[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"taxonomy":3,"taxonomy-items:\u002Ftopic\u002Ffield-crops?technology=%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0&technology=%E5%86%9C%E4%B8%9A%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD":570,"taxonomy-facets:A:":1835},{"version":4,"topics":5},"2026-10-22.2",[6,71,127,199,270,331,387,433,479,533],{"code":7,"slug":8,"name":9,"path":10,"parent_code":11,"description":12,"count":13,"children":14},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",null,"浏览数字农业与前沿技术相关政策、产业动态与研究。",1341,[15,23,31,39,47,55,63],{"code":16,"slug":17,"name":18,"path":19,"parent_code":7,"description":20,"count":21,"children":22},"I1","smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture","智慧农业总体路径、模式与数字化转型",112,[],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7,"description":28,"count":29,"children":30},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","人工智能算法、大模型与决策支持系统",634,[],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7,"description":36,"count":37,"children":38},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","卫星与无人机遥感、长势监测与产量估测",325,[],{"code":40,"slug":41,"name":42,"path":43,"parent_code":7,"description":44,"count":45,"children":46},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","地理信息、空间统计与区域计量方法",69,[],{"code":48,"slug":49,"name":50,"path":51,"parent_code":7,"description":52,"count":53,"children":54},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","农业传感器、物联网组网与边缘计算",155,[],{"code":56,"slug":57,"name":58,"path":59,"parent_code":7,"description":60,"count":61,"children":62},"I6","digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village","数字乡村建设、数字鸿沟与数据治理",151,[],{"code":64,"slug":65,"name":66,"path":67,"parent_code":7,"description":68,"count":69,"children":70},"I7","research-methods","研究方法与评估","\u002Ftopic\u002Fdigital-agriculture\u002Fresearch-methods","综述、文献计量与因果识别方法",2,[],{"code":72,"slug":73,"name":74,"path":75,"parent_code":11,"description":76,"count":77,"children":78},"F","inputs-equipment","农业投入品与装备","\u002Ftopic\u002Finputs-equipment","浏览农业投入品与装备相关政策、产业动态与研究。",608,[79,87,95,103,111,119],{"code":80,"slug":81,"name":82,"path":83,"parent_code":72,"description":84,"count":85,"children":86},"F1","seed-industry","种业与品种管理","\u002Ftopic\u002Finputs-equipment\u002Fseed-industry","种质资源、品种审定与知识产权",162,[],{"code":88,"slug":89,"name":90,"path":91,"parent_code":72,"description":92,"count":93,"children":94},"F2","biotechnology","生物育种与基因技术","\u002Ftopic\u002Finputs-equipment\u002Fbiotechnology","分子设计育种、基因编辑与合成生物",63,[],{"code":96,"slug":97,"name":98,"path":99,"parent_code":72,"description":100,"count":101,"children":102},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","施肥技术、化肥减量与新型肥料",88,[],{"code":104,"slug":105,"name":106,"path":107,"parent_code":72,"description":108,"count":109,"children":110},"F4","crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection","农药管理、绿色植保与生物防治",135,[],{"code":112,"slug":113,"name":114,"path":115,"parent_code":72,"description":116,"count":117,"children":118},"F5","machinery","农机装备与机械化","\u002Ftopic\u002Finputs-equipment\u002Fmachinery","农机装备、机械化作业与导航",121,[],{"code":120,"slug":121,"name":122,"path":123,"parent_code":72,"description":124,"count":125,"children":126},"F6","agricultural-robots","机器人与无人农场","\u002Ftopic\u002Finputs-equipment\u002Fagricultural-robots","农业机器人、无人农场与智能作业",36,[],{"code":128,"slug":129,"name":130,"path":131,"parent_code":11,"description":132,"count":133,"children":134},"H","environment","资源环境与气候变化","\u002Ftopic\u002Fenvironment","浏览资源环境与气候变化相关政策、产业动态与研究。",672,[135,143,151,159,167,175,183,191],{"code":136,"slug":137,"name":138,"path":139,"parent_code":128,"description":140,"count":141,"children":142},"H1","farmland","耕地保护与高标准农田","\u002Ftopic\u002Fenvironment\u002Ffarmland","耕地数量质量、高标准农田与农田建设",29,[],{"code":144,"slug":145,"name":146,"path":147,"parent_code":128,"description":148,"count":149,"children":150},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","土壤理化生物性状、盐碱地治理与固碳",207,[],{"code":152,"slug":153,"name":154,"path":155,"parent_code":128,"description":156,"count":157,"children":158},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","水资源配置、节水灌溉与旱作农业",150,[],{"code":160,"slug":161,"name":162,"path":163,"parent_code":128,"description":164,"count":165,"children":166},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","碳排放核算、碳汇与绿色转型",44,[],{"code":168,"slug":169,"name":170,"path":171,"parent_code":128,"description":172,"count":173,"children":174},"H5","waste","面源污染与废弃物利用","\u002Ftopic\u002Fenvironment\u002Fwaste","面源污染治理与秸秆粪污等废弃物利用",8,[],{"code":176,"slug":177,"name":178,"path":179,"parent_code":128,"description":180,"count":181,"children":182},"H6","biodiversity","生态保护与生物多样性","\u002Ftopic\u002Fenvironment\u002Fbiodiversity","生态系统修复、生物多样性与土地利用变化",45,[],{"code":184,"slug":185,"name":186,"path":187,"parent_code":128,"description":188,"count":189,"children":190},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","气候变化影响、气候韧性农业与灾害预警",197,[],{"code":192,"slug":193,"name":194,"path":195,"parent_code":128,"description":196,"count":197,"children":198},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture","可持续生产模式与保护性耕作",40,[],{"code":200,"slug":201,"name":202,"path":203,"parent_code":11,"description":204,"count":205,"children":206},"A","field-crops","大田作物","\u002Ftopic\u002Ffield-crops","浏览大田作物相关政策、产业动态与研究。",1227,[207,215,223,231,239,247,255,262],{"code":208,"slug":209,"name":210,"path":211,"parent_code":200,"description":212,"count":213,"children":214},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","以水稻为对象的生产、育种、植保、机械化与产业内容",337,[],{"code":216,"slug":217,"name":218,"path":219,"parent_code":200,"description":220,"count":221,"children":222},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","小麦及冬春麦区相关生产与科研",144,[],{"code":224,"slug":225,"name":226,"path":227,"parent_code":200,"description":228,"count":229,"children":230},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","玉米生产、育种、密植高产与复合种植",189,[],{"code":232,"slug":233,"name":234,"path":235,"parent_code":200,"description":236,"count":237,"children":238},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","大豆、油菜、花生、向日葵、芝麻等油料与蛋白作物",166,[],{"code":240,"slug":241,"name":242,"path":243,"parent_code":200,"description":244,"count":245,"children":246},"A5","tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains","马铃薯、甘薯、高粱、谷子、荞麦等薯类杂粮",92,[],{"code":248,"slug":249,"name":250,"path":251,"parent_code":200,"description":252,"count":253,"children":254},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","棉花、甘蔗、烟草、蚕桑等工业原料型作物",87,[],{"code":256,"slug":257,"name":258,"path":259,"parent_code":200,"description":260,"count":141,"children":261},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","饲草产业与间套轮作、连作障碍等种植制度",[],{"code":263,"slug":264,"name":265,"path":266,"parent_code":200,"description":267,"count":268,"children":269},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","单产行动、产能建设与粮食安全、收购形势",143,[],{"code":271,"slug":272,"name":273,"path":274,"parent_code":11,"description":275,"count":276,"children":277},"B","horticulture","园艺作物","\u002Ftopic\u002Fhorticulture","浏览园艺作物相关政策、产业动态与研究。",531,[278,285,293,301,308,315,323],{"code":279,"slug":280,"name":281,"path":282,"parent_code":271,"description":283,"count":189,"children":