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Husnot\n秋葵属 _Abelmoschus_ Medik.\n香葱\n\n（含分葱）_Allium cepiforme_ G．Don &_Allium fistulosum_ L．var．_caespitosum_ Makino\n露珠碎米荠 _Cardamine circaeoides_ Hook．f． &Thomson\n笋瓜\n\n（印度南瓜）_Cucurbita maxima_ Duch．ex Lam.\n荸荠 _Eleocharis dulcis_( Burm．f.) Trin．ex Hensch.\n芡属 _Euryale_ Salisb.\n葫芦 _Lagenaria siceraria_( Molina) Standl.\n水芹\n\n（含中华水芹）_Oenanthe javanica_( Blume) DC． &_Oenanthe sinensis_ Wall．ex DC.\n桔梗属 _Platycodon_ A．DC.\n慈姑\n\n（欧洲慈姑、华夏慈姑）_Sagittaria sagittifolia_ L． &_Sagittaria trifolia_ subsp．_leucopetala_( Miq. ) Q．F．Wang\n菱属 _Trapa_ L.\n腰果 _Anacardium occidentale_ L.\n番荔枝属 _Annona_ L.\n黄皮属 _Clausena_ Burm. f.\n咖啡属 _Co_ _f_ _fea_ L.\n榴梿\n\n（榴莲）_Durio zibethinus_ Rumph．ex Murray\n金柑属 _Fortunella_ Swingle\n红毛丹 _Nephelium lappaceum_ L.\n枳属 _Poncirus_ Raf.\n樱桃\n\n（中国樱桃）_Prunus pseudocerasus_ Lindl.\n番石榴 _Psidium guajava_ L.\n香瓜茄\n\n（人参果）_Solanum muricatum_ Ait.\n茶薪菇 _Cyclocybe chaxingu_(N. L．Huang) Q. M．Liu , Yang Gao &D. M．Hu\n皱木耳 _Auricularia delicata_( Mont. ) Henn.\n中华腐生牛肝菌 _Buchwaldoboletus xylophilus_( Petch) Both &B．Ortiz\n虫草属 _Cordyceps_ Fr.\n毛头鬼伞\n\n（鸡腿菇）_Coprinus comatus_( O. F．Müll. ) Pers.\n榆耳 _Gloeostereum incarnatum_ S. Ito & S. Imai\n灰树花 _Grifola frondosa_( Dicks. ) Gray\n珊瑚状猴头菌 _Hericium coraloides_( Scop.) Pers.\n荷叶离褶伞\n\n（鹿茸菇）_Lyophyllum decastes_( Fr. ) Singer\n鬼笔属\n\n（含竹荪、冬荪）_Phallus_ Junius ex L．\n金顶侧耳 _Pleurotus citrinopileatus_ Singer\n刺芹侧耳\n\n（杏鲍菇）_Pleurotus eryngii_( DC. ) Quél.\n巨大侧耳\n\n（猪肚菇）_Pleurotus giganteus_( Berk.) Karun． &K. D．Hyde\n暗褐脉柄牛肝菌 _Phlebopus portentosus_( Berk． &Broome) Boedijn\n桑黄孔菌属 _Sanghuangporus_ Sheng H．Wu , L. W．Zhou &Y. C．Dai\n海绵胶煤炱菌\n\n（竹燕窝）_Scorias spongiosa_( Schwein. ) Fr.\n元蘑 _Sarcomyxa edulis_( Y. C．Dai , Niemelä& G. F．Qin )T．Saito , Tonouchi &T．Harada\n裂褶菌 _Schizophyllum commune_ Fr.\n广叶绣球菌 _Sparassis latifolia_ Y. C．Dai &Zheng Wang\n大球盖菇 _Stropharia rugosoannulata_ Farl．ex Murrill\n银耳 _Tremella fuciformis_ Berk.\n金耳 _Naematelia aurantialba_( Bandoni & M．Zang)Millanes & Wedin\n草菇 _Volvarie_ _l_ _la volvacea_( Bull. ) Singer\n茯苓 _Wolfiporia cocos_( F．A．Wolf) Ryvarden & Gilb.\n益智 _Alpinia oxyphylla_ Miq.\n草果 _Amomum tsaoko_ Crevost& Lem.\n重齿当归 _Angelica biserrata_(R．H．Shan & C．Q．Yuan) C．Q．Yuan&R．H．Shan\n白芷 _Angelica dahurica_( Fisch．ex Hoffm. ) Benth． &Hook．f．ex Franch．& Sav.\n当归 _Angelica sinensis_( Oliv. ) Diels\n牛蒡属 _Arctium_ L.\n艾 _Artemisia argyi_ H.Lév． & Vaniot\n蒌蒿 _Artemisia selengensis_ Turcz．ex Besser\n苍术 _Atractylodes lancea_( Thunb. ) DC.\n白术 _Atractylodes macrocephala_ Koidz.\n白及 _Bletilla striata_( Thunb．ex A．Murray) Rchb．f.\n艾纳香 _Blumea balsamifera_( L. ) DC.\n党参属 _Codonopsis_ Wall.\n黄连属 _Coptis_ Salisb.\n穿龙薯蓣 _Dioscorea nipponica_ Makino\n贝母属 _Fritillaria_ L.\n龙胆属 _Gentiana_( Tourn.) L.\n甘草属 _Glycyrrhiza_ L.\n贯叶连翘 _Hypericum perforatum_ L.\n线纹香茶菜 _Isodon lophanthoides_(Buch. -Ham．ex D．Don) H．Hara\n山麦冬属 _Liriope_ Lour.\n博落回 _Macleaya cordata_( Willd. ) R．Br.\n麦冬 _Ophiopogon japonicus_( L．f. ) Ker Gawl.\n重楼属 _Paris_ L.\n西洋参 _Panax quinquefolius_ L.\n前胡 _Peucedanum praeruptorum_ Dunn\n大黄属 _Rheum_ L.\n半夏 _Pine_ _l_ _lia ternata_( Thunb. ) Ten．ex Breitenb.\n广藿香 _Pogostemon cablin_( Blanco) Benth.\n黄精属 _Polygonatum_ Mill.\n马齿苋 _Portulaca oleracea_ L.\n夏枯草 _Prunella vulgaris_ L.\n孩儿参 _Pseudostellaria heterophylla_( Miq. ) Pax\n鼠尾草属\n\n（不含半灌木或灌木种）_Salvia_ L.\n玄参 _Scrophularia ningpoensis_ Hemsl.\n黄芩 _Scute_ _l_ _laria_ baicalensis Georgi\n罗汉果属 _Siraitia_ Merr.\n蒲公英属 _Taraxacum_ F．H．Wigg.\n栝楼 _Trichosanthes kirilowii_ Maxim.\n荨麻属 _Urtica_ L.\n五彩芋属 _Caladium_ Vent.\n卡特兰属 _Cattleya_ Lindl.\n姜黄属 _Curcuma_ L.\n石莲花属 _Echeveria_ DC.\n假俭草 _Eremochloa ophiuroides_( Munro) Hack.\n洋桔梗属 _Eustoma_ Salisb.\n姜花属 _Hedychium_ J．Koenig\n玉簪属 _Hosta_ Tratt.\n长寿花 _Kalanchoe blossfeldiana_ Poelln.\n莲属 _Nelumbo_ Adans.\n罗勒 _Ocimum basilicum_ L.\n文心兰属 _Oncidium_ Sw.\n兜兰属 _Paphiopedilum_ Pfitzer\n喜林芋属 _Philodendron_ Schott\n大岩桐 _Sinningia speciosa_( G．Lodd．ex Ker Gawl. ) Hiern\n铁兰属 _Ti_ _l_ _landsia_ L.\n丽穗凤梨属 _Vriesea_ Lindl.\n羊草 _Leymus chinensis_( Trin．