:00:00Z",79,{"impact":19,"substance":105,"depth":19,"authority":208,"freshness":209,"relevant":22,"comment":210},13,8,"系统综述低温等离子体在食药同源资源全链条的应用框架与瓶颈，方法新颖、结论可靠，对农产品加工与智慧农业有参考价值。",[212],{"name":204,"url":201},[27,214,215,216,217],"食药同源","农产品加工","低温等离子体","绿色保鲜",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":221},{"candidates":222,"agricultural_evidence":47},[223,229,233],{"code":58,"score":224,"supported":47,"title":225,"abstract":226,"tags":228},7,[],[27,227],"smart agriculture",[27],{"code":33,"score":46,"supported":47,"title":230,"abstract":231,"tags":232},[],[50],[],{"code":234,"score":22,"supported":14,"title":235,"abstract":236,"tags":237},"E1",[],[],[215],[239,240],"低温等离子体 食药同源","冷等离子体 农产品加工","低温等离子体食药同源-4878","10.26599\u002Ffmhr.2026.9740001",{"doi":242,"openalex_id":244,"authors":245,"venue":204,"cited_by_count":82,"oa_url":201,"card":279,"direction":284,"ingested_from":89},"W7221715405",[246,248,250,252,254,256,259,262,264,266,268,270,272,275,277],{"name":247,"orcid":9},"Tao Jin",{"name":249,"orcid":9},"Xiang-Ping Su",{"name":251,"orcid":9},"Hua Liu",{"name":253,"orcid":9},"Ri-Long Huang",{"name":255,"orcid":9},"Kang-Liang Sheng",{"name":257,"orcid":258},"Ling Yan","https:\u002F\u002Forcid.org\u002F0000-0002-8806-5524",{"name":260,"orcid":261},"Shanshan Shui","https:\u002F\u002Forcid.org\u002F0000-0003-1095-7749",{"name":263,"orcid":9},"Shoaib Younas",{"name":265,"orcid":9},"Shuai Liu",{"name":267,"orcid":9},"Ao-Yu Ren",{"name":269,"orcid":9},"Hafiz-Rizwan Sharif",{"name":271,"orcid":9},"Anees-Ur Rehma",{"name":273,"orcid":274},"Shafqat Rasool","https:\u002F\u002Forcid.org\u002F0000-0002-5223-8358",{"name":276,"orcid":9},"Zia-Ur Rhman",{"name":278,"orcid":9},"Muhammad-Qasim Ali",{"tldr":280,"method":281,"finding":282,"direction":87,"opportunity":283},"综述低温等离子体在食药同源资源全链条中的创新应用，构建端到端框架。","文献综述，按植物、动物、微生物来源分类，梳理LTP作用机制与应用。","LTP是可编程物理调控平台，瓶颈在于参数标准化、机制理解、设备放大与安全评价。","可研究LTP参数标准化与AI过程控制结合，用于食药同源作物采后保鲜与功能成分调控。","智慧农业 \u002F 农业物联网","2026-10-14T23:30:08.799963Z",{"id":287,"title":288,"url":289,"summary":290,"summary_zh":291,"content":9,"source_name":10,"source_url":289,"published_at":292,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":293,"score_detail":294,"sources":296,"tags":298,"primary_topic":302,"secondary_topics":307,"classification":309,"search_phrases":329,"slug":332,"view_count":92,"doi":333,"paper":334,"created_at":356},2119,"Visual measurement for forage maize stubble height fusing cascaded neural networks and robust tracking","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112413","Visual measurement for forage maize stubble height fusing cascaded neural networks and robust tracking。Computers and Electronics in Agriculture","融合级联神经网络与鲁棒跟踪的饲用玉米留茬高度视觉测量。《计算机与电子农业》","2026-09-10T00:00:00Z",78,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":209,"relevant":22,"comment":295},"核心期刊论文，提出级联神经网络与鲁棒跟踪融合的青贮玉米留茬高度视觉测量方法，方法新颖且面向智能收获装备，专业价值较高，但属细分技术进展，影响范围有限。",[297],{"name":10,"url":289},[27,110,299,300,301],"机器视觉","青贮玉米","目标跟踪",{"code":303,"slug":304,"name":305,"path":306,"parent_code":119},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize",[308],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":310},{"candidates":311,"agricultural_evidence":47},[312,316,320,325],{"code":303,"score":139,"supported":47,"title":313,"abstract":314,"tags":315},[304],[305,304],[300],{"code":33,"score":17,"supported":47,"title":317,"abstract":318,"tags":319},[149],[149],[300],{"code":152,"score":321,"supported":14,"title":322,"abstract":323,"tags":324},2,[],[],[110,299],{"code":58,"score":22,"supported":14,"title":326,"abstract":327,"tags":328},[],[],[27],[330,331],"农业人工智能 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