284},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","蔬菜育种、栽培、病虫害与产业",[],{"code":286,"slug":287,"name":288,"path":289,"parent_code":271,"description":290,"count":291,"children":292},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","苹果、柑橘、猕猴桃、葡萄、热带亚热带果树",147,[],{"code":294,"slug":295,"name":296,"path":297,"parent_code":271,"description":298,"count":299,"children":300},"B3","tea-coffee","茶叶与咖啡","\u002Ftopic\u002Fhorticulture\u002Ftea-coffee","茶叶、咖啡、可可等饮品原料作物",51,[],{"code":302,"slug":303,"name":304,"path":305,"parent_code":271,"description":306,"count":125,"children":307},"B4","flowers","花卉与观赏植物","\u002Ftopic\u002Fhorticulture\u002Fflowers","花卉产业、月季等观赏园艺",[],{"code":309,"slug":310,"name":311,"path":312,"parent_code":271,"description":313,"count":141,"children":314},"B5","mushrooms","食用菌","\u002Ftopic\u002Fhorticulture\u002Fmushrooms","食用菌菌种、栽培与产业",[],{"code":316,"slug":317,"name":318,"path":319,"parent_code":271,"description":320,"count":321,"children":322},"B6","medicinal-plants","中药材与药食同源","\u002Ftopic\u002Fhorticulture\u002Fmedicinal-plants","道地药材、药用植物与药食同源开发",22,[],{"code":324,"slug":325,"name":326,"path":327,"parent_code":271,"description":328,"count":329,"children":330},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","设施化保护地生产与植物工厂、无土栽培、都市农业",67,[],{"code":332,"slug":333,"name":334,"path":335,"parent_code":11,"description":336,"count":337,"children":338},"C","livestock","畜牧兽医","\u002Ftopic\u002Flivestock","浏览畜牧兽医相关政策、产业动态与研究。",198,[339,347,355,363,371,379],{"code":340,"slug":341,"name":342,"path":343,"parent_code":332,"description":344,"count":345,"children":346},"C1","pigs","生猪","\u002Ftopic\u002Flivestock\u002Fpigs","生猪养殖、产能与产业形势",14,[],{"code":348,"slug":349,"name":350,"path":351,"parent_code":332,"description":352,"count":353,"children":354},"C2","ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants","奶牛、肉牛、羊养殖与奶业",47,[],{"code":356,"slug":357,"name":358,"path":359,"parent_code":332,"description":360,"count":361,"children":362},"C3","poultry","家禽","\u002Ftopic\u002Flivestock\u002Fpoultry","蛋鸡、肉鸡、水禽养殖",18,[],{"code":364,"slug":365,"name":366,"path":367,"parent_code":332,"description":368,"count":369,"children":370},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","饲料安全、蛋白饲料与饲草供给",35,[],{"code":372,"slug":373,"name":374,"path":375,"parent_code":332,"description":376,"count":377,"children":378},"C5","animal-health","疫病防控与兽药","\u002Ftopic\u002Flivestock\u002Fanimal-health","动物疫病、疫苗、兽药与人兽共患病",50,[],{"code":380,"slug":381,"name":382,"path":383,"parent_code":332,"description":384,"count":385,"children":386},"C6","smart-livestock","养殖环境与智慧畜牧","\u002Ftopic\u002Flivestock\u002Fsmart-livestock","粪污资源化、动物福利与智能化养殖",10,[],{"code":388,"slug":389,"name":390,"path":391,"parent_code":11,"description":392,"count":197,"children":393},"D","aquaculture","渔业水产","\u002Ftopic\u002Faquaculture","浏览渔业水产相关政策、产业动态与研究。",[394,402,410,418,426],{"code":395,"slug":396,"name":397,"path":398,"parent_code":388,"description":399,"count":400,"children":401},"D1","pond-factory-aquaculture","池塘与工厂化养殖","\u002Ftopic\u002Faquaculture\u002Fpond-factory-aquaculture","池塘养殖、循环水与设施渔业",4,[],{"code":403,"slug":404,"name":405,"path":406,"parent_code":388,"description":407,"count":408,"children":409},"D2","offshore-aquaculture","深远海养殖与海洋牧场","\u002Ftopic\u002Faquaculture\u002Foffshore-aquaculture","深远海养殖工船、海洋牧场与渔业资源",5,[],{"code":411,"slug":412,"name":413,"path":414,"parent_code":388,"description":415,"count":416,"children":417},"D3","integrated-aquaculture","稻渔种养与生态渔业","\u002Ftopic\u002Faquaculture\u002Fintegrated-aquaculture","稻渔综合种养、增殖渔业与生态养殖",12,[],{"code":419,"slug":420,"name":421,"path":422,"parent_code":388,"description":423,"count":424,"children":425},"D4","aquatic-health","水质与病害","\u002Ftopic\u002Faquaculture\u002Faquatic-health","养殖水质调控与水生动物病害防控",0,[],{"code":427,"slug":428,"name":429,"path":430,"parent_code":388,"description":431,"count":69,"children":432},"D5","smart-aquaculture","智慧渔业","\u002Ftopic\u002Faquaculture\u002Fsmart-aquaculture","渔业数字化、智能投喂与养殖装备",[],{"code":434,"slug":435,"name":436,"path":437,"parent_code":11,"description":438,"count":439,"children":440},"E","food-supply","农产品加工与流通","\u002Ftopic\u002Ffood-supply","浏览农产品加工与流通相关政策、产业动态与研究。",107,[441,449,457,464,471],{"code":442,"slug":443,"name":444,"path":445,"parent_code":434,"description":446,"count":447,"children":448},"E1","food-processing","加工与食品制造","\u002Ftopic\u002Ffood-supply\u002Ffood-processing","初加工、精深加工与食品制造工艺",25,[],{"code":450,"slug":451,"name":452,"path":453,"parent_code":434,"description":454,"count":455,"children":456},"E2","nutrition","营养与功能食品","\u002Ftopic\u002Ffood-supply\u002Fnutrition","膳食营养、功能成分与大食物观",1,[],{"code":458,"slug":459,"name":460,"path":461,"parent_code":434,"description":462,"count":416,"children":463},"E3","cold-chain","冷链仓储物流","\u002Ftopic\u002Ffood-supply\u002Fcold-chain","保鲜包装、冷链与粮食仓储物流",[],{"code":465,"slug":466,"name":467,"path":468,"parent_code":434,"description":469,"count":369,"children":470},"E4","food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","质量安全检测、农药残留与全链条追溯",[],{"code":472,"slug":473,"name":474,"path":475,"parent_code":434,"description":476,"count":477,"children":478},"E5","branding-commerce","品牌流通与电商","\u002Ftopic\u002Ffood-supply\u002Fbranding-commerce","品牌建设、产销对接与农村电商",33,[],{"code":480,"slug":481,"name":482,"path":483,"parent_code":11,"description":484,"count":485,"children":486},"G","rural-services","农业服务与乡村发展","\u002Ftopic\u002Frural-services","浏览农业服务与乡村发展相关政策、产业动态与研究。",344,[487,495,502,510,518,526],{"code":488,"slug":489,"name":490,"path":491,"parent_code":480,"description":492,"count":493,"children":494},"G1","extension-transfer","农技推广与成果转化","\u002Ftopic\u002Frural-services\u002Fextension-transfer","农技推广体系、科技特派员与科技小院",139,[],{"code":496,"slug":497,"name":498,"path":499,"parent_code":480,"description":500,"count":439,"children":501},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","托管服务、土地流转与新型经营主体",[],{"code":503,"slug":504,"name":505,"path":506,"parent_code":480,"description":507,"count":508,"children":509},"G3","rural-finance","农村金融与保险","\u002Ftopic\u002Frural-services\u002Frural-finance","信贷担保、农业保险与财政支农",20,[],{"code":511,"slug":512,"name":513,"path":514,"parent_code":480,"description":515,"count":516,"children":517},"G4","rural-industry","乡村产业与县域经济","\u002Ftopic\u002Frural-services\u002Frural-industry","乡村产业、三产融合与县域经济",28,[],{"code":519,"slug":520,"name":521,"path":522,"parent_code":480,"description":523,"count":524,"children":525},"G5","rural-governance","乡村治理与人才","\u002Ftopic\u002Frural-services\u002Frural-governance","乡村治理、数字治理与人才培养",11,[],{"code":527,"slug":528,"name":529,"path":530,"parent_code":480,"description":531,"count":321,"children":532},"G6","rural-integration","帮扶与城乡融合","\u002Ftopic\u002Frural-services\u002Frural-integration","常态化帮扶、区域协作与农民增收",[],{"code":534,"slug":535,"name":536,"path":537,"parent_code":11,"description":538,"count":539,"children":540},"J","policy","政策与宏观","\u002Ftopic\u002Fpolicy","浏览政策与宏观相关政策、产业动态与研究。",132,[541,548,555,562],{"code":542,"slug":543,"name":544,"path":545,"parent_code":534,"description":546,"count":408,"children":547},"J1","policy-planning","政策制度与发展规划","\u002Ftopic\u002Fpolicy\u002Fpolicy-planning","部委政策、规划文件与解读",[],{"code":549,"slug":550,"name":551,"path":552,"parent_code":534,"description