ex Bunge) Tzvelev\n长穗偃麦草 _Elytrigia elongata_( Host) Nevski\n海雀稗 _Paspalum vaginatum_ Sw.","嘉兴市农业农村局 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农用无人机","农用无人机作业面积4亿亩-4707","2026-10-12T00:10:55.212922Z",{"id":419,"title":420,"url":421,"summary":422,"summary_zh":20,"content":423,"source_name":424,"source_url":20,"published_at":184,"category":23,"cover_url":20,"hotness":24,"is_selected":44,"score":351,"score_detail":425,"sources":428,"tags":430,"primary_topic":435,"secondary_topics":440,"classification":441,"search_phrases":449,"slug":452,"view_count":32,"doi":20,"paper":20,"created_at":453},4697,"健全植物新品种保护制度为种业振兴提供制度保障","https:\u002F\u002Fwww.agri.cn\u002Fzx\u002Fnyyw\u002F202610\u002Ft20261009_8875474.htm","农业农村部法规司、种业管理司负责人就植物新品种保护条例实施细则农业部分、复审委员会审理规定和第十二批农业植物品种保护名录答记者问。三件规章于11月1日起施行,新增117个属种,保护范围扩大至308个,为种业振兴与乡村特色产业育种创新提供制度支撑","答：为推动农业植物新品种保护各项制度落地实施，持续提升种业知识产权保护质效，净化种业市场环境，下一步农业农村部将重点抓好四方面工作。一是强化制度宣贯解读。通过普法宣传、培训解读、知识问答、科普动漫等方式，指导种业科研育种单位和企业准确理解立法精神和制度要求，会同省级农业农村部门组织开展保护制度“进院校、进企业、进展会、进基地”普法活动，引导育种者知法懂法、守法用法。二是提高品种权审查质效。优化品种授权流程，完善品种权信息系统，对品种名称、新颖性、特异性、亲本来源等从严审查，引导育种者从追求数量向注重质量转变。加快完善从申请受理、测试审查到授权审批、监督管理全流程工作规范和技术标准，健全品种权创造、运用、保护、管理和服务体系。三是完善技术支撑体系。优化完善品种测试体系布局，提升测试机构能力和水平。加快信息化、智能化技术手段在品种测试中的应用，持续提升测试质效。推进新增保护名录植物属种的DUS测试指南、分子鉴定标准研制，实现审查有依据、判定有标准、维权有支撑。四是加大种业监管执法力度。加强农作物品种全链条管理，依法对品种权申请过程中的不诚信行为进行严肃处理。持续开展种业监管执法提升年活动，强化行政执法与司法保护紧密衔接，推行全链条全流程监管，严厉打击假冒伪劣、套牌侵权等违法行为，为种业振兴提供坚实法治保障和制度支撑。","中国农业农村信息网 2026-10-09",{"impact":28,"substance":426,"depth":250,"authority":31,"freshness":78,"relevant":32,"comment":427},20,"农业农村部部署植物新品种保护四方面工作，属全国性种业政策，条款具体、权威性强，值得进入每日精选。",[429],{"name":424,"url":421},[83,431,432,433,434],"种业知识产权","植物新品种保护","品种权审查","种业监管执法",{"code":117,"slug":436,"name":437,"path":438,"parent_code":439},"seed-industry","种业与品种管理","\u002Ftopic\u002Finputs-equipment\u002Fseed-industry","F",[],{"status":95,"locked":44,"version":45,"method":46,"evidence":442},{"candidates":443,"agricultural_evidence":25},[444],{"code":117,"score":308,"supported":25,"title":445,"abstract":447,"tags":448},[446],"种业",[446],[432,83,431],[450,451],"农业农村部 植物新品种保护","种业监管执法提升年 品种权","农业农村部植物新品种保护-4697","2026-10-12T00:10:54.357642Z",{"id":455,"title":456,"url":457,"summary":458,"summary_zh":459,"content":20,"source_name":460,"source_url":457,"published_at":461,"category":462,"cover_url":20,"hotness":24,"is_selected":44,"score":463,"score_detail":464,"sources":468,"tags":470,"primary_topic":474,"secondary_topics":478,"classification":484,"search_phrases":521,"slug":524,"view_count":105,"doi":525,"paper":526,"created_at":544},4693,"Scalable, cloud-native decision support system for Variable Rate Application (VRA) based on multi-year NDRE time series in arable precision agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23282433","This paper presents an automated, cloud-native geospatial Decision Support System (DSS) that generates variable rate prescription maps directly from Sentinel-2 satellite observations. Designed as an end-to-end arable field VRA architecture, the system operates completely autonomously with minimal input, requiring only the field boundary polygon, the crop phenology class (winter or spring crop), and the multi-year observation window. The computation engine streams Cloud-Optimized GeoTIFF (COG) rasters via asynchronous cloud connectivity through the Microsoft Planetary Computer (MPC) SpatioTemporal Asset Catalog (STAC) Application Programming Interface (API), enforcing data quality through multi-stage Scene Classification Layer (SCL) filtering. Instead of the rapidly saturating Normalized Difference Vegetation Index (NDVI), the model relies on the Normalized Difference Red Edge (NDRE) index, which accurately captures canopy chlorophyll and green biomass dynamics. For each native 10×10 m grid cell, the system extracts the 95th percentile (P95) peak value within a crop-specific reproductive phenological window, eliminating single-day snapshot noise while validating winter or spring growth trajectories via automated phenological ratio calculations. The decision and prescription engine is built upon two core agronomic safety pillars: An exponential empirical Bayesian stability shrinkage that dampens unstable, high-variance zones toward the safe