":553,"count":447,"children":554},"J2","modernization","农业强国与现代化","\u002Ftopic\u002Fpolicy\u002Fmodernization","农业强国、农业农村现代化与新质生产力",[],{"code":556,"slug":557,"name":558,"path":559,"parent_code":534,"description":560,"count":400,"children":561},"J3","global-agriculture","国际合作与全球农业","\u002Ftopic\u002Fpolicy\u002Fglobal-agriculture","国际农业合作与国别农业研究",[],{"code":563,"slug":564,"name":565,"path":566,"parent_code":534,"description":567,"count":568,"children":569},"J4","seasonal-agriculture","农时与节庆活动","\u002Ftopic\u002Fpolicy\u002Fseasonal-agriculture","农时生产调度、丰收节与消费季",98,[],{"total":571,"page":455,"page_size":508,"items":572},186,[573,672,752,806,849,891,958,1031,1079,1126,1181,1233,1295,1414,1467,1516,1590,1664,1717,1786],{"id":574,"title":575,"url":576,"summary":577,"summary_zh":578,"content":11,"source_name":579,"source_url":576,"published_at":580,"category":581,"cover_url":11,"hotness":385,"is_selected":582,"score":583,"score_detail":584,"sources":587,"tags":589,"primary_topic":595,"secondary_topics":596,"classification":599,"search_phrases":638,"slug":641,"view_count":424,"doi":642,"paper":643,"created_at":671},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","论文",false,80,{"impact":585,"substance":321,"depth":361,"authority":345,"freshness":385,"relevant":455,"comment":586},16,"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。",[588],{"name":579,"url":576},[590,591,592,593,594],"智慧农业","农业人工智能","农业遥感","氮素监测","紫花苜蓿",{"code":256,"slug":257,"name":258,"path":259,"parent_code":200},[597,598],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"code":364,"slug":365,"name":366,"path":367,"parent_code":332},{"status":600,"locked":582,"version":4,"method":601,"evidence":602},"classified","rules",{"candidates":603,"agricultural_evidence":605},[604,612,619,625,634],{"code":32,"score":361,"supported":605,"title":606,"abstract":608,"tags":611},true,[607],"hyperspectral",[609,610,607],"遥感","remote sensing",[592],{"code":256,"score":613,"supported":605,"title":614,"abstract":616,"tags":618},15,[615],"alfalfa",[617,615],"苜蓿",[594],{"code":364,"score":385,"supported":605,"title":620,"abstract":621,"tags":624},[],[622,617,623],"饲草","forage",[594],{"code":24,"score":385,"supported":605,"title":626,"abstract":627,"tags":633},[],[628,629,630,631,632],"深度学习","机器学习","machine learning","deep learning","decision support",[591],{"code":16,"score":455,"supported":582,"title":635,"abstract":636,"tags":637},[],[],[590],[639,640],"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":642,"openalex_id":644,"authors":645,"venue":579,"cited_by_count":424,"oa_url":576,"card":664,"direction":668,"ingested_from":670},"W7223154576",[646,648,650,652,654,657,659,662],{"name":647,"orcid":11},"Shuai Fu",{"name":649,"orcid":11},"Yunhao Li",{"name":651,"orcid":11},"Chunli Miao",{"name":653,"orcid":11},"Jie Liu",{"name":655,"orcid":656},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":658,"orcid":11},"Yongjia Hu",{"name":660,"orcid":661},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":663,"orcid":11},"吴彩霞",{"tldr":665,"method":666,"finding":667,"direction":668,"opportunity":669},"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。","openalex","2026-10-22T11:20:39.622844Z",{"id":673,"title":674,"url":675,"summary":676,"summary_zh":677,"content":11,"source_name":678,"source_url":675,"published_at":580,"category":581,"cover_url":11,"hotness":385,"is_selected":582,"score":679,"score_detail":680,"sources":685,"tags":687,"primary_topic":691,"secondary_topics":692,"classification":695,"search_phrases":728,"slug":731,"view_count":424,"doi":732,"paper":733,"created_at":751},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy",77,{"impact":585,"substance":681,"depth":682,"authority":683,"freshness":385,"relevant":455,"comment":684},21,17,13,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[686],{"name":678,"url":675},[590,629,688,689,690],"遥感监测","土壤墒情","冬小麦",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[693,694],{"code":144,"slug":145,"name":146,"path":147,"parent_code":128},{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":600,"locked":582,"version":4,"method":601,"evidence":696},{"candidates":697,"agricultural_evidence":605},[698,704,709,713,718,724],{"code":216,"score":699,"supported":605,"title":700,"abstract":702,"tags":703},26,[701,217],"winter wheat",[218,701,217],[690],{"code":144,"score":613,"supported":605,"title":705,"abstract":706,"tags":708},[145],[707,145],"土壤",[689],{"code":24,"score":613,"supported":605,"title":710,"abstract":711,"tags":712},[630],[629,630],[629],{"code":32,"score":385,"supported":605,"title":714,"abstract":715,"tags":717},[],[609,610,716],"multispectral",[688],{"code":152,"score":719,"supported":605,"title":720,"abstract":721,"tags":723},6,[],[722,153],"灌溉",[],{"code":16,"score":400,"supported":605,"title":725,"abstract":726,"tags":727},[],[590],[590],[729,730],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":732,"openalex_id":734,"authors":735,"venue":678,"cited_by_count":424,"oa_url":675,"card":745,"direction":750,"ingested_from":670},"W7223156204",[736,739,741,743],{"name":737,"orcid":738},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":740,"orcid":11},"Yi Zhang",{"name":742,"orcid":11},"WEI Lei",{"name":744,"orcid":11},"Chengcai Zhang",{"tldr":746,"method":747,"finding":748,"direction":668,"opportunity":749},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:18.887301Z",{"id":753,"title":754,"url":755,"summary":756,"summary_zh":11,"content":11,"source_name":757,"source_url":11,"published_at":758,"category":581,"cover_url":11,"hotness":385,"is_selected":582,"score":45,"score_detail":759,"sources":762,"tags":764,"primary_topic":768,"secondary_topics":769,"classification":771,"search_phrases":793,"slug":796,"view_count":424,"doi":11,"paper":797,"created_at":805},5532,"精准棉花病害管理：融合无人机影像与定制YOLO深度学习框架","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.24182","研究提出单阶段全卷积检测器Cotton-Vision，融合幽灵瓶颈通道注意力与跨阶段部分融合三项轻量化设计，参数减少20%。在巴基斯坦旁遮普省7个棉田采集的2700余幅高分辨率无人机影像上测试，精确率97.7%、召回率77.7%、mAP@50为85.6%，较主流基线提升10至20个百分点。预印本，仅在巴基斯坦单一区域验证。","arXiv 预印本 2026-10-16","2026-10-16T00:00:00Z",{"impact":585,"substance":681,"depth":682,"authority":760,"freshness":719,"relevant":455,"comment":761},9,"方法轻量化与实测数据有增量，但仅单区域预印本验证，属细分技术进展，可入精选备选。",[763],{"name":757,"url":755},[590,591,765,766,767],"目标检测","无人机遥感","棉花病害",{"code":248,"slug":249,"name":250,"path":251,"parent_code":200},[770],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":600,"locked":582,"version":4,"method":601,"evidence":772},{"candidates":773,"agricultural_evidence":605},[774,779,785,789],{"code":24,"score":361,"supported":605,"title":775,"abstract":777,"tags":778},[628,776],"YOLO",[],[591,765],{"code":248,"score":524,"supported":605,"title":780,"abstract":782,"tags":784},[781],"棉花",[783],"cotton",[],{"code":16,"score":455,"supported":582,"title":786,"abstract":787,"tags":788},[],[],[590],{"code":32,"score":455,"supported":582,"title":790,"abstract":791,"tags":792},[],[],[766],[794,795],"Cotton-Vision 棉花病害","无人机影像 棉花 检测","Cotton-Vision棉花病害-5532",{"doi":11,"openalex_id":11,"authors":798,"venue":11,"cited_by_count":424,"oa_url":11,"card":799,"direction":668,"ingested_from":804},[],{"tldr":800,"method":801,"finding":802,"direction":668,"opportunity":803},"提出轻量化检测器Cotton-Vision，用无人机影像精准识别棉花病害。","基于YOLO改进，融合幽灵瓶颈通道注意力与跨阶段部分融合，用2700余幅无人机影","精确率97.7%、mAP@50达85.6%，较主流基线提升10至20个百分点。","仅在巴基斯坦单一区域验证，可扩展至多区域多品种，并探索边缘设备实时部署。","agent","2026-10-22T00:07:09.816790Z",{"id":807,"title":808,"url":809,"summary":810,"summary_zh":11,"content":811,"source_name":812,"source_url":11,"published_at":580,"category":813,"cover_url":11,"hotness":385,"is_selected":582,"score":814,"score_detail":815,"sources":817,"tags":819,"primary_topic":823,"secondary_topics":824,"classification":825,"search_phrases":844,"slug":847,"view_count":424,"doi":11,"paper":11,"created_at":848},5518,"河北农林科学院玉米抗病性鉴定开放日 AR智能眼镜助力分级定级","https:\u002F\u002Fwww.cfsn.cn\u002Fnews\u002Fdetail\u002F345\u002F371985.html","河北省农林科学院植物保护研究所主办2026年黄淮海玉米抗病性鉴定田间开放日。作为全国玉米抗病性鉴定牵头单位，该所推进智能化技术嫁接鉴定工作，引入AR智能眼镜拍照自动标注叶片病斑率并判定严重度等级，支持语音控制与后台批量导出，另探索无人机悬停拍照系统开展规模化快速调查。","金秋时节，冀中平原玉米长势喜人、丰收在望。近日，由河北省农林科学院植物保护研究所主办的“2026年黄淮海玉米抗病性鉴定田间开放日”活动在河北省保定市顺利举行。来自玉米育种、植保、种业企业等领域的领导、专家和同仁齐聚一堂，围绕玉米抗病性鉴定标准化、智能化与精准化展开深入交流与现场观摩。\n\n河北省农林科学院植物保护研究所副所长吴玉星表示，玉米抗病性鉴定是育种升级和品种审定推广的必要环节，对保障粮食安全具有重要意义。河北省农林科学院植物保护研究所作为全国玉米抗病性鉴定的牵头单位，严格按照国家标准规范布局、统一试验，全程实行标准化操作程序，确保每一项数据真实、准确、公正。他强调，团队将继续恪守公平、公正、科学、严谨的工作原则，不断优化试验方案，为各试验单位提供优质高效的抗病性鉴定服务，助力优良玉米品种替代升级和高质量发展。\n\n活动期间，河北省农林科学院植物保护研究所研究员石洁结合多年田间鉴定实践，介绍了当前玉米抗病性鉴定面临的现实挑战。她指出，黄淮海地区玉米病害种类多、调查面积大、工作强度高，仅靠人工逐品种、逐病害调查，骨干人员长期在田间超负荷工作，难以满足规模化、精准化鉴定需求。为此，团队积极推动智能化技术嫁接到抗病性鉴定工作中，着力实现数据实时采集、结果直接反馈和精准分级。\n\n北京金禾天成科技有限公司有关负责人介绍了通过AR智能眼镜识别玉米病害及判定严重度等级技术。该技术通过拍照识别，自动标注每片叶片的病斑率和整张图片的病斑率，并给出严重度等级判定，拍照后自动保存识别结果和原图，支持现场快速呈现结果和后台批量导出。系统还可按品种编号自动对应品种名称，支持人工校正和后续复核，有效减轻调查人员劳动强度，提升鉴定效率和标准化水平。\n\n石洁告诉记者，团队正在积极协助研发和应用AR智能眼镜技术，用于玉米叶斑病、穗腐病等病害的精准分级。该设备支持语音控制，可解放双手，在田间行走过程中即可完成“所见即所得”的病情定级，并实时反馈数据。相比传统人眼判断，智能眼镜能够提供更精细的数量化级别，满足抗性基因定位等研究对定量数据的需求。此外，团队还在积极探索无人机高空悬停拍照系统，用于茎腐病、矮花叶病等病害的规模化快速调查，进一步提升鉴定效率。\n\n活动现场还展示了快速检测技术在玉米病害防控中的应用。石洁介绍，该技术可在病害尚未发病的叶片上，通过棉签擦拭、裂解液处理和便携设备检测，30分钟内常温下判断叶片是否携带孢子或已被病原菌侵入。目前可检测病理上几乎所有病害（病毒病除外），包括南方锈病、穗腐病、弯孢叶斑病、瘤黑粉病等。荧光定量PCR版本一次可同时检测8种病原菌，帮助种植大户和种业企业据检测结果用药，实现“检防联动”，从普惠性预防用药转向按需防控、对靶施药、早防早治，大幅节省农药和飞防成本。\n\n本次开放日活动全面开放观摩现场，与会代表近距离了解了玉米抗病性鉴定从试验设计、田间管控、症状跟踪到数据调查的全流程。大家一致认为，智能化鉴定技术和快速检测技术的推广应用，将有力推动黄淮海玉米抗病性鉴定工作向精准化、标准化、高效化迈进，为玉米产业高质量发展保驾护航。（王田）","中国食品安全网 2026-10-21","报道",73,{"impact":361,"substance":508,"depth":585,"authority":385,"freshness":760,"relevant":455,"comment":816},"省级科研机构开放日，AR智能眼镜与快速检测技术有实质信息增量，属细分领域智能化进展，值得精选。",[818],{"name":812,"url":809},[590,591,820,821,822],"智能装备","玉米病害","抗病性鉴定",{"code":224,"slug":225,"name":226,"path":227,"parent_code":200},[],{"status":600,"locked":582,"version":4,"method":601,"evidence":826},{"candidates":827,"agricultural_evidence":605},[828,832,836,840],{"code":224,"score":524,"supported":605,"title":829,"abstract":830,"tags":831},[226],[226],[],{"code":112,"score":455,"supported":582,"title":833,"abstract":834,"tags":835},[],[],[820],{"code":16,"score":455,"supported":582,"title":837,"abstract":838,"tags":839},[],[],[590],{"code":24,"score":455,"supported":582,"title":841,"abstract":842,"tags":843},[],[],[591],[845,846],"河北农科院 玉米抗病性鉴定 AR智能眼镜","黄淮海 玉米 病害 快速检测","河北农科院玉米抗病性鉴定AR智能眼镜-5518","2026-10-22T00:07:04.176689Z",{"id":850,"title":851,"url":852,"summary":853,"summary_zh":11,"content":854,"source_name":855,"source_url":11,"published_at":856,"category":813,"cover_url":11,"hotness":385,"is_selected":605,"score":857,"score_detail":858,"sources":861,"tags":863,"primary_topic":868,"secondary_topics":869,"classification":870,"search_phrases":886,"slug":889,"view_count":455,"doi":11,"paper":11,"created_at":890},5422,"中国农科院发布AI 智能体「绿洲」 助力植物抗逆研究登上《Molecular Plant》","https:\u002F\u002Fwww.caas.cn\u002Fxwzx\u002Fkyhd\u002F28164b3f0e7f4996a5749892288670bc.htm","中国农业科学院农业基因组研究所超级稻种质创新团队联合国内科研单位发布 AI 智能体「绿洲」，能自动查阅植物抗逆研究领域的海量文献和科研数据，帮助研究人员快速找到值得实验验证的新线索，成果发表在《Molecular Plant》。该智能体模拟科研团队协作流程，通过多个智能体协同自主完成资料检索、数据分析和结果核查。团队借此筛选出多个水稻耐盐候选基因，经实验验证其中一个与水稻耐盐响应相关，为作物抗逆改良提供了人工智能辅助工具。","近日，中国农业科学院农业基因组研究所超级稻种质创新团队联合国内科研单位发布了AI智能体“绿洲”。该智能体能自动查阅植物抗逆研究领域的海量文献和科研数据，帮助研究人员快速找到值得实验验证的新线索。相关研究成果发表在《分子植物（Molecular Plant）》上。\n\n植物抗逆研究涉及海量的文献和生物数据，如何从复杂信息中快速发现关键线索并提出可验证假设一直是研究难点。\n\n研究团队开发了一套模拟科研团队协作流程的智能系统，通过多个智能体协同，可自主完成资料检索、数据分析和结果核查，并在任务反馈中不断优化。研究团队借助该系统成功筛选出多个水稻耐盐候选基因，经实验验证，其中一个候选基因与水稻耐盐响应相关，展现了人工智能辅助植物抗逆研究的应用潜力。该研究为植物抗逆机制研究和作物抗逆改良提供辅助工具。\n\n该研究得到国家重点研发计划、国家自然科学基金、中国农业科学院科技创新工程等项目资助。（通讯员  马昕怡）","中国农业科学院 2026-10-20","2026-10-20T00:00:00Z",89,{"impact":859,"substance":321,"depth":361,"authority":613,"freshness":385,"relevant":455,"comment":860},24,"国家级科研机构发布AI科研智能体并登上核心期刊，方法新颖、结论经实验验证，具备行业示范价值，值得进入每日精选。",[862],{"name":855,"url":852},[591,864,865,866,867],"智能育种","AI智能体","植物抗逆","水稻耐盐",{"code":208,"slug":209,"name":210,"path":211,"parent_code":200},[],{"status":600,"locked":582,"version":4,"method":601,"evidence":871},{"candidates":872,"agricultural_evidence":605},[873,878,882],{"code":208,"score":874,"supported":605,"title":875,"abstract":876,"tags":877},3,[],[210],[],{"code":24,"score":69,"supported":582,"title":879,"abstract":880,"tags":881},[],[],[865,591],{"code":88,"score":455,"supported":582,"title":883,"abstract":884,"tags":885},[],[],[864],[887,888],"中国农科院 绿洲 智能体","水稻 耐盐 候选基因","中国农科院绿洲智能体-5422","2026-10-21T00:10:00.076699Z",{"id":892,"title":893,"url":894,"summary":895,"summary_zh":896,"content":11,"source_name":897,"source_url":894,"published_at":898,"category":581,"cover_url":11,"hotness":385,"is_selected":582,"score":899,"score_detail":900,"sources":902,"tags":904,"primary_topic":906,"secondary_topics":907,"classification":909,"search_phrases":939,"slug":942,"view_count":424,"doi":943,"paper":944,"created_at":957},5394,"Rice Yield Prediction Using Multi-Modal Regression Learning with Optimized Ensemble Models","https:\u002F\u002Fdoi.org\u002F10.3390\u002Feng7100572","Accurate rice yield estimation is important for agricultural planning, resource allocation, and food security assessment, but yield depends on interacting climatic, vegetation, remote sensing, seasonal, and crop growth factors. This study presents a Multi-Modal Regression Learning (MMRL) framework for continuous rice yield prediction. The framework integrates rainfall, temperature, humidity, normalized difference vegetation index (NDVI), VH synthetic aperture radar (SAR) backscatter, season, rice cultivation area, and leaf wetness information. Predictor relevance is assessed before model development, and multiple regression learners are trained using a common feature set. Their validation predictions are combined through non-negative ensemble weights constrained to sum to one, thereby producing a single optimized prediction. Experiments were performed on a modeled spatio-temporal agricultural dataset representing Karnataka conditions, with 10,000 observations used for the regression comparison. The proposed MMRL ensemble achieved an R2 of 0.9515, an RMSE of 0.6334 tons\u002Fha, an MAE of 0.5075 tons\u002Fha, and an MAPE of 9.81%, outperforming the evaluated individual regression models. Diagnostic plots further indicated close agreement between predicted and reference yields across most of the evaluated range. The MMRL workflow was also evaluated on a separately reconstructed synthetic dataset generated with fully specified distributions, sampling rules, noise terms, a yield generation equation, and fixed random seeds.","准确的水稻产量估算对农业规划、资源配置和粮食安全评估具有重要意义，但产量取决于气候、植被、遥感、季节和作物生长等多种因素的相互作用。本研究提出了一种用于连续水稻产量预测的多模态回归学习（Multi-Modal Regression Learning，MMRL）框架。