field average, preventing the over-application of inputs on drought-prone areas. A trigonometric Horn slope attenuation derived from digital elevation models that systematically reduces application rates on erosion-prone steep slopes. Cell-level rates are distributed within the target tolerance range using a rank-allocation algorithm coupled with a Hamilton quota (largest remainder method) correction, guaranteeing strict integer-based machine compatibility and zero-sum conservation of the total field input budget. The technology was validated on a 139.03 ha commercial production field across four reference seasons (2022, 2023, 2024, and 2026) for Variable Rate Seeding (VRS) in winter wheat (Triticum aestivum L., 230 kg\u002Fha baseline seed rate) in a rolling hilly landscape in Hungary, benchmarked against conventional uniform application and conservative multi-year VRS planning. The primary quantitative findings are summarized below: · Failure of conventional uniform application: Across the 14,250 cells, the flat 230 kg\u002Fha rate was optimal on only 1.1% of the field area. · Input reallocation: The model reallocated 1,633.7 kg of seed (and 1,167.1 kg compared to conservative VRS) from unproductive areas into high-capacity zones (Figure 1.). Across 98.9% of the field, conventional uniform management generated systematic under- or over-seeding, inducing yield losses or wasted operational expenditure. · Drought mitigation: In temporally unstable zones subject to extreme weather risk (covering 33.6% of the field, 46.69 ha), Bayesian stability shrinkage reduced seed allocation by 495 kg compared to uniform seeding. Establishing lower canopy densities in these vulnerable zones mitigates intra-crop water competition and prevents early-season forced ripening (Lollato et al., 2017). · Erosion control: On steep slopes exceeding 12% inclination (accounting for 9.75% of the field, 13.56 ha), the Horn topographic attenuation engine reduced seed input by 553 kg relative to the fixed 230 kg\u002Fha baseline. This intervention minimizes surface runoff damage and agrochemical leaching. · Economic return: Spatially optimizing plant density based on pedological and topographic constraints delivers substantial yield gains. Reallocating seed across the field—without any expenditure on supplemental fertilizer—produces a projected yield increase of +0.20 to +0.40 t\u002Fha (+28 to +56 tons of grain across the evaluated field) (Kindred et al., 2016). At a baseline wheat price of €192\u002Ft (70,000 HUF\u002Ft), this generates an estimated net value of €5,376 to €10,752 (1.96 to 3.92M HUF) on this single 139 ha field.","本文提出了一种自动化、云原生的地理空间决策支持系统（DSS），可直接从Sentinel-2卫星观测数据生成变量施肥处方图。该系统设计为端到端的耕地变量施肥（VRA）架构，完全自主运行且输入需求极低，仅需田块边界多边形、作物物候类别（冬作或春作）以及多年观测窗口。计算引擎通过Microsoft Planetary Computer（MPC）时空资产目录（STAC）应用程序接口（API）以异步云连接方式流式传输云优化GeoTIFF（COG）栅格数据，并通过多阶段场景分类层（SCL）滤波确保数据质量。该模型不采用易快速饱和的归一化差异植被指数（NDVI），而是依赖归一化差异红边指数（NDRE），后者能够准确捕捉冠层叶绿素和绿色生物量动态。对于每个原生10×10 m网格单元，系统提取作物特定生殖物候窗口内的第95百分位数（P95）峰值，消除单日快照噪声，同时通过自动化物候比率计算验证冬作或春作生长轨迹。决策与处方引擎建立在两个核心农艺安全支柱之上：指数经验贝叶斯稳定性收缩，将不稳定的高方差区域向安全的田块平均值收缩，防止在易旱区域过量施用投入品；基于数字高程模型推导的三角函数Horn坡度衰减，系统性地降低易侵蚀陡坡上的施用量。单元级施用量通过秩分配算法结合Hamilton配额（最大余数法）校正，在目标容差范围内分配，确保严格的基于整数的机器兼容性和田块总投入预算的零和守恒。该技术已在匈牙利起伏丘陵地貌的139.03 ha商业化生产田块上，针对冬小麦（Triticum aestivum L.，基准播种量230 kg\u002Fha）的变量播种（VRS），在四个参考季节（2022、2023、2024和2026年）进行了验证，并以常规均一施用和保守的多年VRS规划为基准进行比较。主要定量结果总结如下：· 常规均一施用的失败：在14,250个单元中，统一的230 kg\u002Fha施","Zenodo (CERN European Organization for Nuclear Research)","2026-10-10T00:00:00Z","论文",83,{"impact":30,"substance":29,"depth":465,"authority":466,"freshness":24,"relevant":32,"comment":467},19,13,"基于多年度NDRE时序的云原生变量施肥决策系统，方法新颖、验证数据扎实，对精准农业信息化有较高参考价值。",[469],{"name":460,"url":457},[471,197,472,473,329],"数字农业","变量施肥","精准农业",{"code":230,"slug":475,"name":476,"path":477,"parent_code":321},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[479],{"code":480,"slug":481,"name":482,"path":483,"parent_code":269},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat",{"status":95,"locked":44,"version":45,"method":46,"evidence":485},{"candidates":486,"agricultural_evidence":25},[487,492,497,503,511,517],{"code":225,"score":251,"supported":25,"title":488,"abstract":490,"tags":491},[489],"precision