该框架整合了降雨量、温度、湿度、归一化植被指数（NDVI）、VH合成孔径雷达（SAR）后向散射、季节、水稻种植面积和叶片湿润信息。在模型开发之前评估预测变量的相关性，并使用共同特征集训练多个回归学习器。其验证预测结果通过约束为和等于1的非负集成权重进行组合，从而生成单一优化预测。实验在代表卡纳塔克邦条件的建模时空农业数据集上进行，使用10,000个观测值进行回归比较。所提出的MMRL集成模型实现了R²为0.9515、RMSE为0.6334吨\u002F公顷、MAE为0.5075吨\u002F公顷、MAPE为9.81%，优于所评估的各个回归模型。诊断图进一步表明，在大多数评估范围内，预测产量与参考产量高度一致。MMRL工作流程还在一个单独重建的合成数据集上进行了评估，该数据集使用完全指定的分布、采样规则、噪声项、产量生成方程和固定随机种子生成。","Eng—Advances in Engineering","2026-10-18T00:00:00Z",78,{"impact":361,"substance":321,"depth":361,"authority":416,"freshness":173,"relevant":455,"comment":901},"多模态回归集成方法在水稻估产上取得较高精度，方法新颖、数据规模明确，对农业信息化有参考价值，但属学术论文且数据集为模拟数据，产业影响有限。",[903],{"name":897,"url":894},[590,591,210,905,609],"产量预测",{"code":208,"slug":209,"name":210,"path":211,"parent_code":200},[908],{"code":32,"slug":33,"name":34,"path":35,"parent_code":7},{"status":600,"locked":582,"version":4,"method":601,"evidence":910},{"candidates":911,"agricultural_evidence":605},[912,916,920,926,931,935],{"code":208,"score":613,"supported":605,"title":913,"abstract":914,"tags":915},[209],[210,209],[210],{"code":32,"score":173,"supported":605,"title":917,"abstract":918,"tags":919},[],[609,610],[905,609],{"code":263,"score":719,"supported":605,"title":921,"abstract":922,"tags":925},[],[923,924],"粮食安全","food security",[],{"code":542,"score":874,"supported":605,"title":927,"abstract":928,"tags":930},[],[929],"农业规划",[],{"code":16,"score":455,"supported":582,"title":932,"abstract":933,"tags":934},[],[],[590],{"code":24,"score":455,"supported":582,"title":936,"abstract":937,"tags":938},[],[],[591],[940,941],"水稻 产量预测 多模态","NDVI SAR 水稻估产","水稻产量预测多模态-5394","10.3390\u002Feng7100572",{"doi":943,"openalex_id":945,"authors":946,"venue":897,"cited_by_count":424,"oa_url":894,"card":952,"direction":668,"ingested_from":670},"W7222942064",[947,949],{"name":948,"orcid":11},"Nayana Venkatesh",{"name":950,"orcid":951},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":953,"method":954,"finding":955,"direction":668,"opportunity":956},"构建多模态回归学习框架，融合气候、遥感与作物因子，实现水稻产量连续预测。","融合降雨、温度、NDVI、SAR后向散射等多源特征，用非负权重集成多个回归器。","集成模型R²达0.9515，RMSE为0.6334吨\u002F公顷，优于各单一回归模型。","可引入真实田间多时相遥感与深度时序模型，验证合成数据结论在真实产区的泛化性。","2026-10-20T23:30:19.292634Z",{"id":959,"title":960,"url":961,"summary":962,"summary_zh":963,"content":11,"source_name":964,"source_url":961,"published_at":965,"category":581,"cover_url":11,"hotness":385,"is_selected":582,"score":583,"score_detail":966,"sources":968,"tags":970,"primary_topic":973,"secondary_topics":974,"classification":975,"search_phrases":1012,"slug":1015,"view_count":424,"doi":1016,"paper":1017,"created_at":1030},5388,"A digital twin framework for proactive disease management in paddy cultivation using spatio-temporal graph neural networks","https:\u002F\u002Fdoi.org\u002F10.1080\u002F20421338.2026.2707951","Rice production is highly affected by disease outbreaks, particularly in Asian agricultural systems, resulting in major yield losses and excessive pesticide usage. This study proposes a digital twin framework for paddy cultivation that integrates predictive disease modelling with real-time crop phenology simulation to support proactive disease management. The framework utilizes historical phenological records, IoT-based microclimatic sensor data, irrigation information, and field management records collected from 150 hectares of paddy fields in Maharashtra, India. A Spatio-Temporal Graph Neural Network (ST-GNN), integrating Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks, is employed to model spatial disease propagation and temporal crop dynamics for predicting blast, brown planthopper, and bacterial leaf blight outbreaks up to 14 days in advance. Experimental results demonstrate 87.3% prediction accuracy, 23.7% reduction in pesticide usage, and 31.2% reduction in crop losses, while outperforming traditional Random Forest and SVM models in F1-score performance. The proposed framework enables real-time decision support for sustainable precision agriculture and optimized resource management.","水稻生产极易受到病害暴发的影响，在亚洲农业体系中尤为突出，造成重大产量损失和农药过度使用。本研究提出一个水稻栽培数字孪生（digital twin）框架，将预测性病害建模与实时作物物候模拟相结合，以支持主动式病害管理。该框架利用从印度马哈拉施特拉邦150公顷稻田采集的历史物候记录、基于物联网的微气候传感器数据、灌溉信息及田间管理记录。采用融合图卷积网络（Graph Convolutional Networks, GCN）与长短期记忆网络（Long Short-Term Memory, LSTM）的时空图神经网络（Spatio-Temporal Graph Neural Network, ST-GNN），对病害空间传播与作物时序动态进行建模，以提前14天预测稻瘟病、褐飞虱和白叶枯病的暴发。实验结果表明，预测准确率达87.3%，农药使用量减少23.7%，作物损失减少31.2%，且在F1分数上优于传统随机森林（Random Forest）和支持向量机（SVM）模型。所提框架可为可持续精准农业和优化资源管理提供实时决策支持。","African Journal of Science Technology Innovation and Development","2026-10-19T00:00:00Z",{"impact":361,"substance":321,"depth":361,"authority":683,"freshness":760,"relevant":455,"comment":967},"方法新颖、数据规模明确且量化效果显著，属智慧农业细分领域实质进展，值得进入每日精选。",[969],{"name":964,"url":961},[590,591,210,971,972],"数字孪生","病虫害预警",{"code":208,"slug":209,"name":210,"path":211,"parent_code":200},[],{"status":600,"locked":582,"version":4,"method":601,"evidence":976},{"candidates":977,"agricultural_evidence":605},[978,986,992,998,1002,1008],{"code":16,"score":979,"supported":605,"title":980,"abstract":982,"tags":985},19,[981],"digital twin",[983,971,984,981],"精准农业","precision agriculture",[971,590],{"code":208,"score":361,"supported":605,"title":987,"abstract":989,"tags":991},[988],"paddy",[210,990,209,988],"稻田",[210],{"code":104,"score":719,"supported":605,"title":993,"abstract":994,"tags":997},[],[995,996],"农药","pesticide",[],{"code":152,"score":719,"supported":605,"title":999,"abstract":1000,"tags":1001},[],[722,153],[],{"code":48,"score":719,"supported":605,"title":1003,"abstract":1004,"tags":1007},[],[1005,1006],"物联网","IoT",[],{"code":24,"score":400,"supported":605,"title":1009,"abstract":1010,"tags":1011},[],[632],[591],[1013,1014],"水稻 病害预测 数字孪生","ST-GNN 水稻 病虫害","水稻病害预测数字孪生-5388","10.1080\u002F20421338.2026.2707951",{"doi":1016,"openalex_id":1018,"authors":1019,"venue":964,"cited_by_count":424,"oa_url":11,"card":1024,"direction":750,"ingested_from":670},"W7222969403",[1020,1022],{"name":1021,"orcid":11},"Jyoti Rani",{"name":1023,"orcid":11},"Dr.  Narendra S.  Jadhav",{"tldr":1025,"method":1026,"finding":1027,"direction":1028,"opportunity":1029},"构建水稻数字孪生框架，用时空图神经网络提前14天预测病害。","融合物联网微气候、物候与管理数据，用GCN+LSTM的ST-GNN建模。","预测准确率87.3%，农药减23.7%、损失减31.2%，优于RF与SVM。","农业人工智能与决策模型","可探索多尺度时空图建模与数字孪生闭环决策，并迁移至其他作物与区域。","2026-10-20T23:30:12.636299Z",{"id":1032,"title":1033,"url":1034,"summary":1035,"summary_zh":11,"content":11,"source_name":1036,"source_url":11,"published_at":1037,"category":581,"cover_url":11,"hotness":385,"is_selected":582,"score":899,"score_detail":1038,"sources":1040,"tags":1042,"primary_topic":1046,"secondary_topics":1047,"classification":1048,"search_phrases":1067,"slug":1070,"view_count":424,"doi":11,"paper":1071,"created_at":1078},5365,"基于经济型多光谱成像与知识蒸馏模型压缩的小麦线条花叶病毒早期检测","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-10\u002F20261017000601916.htm","研究人员利用配备六个光谱滤光片的消费级数码单反(DSLR)相机在自然环境条件下获取了一个新的小麦线条花叶病毒(WhSMV)多光谱数据集。评估十二个深度学习模型识别 WhSMV 感染不同阶段的性能,并首次将模型压缩技术应用于多光谱图像用于 WhSMV 感染的早期检测。比较了深度学习算法与压缩模型的性能,揭示压缩模型达到与大型深度学习模型相当的性能。WhSMV 是小麦土传媒介传播病毒,巴西与南非均有报道。","Smart Agricultural Technology 2026-10-17","2026-10-17T00:00:00Z",{"impact":361,"substance":321,"depth":682,"authority":683,"freshness":173,"relevant":455,"comment":1039},"该研究构建了新的多光谱数据集，并首次将模型压缩用于小麦病毒早期检测，方法新颖、结论可靠，对智慧农业植保具有参考价值。",[1041],{"name":1036,"url":1034},[590,591,1043,1044,1045],"多光谱成像","小麦病害","模型压缩",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[],{"status":600,"locked":582,"version":4,"method":601,"evidence":1049},{"candidates":1050,"agricultural_evidence":605},[1051,1055,1059,1063],{"code":216,"score":524,"supported":605,"title":1052,"abstract":1053,"tags":1054},[218],[218],[],{"code":24,"score":400,"supported":605,"title":1056,"abstract":1057,"tags":1058},[],[628],[591],{"code":16,"score":455,"supported":582,"title":1060,"abstract":1061,"tags":1062},[],[],[590],{"code":32,"score":455,"supported":582,"title":1064,"abstract":1065,"tags":1066},[],[],[1043],[1068,1069],"小麦线条花叶病毒 多光谱 检测","知识蒸馏 模型压缩 小麦","小麦线条花叶病毒多光谱检测-5365",{"doi":11,"openalex_id":11,"authors":1072,"venue":11,"cited_by_count":424,"oa_url":11,"card":1073,"direction":668,"ingested_from":804},[],{"tldr":1074,"method":1075,"finding":1076,"direction":668,"opportunity":1077},"用经济型多光谱成像结合知识蒸馏压缩模型，实现小麦线条花叶病毒早期检测。","消费级单反加六波段滤光片采集多光谱数据，评估12个深度学习模型并做模型压缩。","压缩模型性能与大型深度学习模型相当，可用于WhSMV感染早期识别。","可探索低成本多光谱设备与轻量模型在田间病害早期预警中的部署与跨区域泛化。","2026-10-20T02:47:54.487590Z",{"id":1080,"title":1081,"url":1082,"summary":1083,"summary_zh":11,"content":11,"source_name":1084,"source_url":11,"published_at":758,"category":581,"cover_url":11,"hotness":385,"is_selected":582,"score":1085,"score_detail":1086,"sources":1088,"tags":1090,"primary_topic":1093,"secondary_topics":1094,"classification":1095,"search_phrases":1114,"slug":1117,"view_count":424,"doi":11,"paper":1118,"created_at":1125},5362,"深度语义分割模型的时序泛化评估:无人机小麦倒伏监测 5 年跨年测试","https:\u002F\u002Fbioengineer.org\u002Fai-models-trained-on-one-year-of-wheat-fail-the-next-five-year-drone-study-shows\u002F","Zhang G., Wang X., Wang D., Sun D., Zhang Y., Zhang D. (2026) 利用 5 年田间试验评估深度语义分割模型在无人机(UAV)小麦倒伏监测中的时序泛化能力。研究发现:同年内高精度不能保证跨年可靠性,模型从训练年份迁移到其他年份时性能显著下降,基于注意力的模型通常比全卷积网络更稳健,可能因为层级窗口自注意力能整合更长范围上下文,而卷积强调局部模式;即便最优架构在大时序漂移下也出现严重退化。强调时序泛化测试应成为农业 AI 模型部署前的必备环节。DOI: 10.1016\u002Fj.atech.2026.102631。","Smart Agricultural Technology Vol.15 Article 102631 2026-10-16",79,{"impact":361,"substance":321,"depth":361,"authority":683,"freshness":173,"relevant":455,"comment":1087},"基于5年跨年田间试验揭示农业AI模型时序泛化瓶颈，结论扎实且具部署指导价值，值得入选。",[1089],{"name":1084,"url":1082},[590,591,766,1091,1092],"小麦倒伏","模型泛化",{"code":216,"slug":217,"name":218,"path":219,"parent_code":200},[],{"status":600,"locked":582,"version":4,"method":601,"evidence":1096},{"candidates":1097,"agricultural_evidence":605},[1098,1102,1106,1110],{"code":216,"score":524,"supported":605,"title":1099,"abstract":1100,"tags":1101},[218],[218],[],{"code":16,"score":455,"supported":582,"title":1103,"abstract":1104,"tags":1105},[],[],[590],{"code":24,"score":455,"supported":582,"title":1107,"abstract":1108,"tags":1109},[],[],[591],{"code":32,"score":455,"supported":582,"title":1111,"abstract":1112,"tags":1113},[],[],[766],[1115,1116],"无人机 小麦倒伏 监测","深度语义分割 时序泛化","无人机小麦倒伏监测-5362",{"doi":11,"openalex_id":11,"authors":1119,"venue":11,"cited_by_count":424,"oa_url":11,"card":1120,"direction":668,"ingested_from":804},[],{"tldr":1121,"method":1122,"finding":1123,"direction":668,"opportunity":1124},"用5年田间试验评估深度语义分割模型在无人机小麦倒伏监测中的跨年泛化能力。","5年无人机小麦倒伏影像，对比注意力模型与全卷积网络的跨年迁移表现。","同年内高精度不能保证跨年可靠，注意力模型比全卷积网络更稳健，但大时序漂移下仍严重退化。","可研究时序域自适应与跨年增量学习，建立农业AI模型部署前的时序泛化评测基准。","2026-10-20T02:47:54.289085Z",{"id":1127,"title":1128,"url":1129,"summary":1130,"summary_zh":11,"content":1131,"source_name":1132,"source_url":11,"published_at":11,"category":813,"cover_url":11,"hotness":385,"is_selected":582,"score":1133,"score_detail":1134,"sources":1138,"tags":1140,"primary_topic":1145,"secondary_topics":1146,"classification":1147,"search_phrases":1176,"slug":1179,"view_count":424,"doi":11,"paper":11,"created_at":1180},5341,"中国农科院联合发布植物抗逆 AI 智能体与盐稻芯 1 号耐盐水稻 11K 液相育种芯片","https:\u002F\u002Fwww.md2we.com\u002Fitems\u002F4944","国家盐碱地中心发布植物抗逆 AI 智能体与耐盐水稻 11K 液相育种芯片(盐稻芯 1 号)。植物抗逆 AI 智能体聚焦盐碱、干旱、高温、低温四大非生物逆境,以水稻等 6 个物种为研究核心,整合全球逆境文献与多源科研数据,依托 6 个 AI 智能体分工完成资料检索、数据分析、交叉复核、综合研判。盐稻芯 1 号以耐盐多组学证据定向选择位点,改变了耐盐水稻从生育期表型鉴定向苗期基因型选择的育种变革模式,相较全基因组重测序,该芯片成本仅为其三分之一。","### 两大科研成果重磅发布！盐碱地综合利用国家技术创新中心解锁抗逆育种智能新范式\n\n2026-10-15 04:44:38\n\n**大众网记者 唐梦琳 东营报道**\n\n9月19日，盐碱地综合利用国家技术创新中心（以下简称国家盐碱地中心）在总部举办新闻发布会，正式发布全球首个植物抗逆AI智能体与国内首个耐盐水稻11K液相育种芯片（盐稻芯1号）两项重大科研成果。发布会由中心综合事业部部长石东毅主持，中心副主任杨长军、耐盐碱植物微生物基因挖掘创新团队首席专家商连光发布。会上，杨长军介绍了成果开发背景与意义，商连光发布两项最新研究成果，并回答记者提问。\n\n![Image 1: Image 1](https:\u002F\u002Fwsrv.nl\u002F?url=https%3A%2F%2Fdzngimage.dzwww.com%2F2026%2F09%2F19%2F792a00e9232c00fdcf438be95889fed0.png)\n\n据介绍，两项成果由国家盐碱地中心联合中国农业科学院深圳农业基因组研究所、崖州湾国家实验室、山东省农业科学院湿地农业与生态研究所共同研发，以人工智能与分子育种技术深度融合，破解作物抗逆选育难题，为盐碱地综合利用、种业振兴和国家粮食安全注入强劲科技动能。\n\n植物抗逆AI智能体聚焦盐碱、干旱、高温、低温四大非生物逆境，以水稻等6个物种为研究核心，整合全球逆境文献与多源科研数据，依托6个AI 智能体分工完成资料检索、数据分析、交叉复核、综合研判，能够持续迭代学习科研成果，实现从单一胁迫研究向多逆境耦合研究范式升级。植物抗逆AI智能体可智能挖掘抗逆关键基因、显著压缩分子育种研发周期，作物抗逆上研究性能优于通用科研大模型，并将面向全球科研用户开放共享。\n\n与通用芯片按基因组位置均匀布点不同，耐盐水稻11K液相育种芯片（盐稻芯1号）以耐盐多组学证据定向选择位点，改变了耐盐水稻从生育期表型鉴定向苗期基因型选择的育种变革模式。相较全基因组重测序，该芯片成本仅为其三分之一，核心耐盐表型上，预测精度较同类芯片明显提升，育种科研人员可在苗期完成大规模群体耐盐筛选，为解决水稻耐盐研究中生育期和土地条件制约等问题提供了新的途径。\n\n两项成果取得了五个主要显著进展：一是推动研究范式由单一胁迫走向多逆境耦合；二是育种模式由经验筛选转向数据驱动精准选育；三是科研组织由分散探索升级为 AI 智能协同研究；四是芯片标记由全基因组铺点升级为多组学定向锚定；五是利用芯片将育种选择由大田表型鉴定前移至苗期基因型筛选。\n\n下一步，国家盐碱地中心将持续迭代升级植物抗逆AI智能体，丰富多物种、多场景科研数据集，优化算法模型；打通基础研究到育种应用链条，加速抗逆基因挖掘、种质创制和新品种选育；搭建全球逆境科研协同创新网络，深化与FAO、INSAS等国际组织交流合作，汇聚各方科研力量，联合破解植物逆境响应、气候适应性育种、边际土地高效利用等重大科学问题。\n\n发布会现场还介绍了国家盐碱地中心平台建设、人才集聚、科研攻关、成果转化等方面最新建设成效。\n\n免责声明：以上数据来源于天眼查等平台，相关内容仅为传递更多信息之目的，不代表本网观点，亦不代表本网站赞同其观点或证实其内容的真实性。如有侵权请联系0531-85193563。  \n 本文来源：海报新闻\n\n查看更多","AgriHot 转发 2026-10",83,{"impact":1135,"substance":1136,"depth":682,"authority":683,"freshness":874,"relevant":455,"comment":1137},27,23,"全球首个植物抗逆AI智能体与国内首个耐盐水稻11K液相育种芯片同期发布，属种业与农业AI领域的重大突破，但发布已逾一月，时效性偏弱。",[1139],{"name":1132,"url":1129},[591,1141,1142,1143,1144],"盐碱地利用","种业振兴","育种芯片","耐盐水稻",{"code":208,"slug":209,"name":210,"path":211,"parent_code":200},[],{"status":600,"locked":582,"version":4,"method":601,"evidence":1148},{"candidates":1149,"agricultural_evidence":605},[1150,1154,1159,1164,1168,1172],{"code":208,"score":524,"supported":605,"title":1151,"abstract":1152,"tags":1153},[210],[210],[],{"code":144,"score":400,"supported":605,"title":1155,"abstract":1156,"tags":1158},[],[1157],"盐碱地",[1141],{"code":184,"score":874,"supported":605,"title":1160,"abstract":1161,"tags":1163},[],[1162],"干旱",[],{"code":80,"score":455,"supported":582,"title":1165,"abstract":1166,"tags":1167},[],[],[1142],{"code":88,"score":455,"supported":582,"title":1169,"abstract":1170,"tags":1171},[],[],[1143],{"code":24,"score":455,"supported":582,"title":1173,"abstract":1174,"tags":1175},[],[],[591],[1177,1178],"盐稻芯1号 耐盐水稻","农业人工智能 盐碱地利用 种业振兴 耐盐水稻","盐稻芯1号耐盐水稻-5341","2026-10-20T02:47:43.721822Z",{"id":1182,"title":1183,"url":1184,"summary":1185,"summary_zh":11,"content":1186,"source_name":1187,"source_url":11,"published_at":1188,"category":813,"cover_url":11,"hotness":385,"is_selected":605,"score":1189,"score_detail":1190,"sources":1192,"tags":1194,"primary_topic":1197,"secondary_topics":1198,"classification":1199,"search_phrases":1228,"slug":1231,"view_count":424,"doi":11,"paper":11,"created_at":1232},5335,"中国农业科学院「农知育芯 BRIS」智能育种平台在洛阳发布 打造智能育种科研副脑","https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002Fa297dd7421fa4a3ba08c08863fbe3eb7.htm","10 月 14—16 日中国农学会图书情报分会 2026 年学术年会在河南洛阳举办。