agriculture",[],[471,197,473],{"code":230,"score":251,"supported":25,"title":493,"abstract":495,"tags":496},[494],"decision support",[494],[],{"code":480,"score":498,"supported":25,"title":499,"abstract":500,"tags":502},9,[],[482,501,481],"winter wheat",[],{"code":504,"score":505,"supported":25,"title":506,"abstract":507,"tags":510},"F3",7,[],[508,509],"施肥","fertilizer",[472],{"code":512,"score":105,"supported":25,"title":513,"abstract":514,"tags":516},"H7",[],[515],"drought",[],{"code":317,"score":32,"supported":44,"title":518,"abstract":519,"tags":520},[],[],[329],[522,523],"Sentinel-2 NDRE 变量施肥","云原生 决策支持系统 精准农业","Sentinel-2NDRE变量施肥-4693","10.5281\u002Fzenodo.23282433",{"doi":525,"openalex_id":527,"authors":528,"venue":460,"cited_by_count":64,"oa_url":457,"card":536,"direction":542,"ingested_from":543},"W7221365319",[529,531,533],{"name":530,"orcid":20},"Zoltán Szenek",{"name":532,"orcid":20},"Donát Szám",{"name":534,"orcid":535},"Gábor Milics","https:\u002F\u002Forcid.org\u002F0000-0002-4499-8322",{"tldr":537,"method":538,"finding":539,"direction":540,"opportunity":541},"构建云原生决策支持系统，用多年NDRE时序自动生成精准农业变量施肥\u002F播种处方图。","Sentinel-2 NDRE、COG\u002FSTAC云流式处理、贝叶斯收缩与坡度衰减","在匈牙利139公顷冬小麦田验证，系统优于传统均一施用与保守多年VRS规划。","农业遥感与作物表型","可探索将NDRE时序DSS扩展至多作物、多区域及实时物联网数据融合的通用处方框架。","数字乡村与农业信息化","openalex","2026-10-11T23:30:36.790037Z",{"id":546,"title":547,"url":548,"summary":549,"summary_zh":20,"content":20,"source_name":550,"source_url":20,"published_at":184,"category":462,"cover_url":20,"hotness":24,"is_selected":44,"score":551,"score_detail":552,"sources":554,"tags":556,"primary_topic":20,"secondary_topics":560,"classification":561,"search_phrases":587,"slug":590,"view_count":32,"doi":591,"paper":592,"created_at":601},4732,"智能农业提升旱地混合作物家畜系统的氮利用与产量预测","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs44264-025-00012-8","整合1990至2025年158篇文献,综述人工智能、物联网与遥感在旱地混作系统的应用。精准施肥使氮利用效率提高超15%;机器学习结合卫星数据可解释超70%产量变异。数字可及性、基础设施与社会经济制约仍是广泛采纳的主要障碍","Discover Agriculture 2026-10-09",81,{"impact":142,"substance":142,"depth":30,"authority":466,"freshness":78,"relevant":32,"comment":553},"基于158篇文献的系统综述，量化了智能农业在旱地混作系统的增产增效潜力，对智慧农业研究与实践有参考价值。",[555],{"name":550,"url":548},[197,198,557,558,559],"精准施肥","遥感监测","旱地农业",[],{"status":204,"locked":44,"version":45,"method":46,"evidence":562},{"candidates":563,"agricultural_evidence":25,"reason":234},[564,568,573,577,583],{"code":504,"score":99,"supported":25,"title":565,"abstract":566,"tags":567},[],[508],[557],{"code":230,"score":99,"supported":25,"title":569,"abstract":570,"tags":572},[],[571],"机器学习",[198],{"code":317,"score":99,"supported":25,"title":574,"abstract":575,"tags":576},[],[329],[558],{"code":578,"score":105,"supported":25,"title":579,"abstract":580,"tags":582},"I5",[],[581],"物联网",[],{"code":225,"score":32,"supported":44,"title":584,"abstract":585,"tags":586},[],[],[197],[588,589],"旱地混作系统 氮利用效率","机器学习 作物产量预测","旱地混作系统氮利用效率-4732","10.1007\u002Fs44264-025-00012-8",{"doi":591,"openalex_id":20,"authors":593,"venue":20,"cited_by_count":64,"oa_url":20,"card":594,"direction":598,"ingested_from":600},[],{"tldr":595,"method":596,"finding":597,"direction":598,"opportunity":599},"综述AI、物联网与遥感在旱地混作系统的应用，评估其对氮利用与产量预测的提升。","整合1990-2025年158篇文献，分析精准施肥、机器学习与卫星遥感数据。","精准施肥使氮利用效率提高超15%，机器学习结合卫星数据可解释超70%产量变异。","智慧农业 \u002F 农业物联网","数字可及性、基础设施与社会经济制约是采纳瓶颈，可研究低成本适配旱地小农的智能方案。","agent","2026-10-12T00:11:14.741867Z",{"id":603,"title":604,"url":605,"summary":606,"summary_zh":20,"content":20,"source_name":607,"source_url":20,"published_at":184,"category":140,"cover_url":20,"hotness":24,"is_selected":44,"score":551,"score_detail":608,"sources":611,"tags":613,"primary_topic":618,"secondary_topics":619,"classification":620,"search_phrases":654,"slug":657,"view_count":32,"doi":20,"paper":20,"created_at":658},4712,"浙江农科院揭示水稻抗褐飞虱关键调控机制","https:\u002F\u002Fwww.zaas.ac.cn\u002Fxwdt\u002Fart\u002F2026\u002Fart_d7e3b59f9cba4a0a9b3a2d6c5e4f1a2b.htm","浙江省农科院病毒学与生物技术研究所联合农产品质量安全全国重点实验室近日揭示水稻抗褐飞虱关键调控机制,挖掘出水稻抗虫育种新靶点,为抗褐飞虱新品种选育提供理论支撑,助力减少化学农药使用、保障粮食安全","浙江省农业科学院官网 