中国农业科学院农业信息研究所副所长赵瑞雪介绍,农知育芯(BRIS,Breeding Research Intelligent Service)集成 8 项分析工具、3 项自研算法,汇聚 2011—2025 年育种相关核心期刊文献,整合 6800 万条 NCBI 基因索引、57 万条 UniProt 专家校验蛋白信息;自主构建的育种知识图谱包含 7.9 万余个知识实体、12.6 万余条实体关系,覆盖水稻、玉米、小麦和拟南芥四类作物。","10月14日至16日，中国农学会图书情报分会2026年学术年会在河南洛阳举办。会议由中国农学会图书情报分会主办，中国农业科学院[农业信息研究所](http:\u002F\u002Faii.caas.cn\u002F)、河南省农业科学院农业信息技术研究所、洛阳市农林科学院联合承办。中国农业科学院[农业信息研究所](http:\u002F\u002Faii.caas.cn\u002F)副所长赵瑞雪在会上介绍农知育芯（BRIS，Breeding Research Intelligent Service）阶段性研究成果。农知育芯融合育种文献、权威数据库、知识图谱和分析算法，集成8项分析工具、3项自研算法，为研究人员提供知识获取、任务规划、证据推理全链条智能科研服务。\n\n智能育种是AI+农业的重要场景。随着高通量测序技术快速发展，育种科研数据快速增长，基因、性状和调控通路等知识分散于海量文献和数据库，候选基因筛选、功能解析和多组学研究面临知识检索繁琐、数据分析复杂、验证周期长和试错成本高等挑战。而通用大模型也难以充分满足专业知识、结果依据和溯源能力等科研要求。\n\n面向这一需求，农知育芯打通问答与计算、分析与决策、知识与工具三类能力。科研人员可通过自然语言提出问题或发起任务，系统自动调度工具与算法，汇聚文献、数据库、知识图谱及用户数据，形成分析结果与科研建议。在关键候选基因发现等任务中，平台可辅助科研人员将筛选过程由“大海捞针”转向“精准发现”，堪称智能育种的“科研副脑”。\n\n该平台以高质量知识图谱引导推理，结合分析工具计算、模型研判和知识增强开展协同验证，并对文献、数据库、工具和参数进行全链路核查，形成“问题—数据—结论”的科研闭环。\n\n据悉，农知育芯已汇聚2011年至2025年育种相关核心期刊文献，整合6800万条NCBI基因索引、57万条UniProt专家校验蛋白信息；自主构建的育种知识图谱包含7.9万余个知识实体、12.6万余条实体关系，覆盖水稻、玉米、小麦和拟南芥四类作物，形成文献、权威数据库和知识图谱互为校验的证据网络。\n\n该平台自主研发差异表达分析、GWAS、QTL定位、WGCNA共表达网络等8项经典生物信息与统计遗传分析工具，并构建多效基因挖掘、基因功能预测、通路与网络挖掘3项自研育种算法。\n\n依托上述知识资源、分析工具和算法能力，农知育芯面向育种科研人员提供智能问答和六大科研场景分析工具，涵盖关键候选基因发现、候选基因功能解析、分子机制与调控通路、基因型—表型—转录组联合研判、种质群体遗传解析和分析结果综合研判。\n\n该平台能提供快速问答、结构化分析和深度研究三类任务模式，可适配知识查询、多组学数据分析和综合育种前景评估等不同需求。系统可自动完成数据分析，汇聚多源文献证据，一键导出标准化PDF科研报告，并提供候选基因优先级排序和后续实验建议，支持PC端科研分析和移动端田间使用，服务育种科研人员和相关专业学生。","中国农业科学院 2026-10-14~16","2026-10-14T00:00:00Z",85,{"impact":859,"substance":321,"depth":361,"authority":613,"freshness":719,"relevant":455,"comment":1191},"国家级科研机构发布的智能育种平台，知识资源与分析工具细节扎实，属AI+农业重要进展，值得精选。",[1193],{"name":1187,"url":1184},[591,1142,864,1195,1196],"知识图谱","科研平台",{"code":200,"slug":201,"name":202,"path":203,"parent_code":11},[],{"status":1200,"locked":582,"version":4,"method":601,"evidence":1201},"partial",{"candidates":1202,"agricultural_evidence":605,"reason":1227},[1203,1207,1211,1215,1219,1223],{"code":208,"score":874,"supported":605,"title":1204,"abstract":1205,"tags":1206},[],[210],[],{"code":216,"score":874,"supported":605,"title":1208,"abstract":1209,"tags":1210},[],[218],[],{"code":224,"score":874,"supported":605,"title":1212,"abstract":1213,"tags":1214},[],[226],[],{"code":24,"score":69,"supported":582,"title":1216,"abstract":1217,"tags":1218},[],[],[591,1195],{"code":80,"score":455,"supported":582,"title":1220,"abstract":1221,"tags":1222},[],[],[1142],{"code":88,"score":455,"supported":582,"title":1224,"abstract":1225,"tags":1226},[],[],[864],"一级领域明确，二级方向需要细分",[1229,1230],"农知育芯 BRIS 智能育种","中国农科院 农业信息研究所 育种平台","农知育芯BRIS智能育种-5335","2026-10-20T02:47:43.320559Z",{"id":1234,"title":1235,"url":1236,"summary":1237,"summary_zh":1238,"content":11,"source_name":1239,"source_url":1236,"published_at":898,"category":581,"cover_url":11,"hotness":385,"is_selected":582,"score":1085,"score_detail":1240,"sources":1242,"tags":1244,"primary_topic":1246,"secondary_topics":1247,"classification":1248,"search_phrases":1268,"slug":1271,"view_count":424,"doi":1272,"paper":1273,"created_at":1294},5319,"Synergistic Use of Multitemporal SAR and Optical Data with Random Forest Approach for Rice Mapping in Bangladesh","https:\u002F\u002Fdoi.org\u002F10.31223\u002Fx5f22w","Accurate in-season crop mapping is vital for national food security, yet traditional ground-based estimation methods in Bangladesh often lack precision. This study develops a robust machine-learning framework to map Boro rice in the Manikganj district by integrating Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery. To overcome the challenge of seasonal cloud cover, Sentinel-1 VH polarization was utilized to identify unique backscatter reductions during the transplantation phase (late February to early March) caused by specular reflection from flooded fields. By combining these SAR signatures with high-resolution spectral indices (NDVI, EVI, and BSI) from Sentinel-2 during peak growth stages, the research captures the distinct phenological signatures of paddy rice. A pixel-based Random Forest algorithm was applied within the Google Earth Engine (GEE) platform, utilizing about 200 field-verified reference points per sub-district (1,385 in total) for training and validation. The results demonstrated high classification performance, with overall accuracies ranging from 88.1% to 94.9% across the seven sub-districts. Furthermore, a comparison with official government statistics revealed a minimal total area difference of only 2.34%, underscoring the reliability of this integrated satellite approach for operational crop monitoring.","准确的当季作物制图对国家粮食安全至关重要，然而孟加拉国传统的基于地面估计的方法往往缺乏精度。本研究开发了一个稳健的机器学习框架，通过整合Sentinel-1合成孔径雷达（SAR）和Sentinel-2光学影像，对马尼格甘杰县的Boro水稻进行制图。为克服季节性云覆盖的挑战，研究利用Sentinel-1 VH极化识别插秧期（2月下旬至3月上旬）由淹水田块镜面反射所引起的独特后向散射降低特征。通过将这些SAR特征与Sentinel-2在生长旺盛期的高分辨率光谱指数（NDVI、EVI和BSI）相结合，研究捕捉到了水稻独特的物候特征。在Google Earth Engine（GEE）平台上应用了基于像元的随机森林算法，利用每个乡约200个实地验证参考点（共计1，385个）进行训练和验证。结果表明分类性能较高，七个乡的总体精度在88.1%至94.9%之间。此外，与政府官方统计数据相比，总面积差异仅为2.34%，凸显了这种综合卫星方法在业务化作物监测中的可靠性。","OpenAlex",{"impact":361,"substance":321,"depth":361,"authority":416,"freshness":760,"relevant":455,"comment":1241},"方法扎实、精度可靠的水稻遥感制图研究，对农业遥感业务化监测有参考价值，但属区域性案例，影响层级有限。",[1243],{"name":1239,"url":1236},[210,923,629,1245,609],"作物制图",{"code":208,"slug":209,"name":210,"path":211,"parent_code":200},[],{"status":600,"locked":582,"version":4,"method":601,"evidence":1249},{"candidates":1250,"agricultural_evidence":605},[1251,1255,1260,1264],{"code":208,"score":361,"supported":605,"title":1252,"abstract":1253,"tags":1254},[209],[210,209,988],[210],{"code":263,"score":1256,"supported":605,"title":1257,"abstract":1258,"tags":1259},7,[],[923,924],[923],{"code":24,"score":400,"supported":605,"title":1261,"abstract":1262,"tags":1263},[],[629],[629],{"code":32,"score":455,"supported":582,"title":1265,"abstract":1266,"tags":1267},[],[],[609],[1269,1270],"Sentinel-1 Sentinel-2 水稻制图","孟加拉国 Boro 水稻 遥感","Sentinel-1Sentinel-2水稻制图-5319","10.31223\u002Fx5f22w",{"doi":1272,"openalex_id":1274,"authors":1275,"venue":11,"cited_by_count":424,"oa_url":1288,"card":1289,"direction":668,"ingested_from":670},"W7222810209",[1276,1278,1281,1284,1286],{"name":1277,"orcid":11},"Mst. Farida Perveen",{"name":1279,"orcid":1280},"Sravan Shrestha","https:\u002F\u002Forcid.org\u002F0000-0001-6979-1726",{"name":1282,"orcid":1283},"Abdullah Al Mehadi","https:\u002F\u002Forcid.org\u002F0000-0002-9002-633X",{"name":1285,"orcid":11},"Saima Kazmina",{"name":1287,"orcid":11},"Amio Al Saem","https:\u002F\u002Feartharxiv.org\u002Frepository\u002Fobject\u002F15552\u002Fdownload\u002F26951\u002F",{"tldr":1290,"method":1291,"finding":1292,"direction":668,"opportunity":1293},"融合Sentinel-1 SAR与Sentinel-2光学影像，用随机森林在GEE上绘制孟加拉国Bo","Sentinel-1 VH后向散射与Sentinel-2 NDVI\u002FEVI\u002FBS","七个分县总体精度88.1%–94.9%，与官方统计面积仅差2.34%。","可将该SAR-光学协同框架迁移至其他多云雨稻区，并探索多时相特征优选与跨季泛化。","2026-10-19T23:30:22.772950Z",{"id":1296,"title":1297,"url":1298,"summary":1299,"summary_zh":1300,"content":11,"source_name":1301,"source_url":1298,"published_at":898,"category":581,"cover_url":11,"hotness":385,"is_selected":582,"score":1302,"score_detail":1303,"sources":1305,"tags":1307,"primary_topic":1310,"secondary_topics":1311,"classification":1312,"search_phrases":1352,"slug":1355,"view_count":424,"doi":1356,"paper":1357,"created_at":1413},5314,"A