2026-10-09",{"impact":142,"substance":609,"depth":250,"authority":308,"freshness":505,"relevant":32,"comment":610},21,"省级科研机构揭示水稻抗褐飞虱关键调控机制，属种业与绿色防控领域的实质科研进展，具备聚合与检索价值。",[612],{"name":607,"url":605},[83,614,615,616,617],"绿色防控","水稻育种","抗虫育种","褐飞虱",{"code":265,"slug":266,"name":267,"path":268,"parent_code":269},[],{"status":95,"locked":44,"version":45,"method":46,"evidence":621},{"candidates":622,"agricultural_evidence":25},[623,627,633,639,645,649],{"code":265,"score":275,"supported":25,"title":624,"abstract":625,"tags":626},[267],[267],[615],{"code":628,"score":99,"supported":25,"title":629,"abstract":630,"tags":632},"F4",[],[631],"农药",[614],{"code":634,"score":105,"supported":25,"title":635,"abstract":636,"tags":638},"A8",[],[637],"粮食安全",[],{"code":640,"score":105,"supported":25,"title":641,"abstract":642,"tags":644},"E4",[],[643],"农产品质量",[],{"code":117,"score":32,"supported":44,"title":646,"abstract":647,"tags":648},[],[],[83],{"code":650,"score":32,"supported":44,"title":651,"abstract":652,"tags":653},"H8",[],[],[614],[655,656],"浙江农科院 水稻 褐飞虱","水稻抗褐飞虱 育种靶点","浙江农科院水稻褐飞虱-4712","2026-10-12T00:11:04.443471Z",{"id":660,"title":661,"url":662,"summary":663,"summary_zh":20,"content":664,"source_name":138,"source_url":20,"published_at":139,"category":140,"cover_url":20,"hotness":24,"is_selected":44,"score":551,"score_detail":665,"sources":667,"tags":669,"primary_topic":675,"secondary_topics":681,"classification":682,"search_phrases":700,"slug":703,"view_count":64,"doi":20,"paper":20,"created_at":704},4702,"中国农科院首届草业成果蒙科聚发布会在通辽举办","https:\u002F\u002Fwww.farmer.com.cn\u002F2026\u002F10\u002F11\u002F991030496.html","10月10日,中国农业科学院首届草业领域科技成果蒙科聚专题发布会在内蒙古通辽市举行,集中发布9项优质饲草、智慧放牧、盐碱地改良等创新成果,并与内蒙古交通集团、首农三元等多家企业集中签约,落实院地全面深化合作协议","金秋十月，草硕粮丰。10月10日，中国农业科学院首届草业领域科技成果2026年“蒙科聚”专题发布会在内蒙古通辽市举行。本次活动在中国农业科学院、内蒙古自治区科技厅、内蒙古自治区农牧厅和通辽市人民政府统筹指导下，由中国农业科学院成果转化局、中国农业科学院北方农牧业技术创新中心、内蒙古科学技术研究院、通辽市科学技术局和通辽市农牧局联合主办，中国农科院草原研究所会同内蒙古民族大学、通辽市科学技术成果转化中心、“蒙科聚”通辽分中心、通辽市农牧业科学院和通辽市农牧业发展中心等单位共同承办。\n\n开幕式上，院地领导为协同创新宣贯新策、定向领航。会议指出，今年7月自治区和中国农科院优势互补双向奔赴，签署了《内蒙古自治区人民政府 中国农业科学院全面深化合作协议》，全面深化战略合作关系，在联合打造农牧业科技创新高地、科技赋能国家重要农畜产品生产基地建设等方面作出一揽子部署，本次发布会正是落实协议的创新谋划和务实举措。会议提出，真正建立“政府搭台、企业出题、院所答题”机制要靠各方协同、同向发力，打破“有什么转什么”的惯性思维，做到“需求在哪就在哪攻关、攻得出就能用得上”；要切实推动教育科技人才一体发展，形成“教育育人、人才创新、科技落地”的良性循环。会议强调，自治区党委“1571”工作部署明确提出推动草业向千亿级产业目标迈进，要更好依托“蒙科聚”核心枢纽平台推动科技成果从“书架”“试验田”走向“货架”，打通成果转化“最后一公里”，让科技创新真正转化为现实生产力。\n\n此次成果发布活动“含金量”足、科企签约“抓落地”实。本次集中发布了中国农业科学院草原所、牧医所、饲料所、资划所、兰牧药及内蒙古民族大学、通辽市农牧业科学院等单位的9项科技创新成果，覆盖优质饲草品种、盐碱化草地地力提升、非粮饲料资源高效利用、智慧放牧与饲草智能装备、科尔沁沙地生态修复等关键领域。现场同步举行科企合作集中签约，内蒙古交通集团有限公司、内蒙古青城乳业和牛羊肉交易中心有限公司、内蒙古额仑生态集团有限责任公司、首农集团北京三元绿化工程有限公司、北京中航泰达环保科技股份有限公司、众为（南京）智能装备有限公司等多家企业签约，标志着一批实验室成果迈入产业一线，助力科研成果转化为现代草牧业发展与草原生态治理的现实生产力。\n\n立足国家战略擘画、各方资源禀赋、区域产业需求，本次成果发布会承载着重要意义。一是落实“大食物观”、保障饲料粮安全。通辽作为自治区重要农畜产品主产区，草畜融合发展科技需求迫切，要以科技赋能草业更好串联起北方生态安全屏障与国家重要农畜产品生产基地发展重任。二是落实院地全面深化合作协议。以“蒙科聚”枢纽平台为依托，引入国家级科技资源与产业场景实现专业化系统性对接，推动人才、项目、技术成果等优势资源转移落地，不断增强对内蒙古的科技创新支撑。三是探索科研范式变革。智慧放牧、智能装备持续研发集成，“论文导向”向“产业导向”深刻转变，助力形成“产研联动、双向赋能”的良性循环。\n\n面向“十五五”，草原所将以此次发布会为新起点接续发力。作为扎根内蒙古六十余年的草业科研“国家队”，草原所将扎实落实自治区党委“1571”工作部署和中国农业科学院“四个面向”“两个一流”战略目标，围绕草种业振兴、退化草原修复、盐碱化草地改良、草畜一体化、智慧草牧业等重点领域持续攻关。下一步，草原所将依托北方中心和“蒙科聚”平台，推动成果清单向合作清单、项目清单转化，把更多成果播种在内蒙古广袤的土地上，让科技成果真正服务基层、惠及农牧民，为筑牢我国北方重要生态安全屏障、建设国家重要农畜产品生产基地作出新的贡献。",{"impact":142,"substance":426,"depth":143,"authority":308,"freshness":498,"relevant":32,"comment":666},"国家级草业科技成果集中发布与科企签约，覆盖饲草品种、智慧放牧、盐碱地治理等关键领域，信息增量足、信源权威，值得进入每日精选。",[668],{"name":138,"url":662},[670,671,672,673,674],"成果转化","盐碱地治理","草业科技","饲草种业","智慧放牧",{"code":676,"slug":677,"name":678,"path":679,"parent_code":680},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":95,"locked":44,"version":45,"method":46,"evidence":683},{"candidates":684,"agricultural_evidence":25},[685,690,696],{"code":676,"score":99,"supported":25,"title":686,"abstract":687,"tags":689},[],[688],"盐碱地",[671],{"code":691,"score":105,"supported":25,"title":692,"abstract":693,"tags":695},"C4",[],[694],"饲草",[],{"code":158,"score":32,"supported":44,"title":697,"abstract":698,"tags":699},[],[],[670],[701,702],"中国农科院 蒙科聚 草业成果","通辽 草业 科企签约","中国农科院蒙科聚草业成果-4702","2026-10-12T00:10:54.733723Z",{"id":706,"title":707,"url":708,"summary":709,"summary_zh":710,"content":20,"source_name":711,"source_url":708,"published_at":461,"category":462,"cover_url":20,"hotness":24,"is_selected":44,"score":551,"score_detail":712,"sources":714,"tags":716,"primary_topic":720,"secondary_topics":721,"classification":722,"search_phrases":733,"slug":736,"view_count":64,"doi":737,"paper":738,"created_at":759},4695,"AgroNLP-SimAssist: a multilingual transformer-based decision support simulator for crop disease diagnosis, advisory generation, and market intelligence","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12870-026-10040-8","Abstract There are three major shortcomings of agricultural NLP and need addressing, regardless of the recent developments in the field: (i) use of proprietary or small-scale datasets that affect reproducibility; (ii) one-task models that only focus on intent recognition but lack integration for crop disease diagnosis, advice, and market information; and (iii) the absence of simulators for evaluating the system’s performance during actual deployment. The application of precision agriculture is constrained by limitations in diverse farming environments. The study introduces AgroNLP-SimAssist, which represents a unified multilingual transformer architecture augmented by an easily deployable agricultural decision support simulator, as the proposed solution to overcome the challenges. The five major components constituting the proposed framework are: (1) domain-specific multilingual transformer embedding; (2) data collection and cleaning from publicly available agricultural datasets; (3) multiple prediction tasks including trend price estimation, crop disease detection, and advisory recommendation; (4) SHAP value explainability for expert validation; and (5) advisory simulation engine for evaluating latency and scalability. Multilingually available data sources, which include multilingual agricultural reports from the FAO organization, crop-disease descriptions from the PlantVillage knowledge base, and price-market narratives from the Agmarknet website, have been used for training and validation purposes. Among the 3,200 multilingual farmer queries dataset (in English, Hindi, and Kannada), there was an assignment of 70%, 15%, and 15% to the training, validation, and testing sets, respectively. In terms of accuracy compared to the existing classifiers, results from experiments yielded 96.1% disease classification, 94.3% advisory relevance, and 92.7% named entity recognition with F1-measure. The simulation system scaled up to 10,000 concurrent users with an inference latency of 0.35 s\u002Fquery. The proposed data-driven AgroNLP-SimAssist framework facilitates extension services and precision agriculture through providing a scalable, reproducible, and interpretable solution for intelligent agricultural advisory systems.","尽管近年来农业自然语言处理（NLP）领域取得了进展，但仍存在三个主要不足亟需解决：(i) 使用专有或小规模数据集，影响可复现性；(ii) 单任务模型仅关注意图识别，缺乏对作物病害诊断、建议和市场信息的整合；(iii) 缺乏用于评估系统实际部署性能的模拟器。精准农业的应用受到多样化农业环境的限制。本研究提出了AgroNLP-SimAssist，一种统一的多语言Transformer架构，并辅以易于部署的农业决策支持模拟器，作为克服上述挑战的解决方案。该框架由五个主要组件构成：(1) 领域特定的多语言Transformer嵌入；(2) 从公开农业数据集中进行数据收集与清洗；(3) 多预测任务，包括趋势价格估计、作物病害检测和咨询推荐；(4) SHAP值可解释性用于专家验证；(5) 咨询模拟引擎用于评估延迟和可扩展性。多语言可用数据源已用于训练和验证，包括联合国粮农组织（FAO）的多语言农业报告、PlantVillage知识库的作物病害描述以及Agmarknet网站的价格市场叙述。在包含3200条多语言农民查询数据集（英语、印地语和卡纳达语）中，训练集、验证集和测试集的分配比例分别为70%、15%和15%。与现有分类器相比，实验结果显示病害分类准确率达96.1%，咨询相关性达94.3%，命名实体识别的F1值为92.7%。模拟系统可扩展至10000名并发用户，推理延迟为0.35秒\u002F查询。所提出的数据驱动AgroNLP-SimAssist框架通过为智能农业咨询系统提供可扩展、可复现且可解释的解决方案，促进了推广服务和精准农业的发展。","BMC Plant Biology",{"impact":30,"substance":142,"depth":30,"authority":308,"freshness":498,"relevant":32,"comment":713},"多语言农业大模型整合病害诊断、农技咨询与市场情报，数据规模与仿真验证扎实，对智慧农业信息化有参考价值。",[715],{"name":711,"url":708},[197,198,717,718,719],"农业大模型","农业决策支持","作物病害诊断",{"code":230,"slug":475,"name":476,"path":477,"parent_code":321},[],{"status":95,"locked":44,"version":45,"method":46,"evidence":723},{"candidates":724,"agricultural_evidence":25},[725,729],{"code":230,"score":466,"supported":25,"title":726,"abstract":727,"tags":728},[494],[494],[198,717],{"code":225,"score":505,"supported":25,"title":730,"abstract":731,"tags":732},[],[473,489],[197],[734,735],"AgroNLP-SimAssist 作物病害诊断","多语言农业大模型 农业决策支持","AgroNLP-SimAssist作物病害诊断-4695","10.1186\u002Fs12870-026-10040-8",{"doi":737,"openalex_id":739,"authors":740,"venue":711,"cited_by_count":64,"oa_url":708,"card":753,"direction":757,"ingested_from":543},"W7221339938",[741,743,745,747,749,751],{"name":742,"orcid":20},"Priya Thirugnanam",{"name":744,"orcid":20},"Sandeep kumar Mathivanan",{"name":746,"orcid":20},"Sundara Kumar M R",{"name":748,"orcid":20},"Sangeetha Ramaswamy",{"name":750,"orcid":20},"Shruthi T S",{"name":752,"orcid":20},"Sangeetha S.K.B",{"tldr":754,"method":755,"finding":756,"direction":757,"opportunity":758},"提出多语言Transformer农业决策支持模拟器，统一实现作物病害诊断、农技建议与市场情报。","基于FAO、PlantVillage、Agmarknet多语数据训练Transf","病害分类96.1%、建议相关性94.3%、NER F1 92.7%，万级并发延迟0.35秒。","农业人工智能与决策模型","可扩展至更多语种与作物，并引入真实田间部署反馈闭环优化模拟器。","2026-10-11T23:30:56.064196Z",{"id":761,"title":762,"url":763,"summary":764,"summary_zh":765,"content":20,"source_name":766,"source_url":763,"published_at":184,"category":462,"cover_url":20,"hotness":24,"is_selected":44,"score":551,"score_detail":767,"sources":769,"tags":771,"primary_topic":20,"secondary_topics":775,"classification":776,"search_phrases":791,"slug":794,"view_count":64,"doi":795,"paper":796,"created_at":834},4691,"An African Tree Crown–Stem Allometry Dataset for Monitoring Trees Outside Forests and Landscape Restoration","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fessd-2026-756","Abstract. Very high-resolution (VHR) satellite imagery and advances in machine learning increasingly enable individual tree crowns to be mapped across large landscapes, creating new opportunities for census-based estimation of tree biomass and carbon. A fundamental limitation, however, is that satellite imagery can measure crown dimensions but cannot directly observe diameter at breast height (DBH), the principal predictor used in conventional tree biomass allometry. This measurement gap is particularly important in African landscapes dominated by trees outside forests (TOF), where trees are spatially dispersed, conventional forest inventory sampling is difficult to scale, and ground datasets containing concurrent measurements of crown and stem dimensions remain scarce. Here, we present an African individual-tree crown–stem allometry dataset designed to provide the empirical link between remotely observable crown dimensions and the stem measurements required by established biomass allometric models. The dataset was collected using a standardized field protocol across landscapes associated with African Forest Landscape Restoration Initiative (AFR100) projects in Kenya, Ghana, Rwanda, and Malawi. The sampled landscapes encompass agroforestry, reforestation, assisted natural regeneration, and other restoration and land-management systems representative of TOF conditions. Following quality control, the dataset contains 3,493 individual trees with concurrent measurements of DBH, two perpendicular crown diameters, tree height, species identity, geographic location, and associated plot and landscape identifiers. Field measurements were designed for spatial linkage with individual tree crowns delineated from VHR satellite imagery, providing calibration observations for translating remotely sensed crown projected area (CA) into stem diameter. We demonstrate this application by fitting power-law DBH–CA transfer functions and evaluating their predictive performance across sites and countries. All site-specific relationships were significant (p \u003C 0.001), with R² values ranging from 0.44 to 0.85 and generally small prediction bias. Variation in model coefficients and predictive error among landscapes demonstrates substantial ecological and structural heterogeneity in crown–stem relationships and emphasizes the value of geographically distributed calibration data rather than reliance on a single universal allometric relationship. A hierarchical synthesis of 19 site-specific models further demonstrates how these data can be used to derive a generalized crown-to-stem relationship while retaining information on among-landscape variation. The principal contribution of this dataset is therefore to provide a measurement bridge between individual-tree remote sensing and conventional stem-based biomass allometry. VHR imagery and machine-learning methods can delineate individual crowns and measure their projected areas; crown-to-stem transfer functions calibrated with these field data can then estimate DBH; and established DBH-based allometric equations can subsequently be used to estimate biomass and carbon for each remotely detected tree. This framework extends individual-tree allometric scaling from field plots to landscape-scale tree censuses while maintaining compatibility with established forest mensuration and biomass estimation methods. The dataset has applications beyond the models demonstrated here, including development and validation of alternative crown–stem and crown–biomass models, analysis of tree structural and species-level variation, ecological scaling studies, and calibration of individual-tree remote-sensing products. It provides a reusable observational resource for advancing the inclusion of TOF in Forest and Landscape Restoration monitoring, carbon accounting, and measurement, reporting, and verification across African landscapes.","摘要。甚高分辨率（VHR）卫星影像与机器学习的进步日益使得能够在广阔景观中绘制单木树冠，为基于普查的树木生物量与碳估算创造了新机遇。然而，一个根本性限制在于，卫星影像能够测量树冠尺寸，却无法直接观测胸径（DBH），而胸径是传统树木生物量异速生长方程中所使用的主要预测变量。这一测量缺口在以林外树木（TOF）为主的非洲景观中尤为重要，因为这些景观中树木空间分散，传统森林清查抽样难以规模化推广，且同时包含树冠与树干尺寸测量的地面数据集仍然稀缺。在此，我们呈现一个非洲单木树冠–树干异速生长数据集，旨在为可遥感观测的树冠尺寸与既有生物量异速生长模型所需的树干测量值之间提供经验联系。该数据集采用标准化野外调查方案，在肯尼亚、加纳、卢旺达和马拉维与“非洲森林景观恢复倡议”（AFR100）项目相关的景观中采集。所采样的景观涵盖农林业、再造林、辅助自然更新以及其他代表林外树木条件的恢复与土地管理系统。经过质量控制后，该数据集包含3，493株单木，同时测量了胸径、两个垂直方向的树冠直径、树高、物种身份、地理位置以及相关样地和景观标识符。野外测量设计用于与从甚高分辨率卫星影像中 delineated 的单木树冠进行空间关联，为将遥感获取的树冠投影面积（CA）转换为树干直径提供校准观测。我们通过拟合幂律胸径–树冠面积传递函数并评估其跨站点和跨国家的预测性能来展示这一应用。所有站点特定关系均显著（p \u003C 0.001），R²值范围为0.44至0.85，且预测偏差通常较小。不同景观之间模型系数和预测误差的变异表明，树冠–树干关系存在显著的生态与结构异质性，并凸显了地理分布校准数据的价值，而非依赖单一通用异速生长关系。对19个站点特定","Earth System Science Data Discussions",{"impact":30,"substance":29,"depth":30,"authority":466,"freshness":498,"relevant":32,"comment":768},"非洲四国3493株树木冠层-胸径实测数据集，为遥感单木生物量估算提供校准桥梁，方法新颖、数据规模可观，对农业遥感与林业碳汇监测有参考价值。",[770],{"name":766,"url":763},[198,329,772,773,774],"生物量估算","林业碳汇","森林恢复",[],{"status":204,"locked":44,"version":45,"method":46,"evidence":777},{"candidates":778,"agricultural_evidence":44,"reason":790},[779,785],{"code":317,"score":780,"supported":25,"title":781,"abstract":782,"tags":784},8,[],[329,783],"remote sensing",[772,329],{"code":230,"score":505,"supported":25,"title":786,"abstract":787,"tags":789},[],[571,788],"machine learning",[198],"农业相关性需要复核",[792,793],"AFR100 非洲 树冠 胸径","遥感 树冠 生物量 非洲","AFR100非洲树冠胸径-4691","10.5194\u002Fessd-2026-756",{"doi":795,"openalex_id":797,"authors":798,"venue":766,"cited_by_count":64,"oa_url":828,"card":829,"direction":540,"ingested_from":543},"W7221198582",[799,801,804,806,808,810,813,816,819,821,824,826],{"name":800,"orcid":20},"Tabitha Alimo",{"name":802,"orcid":803},"David Lewis Skole","https:\u002F\u002Forcid.org\u002F0000-0002-8575-3540",{"name":805,"orcid":20},"Jay Samek",{"name":807,"orcid":20},"Dan Ndalowa",{"name":809,"orcid":20},"Vincent Oeba",{"name":811,"orcid":812},"Judith Francesca Mangani Kamoto","https:\u002F\u002Forcid.org\u002F0000-0002-0662-7125",{"name":814,"orcid":815},"Alfred Chioza","https:\u002F\u002Forcid.org\u002F0009-0004-6348-0758",{"name":817,"orcid":818},"Chad C. Papa","https:\u002F\u002Forcid.org\u002F0009-0003-8260-8545",{"name":820,"orcid":20},"Justine Spore",{"name":822,"orcid":823},"Darby Levin","https:\u002F\u002Forcid.org\u002F0000-0002-4040-1613",{"name":825,"orcid":20},"John Brandt",{"name":827,"orcid":20},"Tangu Tumeo","https:\u002F\u002Fessd.copernicus.org\u002Fpreprints\u002Fessd-2026-756\u002Fessd-2026-756.pdf",{"tldr":830,"method":831,"finding":832,"direction":540,"opportunity":833},"构建非洲四国3493棵树的冠幅-胸径异速生长数据集，用于遥感估算森林外树木生物量。","在AFR100项目区标准化实测DBH、冠幅、树高等，拟合幂律DBH-CA转换函数","各站点DBH-CA关系显著但系数与误差差异大，需分布式校准而非通用异速方程。","可扩展至更多非洲景观与树种，结合VHR影像和机器学习提升森林外树木碳储量估算精度。","2026-10-11T23:30:33.259708Z"]