data‐driven image extraction and analysis pipeline for plant phenotyping in controlled environments","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fppj2.70116","Abstract Advances in automation, imaging, and artificial intelligence have enabled large‐scale plant phenotyping, but image analysis remains a critical bottleneck for crop improvement and biological discovery. We developed an integrated multispectral phenotyping framework using imagery from the Texas A&M AgriLife Automated Precision Phenotyping Greenhouse and expanded plant growth and phenotyping (PGP v2) data across maize ( Zea mays L.), cotton ( Gossypium hirsutum ), rice ( Oryza sativa ), and sorghum ( Sorghum bicolor L.). The pipeline integrates pseudo‐red‐green‐blue composite image generation, plant detection and segmentation, image stitching, vegetation‐index analysis, texture analysis, morphological trait extraction, and temporal comparison of image‐derived features to quantify changes in plant structure, spectral reflectance, and texture over time. Among the evaluated segmentation approaches, segment anything model (SAM) v3 provided the highest and most consistent accuracy across diverse crop structures, although it required greater computational time than classical methods. Segment anything model for long sequences (SAM2Long)maintained plant‐instance associations across vertically stacked frames, while scale‐invariant feature transform–based stitching reconstructed plant mosaics when individual plants extended beyond a single field of view. For each plant and imaging date, the pipeline generated an 863‐dimensional feature vector spanning vegetation indices, spectral statistics, texture descriptors, and morphological traits. The framework was evaluated through two case studies: treatment‐level temporal analysis of mutagenized sorghum lines and cold‐stress phenotyping of maize using a separate imaging system. In both studies, the extracted features supported statistical and multivariate analyses of phenotypic variation and enabled separation of plants based on treatment‐ or stress‐related responses. The combined dataset and workflow provide structured, automated, and well‐documented phenotypic analysis across multiple crops, experimental settings, and imaging systems for controlled‐environment plant science and crop improvement.","摘要　自动化、成像和人工智能的进步使得大规模植物表型分析成为可能，但图像分析仍然是作物改良和生物学发现的关键瓶颈。我们利用德克萨斯农工大学农业生命科学自动化精准表型温室（Texas A&M AgriLife Automated Precision Phenotyping Greenhouse）的图像，开发了一个集成多光谱表型分析框架，并扩展了玉米（Zea mays L.）、棉花（Gossypium hirsutum）、水稻（Oryza sativa）和高粱（Sorghum bicolor L.）的植物生长与表型分析（plant growth and phenotyping，PGP v2）数据。该流程整合了伪红绿蓝合成图像生成、植物检测与分割、图像拼接、植被指数分析、纹理分析、形态性状提取以及图像衍生特征的时间比较，以量化植物结构、光谱反射率和纹理随时间的变化。在评估的分割方法中，segment anything model（SAM）v3在不同作物结构下提供了最高且最一致的准确性，尽管其所需计算时间多于经典方法。面向长序列的segment anything model（SAM2Long）在垂直堆叠帧之间维持了植物实例关联，而基于尺度不变特征变换（scale-invariant feature transform）的拼接在单株植物超出单一视野时重建了植物拼接图。对于每株植物和每个成像日期，该流程生成了一个863维特征向量，涵盖植被指数、光谱统计量、纹理描述符和形态性状。该框架通过两个案例研究进行了评估：诱变高粱品系的处理水平时间分析，以及使用独立成像系统对玉米进行的冷胁迫表型分析。在这两项研究中，提取的特征支持了表型变异的统计分析和多变量分析，并能够根据处理或胁迫相关响应区分植物。合并的数据集和工作流程为受控环境植物科学和作物改良提供了跨多种作物、实验设置和成像系统的结构化、自动化且记录完善的表型分析。","The Plant Phenome Journal",81,{"impact":361,"substance":321,"depth":361,"authority":345,"freshness":760,"relevant":455,"comment":1304},"该研究提出一套面向多作物的自动化多光谱表型分析流程，方法新颖、数据规模可观，对智慧育种与受控环境植物科学研究具有较高参考价值。",[1306],{"name":1301,"url":1298},[590,591,1308,1309,1043],"图像分割","表型组学",{"code":200,"slug":201,"name":202,"path":203,"parent_code":11},[],{"status":1200,"locked":582,"version":4,"method":601,"evidence":1313},{"candidates":1314,"agricultural_evidence":605,"reason":1227},[1315,1319,1323,1329,1333,1339,1344,1348],{"code":208,"score":719,"supported":605,"title":1316,"abstract":1317,"tags":1318},[],[210,209],[],{"code":224,"score":719,"supported":605,"title":1320,"abstract":1321,"tags":1322},[],[226,225],[],{"code":240,"score":719,"supported":605,"title":1324,"abstract":1325,"tags":1328},[],[1326,1327],"高粱","sorghum",[],{"code":248,"score":719,"supported":605,"title":1330,"abstract":1331,"tags":1332},[],[781,783],[],{"code":324,"score":719,"supported":605,"title":1334,"abstract":1335,"tags":1338},[],[1336,1337],"温室","greenhouse",[],{"code":24,"score":408,"supported":605,"title":1340,"abstract":1341,"tags":1343},[],[1342],"artificial intelligence",[591,1308],{"code":32,"score":400,"supported":605,"title":1345,"abstract":1346,"tags":1347},[],[716],[1043],{"code":16,"score":455,"supported":582,"title":1349,"abstract":1350,"tags":1351},[],[],[590],[1353,1354],"Texas A&M 自动化表型温室","玉米 高粱 多光谱表型","TexasAM自动化表型温室-5314","10.1002\u002Fppj2.70116",{"doi":1356,"openalex_id":1358,"authors":1359,"venue":1301,"cited_by_count":424,"oa_url":1298,"card":1408,"direction":668,"ingested_from":670},"W7222893982",[1360,1362,1365,1368,1371,1373,1375,1378,1381,1384,1387,1390,1393,1396,1399,1402,1404,1406],{"name":1361,"orcid":11},"Fahimeh Orvati Nia",{"name":1363,"orcid":1364},"Joshua Peeples","https:\u002F\u002Forcid.org\u002F0000-0001-7861-7789",{"name":1366,"orcid":1367},"Seth C. Murray","https:\u002F\u002Forcid.org\u002F0000-0002-2960-8226",{"name":1369,"orcid":1370},"Andrew J. McFarland","https:\u002F\u002Forcid.org\u002F0009-0000-3516-8274",{"name":1372,"orcid":11},"Troy Vann",{"name":1374,"orcid":11},"Shima Salehi",{"name":1376,"orcid":1377},"Robert G. Hardin","https:\u002F\u002Forcid.org\u002F0000-0002-0714-8281",{"name":1379,"orcid":1380},"David D. Baltensperger","https:\u002F\u002Forcid.org\u002F0000-0003-4511-5680",{"name":1382,"orcid":1383},"Amir M. H. Ibrahim","https:\u002F\u002Forcid.org\u002F0000-0002-0584-753X",{"name":1385,"orcid":1386},"J. Alex Thomasson","https:\u002F\u002Forcid.org\u002F0000-0002-5557-105X",{"name":1388,"orcid":1389},"Henry Y. Fadamiro","https:\u002F\u002Forcid.org\u002F0000-0002-4530-9680",{"name":1391,"orcid":1392},"Nithya K. Subramanian","https:\u002F\u002Forcid.org\u002F0000-0002-1659-7396",{"name":1394,"orcid":1395},"Suresh D. Pillai","https:\u002F\u002Forcid.org\u002F0000-0002-9766-8765",{"name":1397,"orcid":1398},"Rebecca Roston","https:\u002F\u002Forcid.org\u002F0000-0002-3063-5002",{"name":1400,"orcid":1401},"Joslin Ishimwe","https:\u002F\u002Forcid.org\u002F0009-0003-6999-7718",{"name":1403,"orcid":11},"Diptadeep Basak",{"name":1405,"orcid":11},"Nazar Oladepo",{"name":1407,"orcid":11},"Uday Santhosh Raju Vysyaraju",{"tldr":1409,"method":1410,"finding":1411,"direction":668,"opportunity":1412},"构建多光谱图像提取分析流程，实现多作物表型自动化量化。","用温室多光谱图像，集成SAM v3分割、SIFT拼接与863维特征提取。","SAM v3分割最准，特征可区分高粱诱变与玉米冷胁迫响应。","可探索轻量化分割模型与跨成像系统迁移，降低计算成本并拓展田间表型。","2026-10-19T23:30:17.276726Z",{"id":1415,"title":1416,"url":1417,"summary":1418,"summary_zh":1419,"content":11,"source_name":1420,"source_url":1417,"published_at":1037,"category":581,"cover_url":11,"hotness":385,"is_selected":582,"score":45,"score_detail":1421,"sources":1423,"tags":1425,"primary_topic":1428,"secondary_topics":1429,"classification":1430,"search_phrases":1447,"slug":1450,"view_count":455,"doi":1451,"paper":1452,"created_at":1466},5234,"A smart agriculture-oriented cross-attention multiscale CNN for sugarcane disease diagnosis","https:\u002F\u002Fdoi.org\u002F10.1080\u002F23080477.2026.2749739","Sugarcane leaf diseases can adversely affect crop health and yield, making timely disease identification important for smart agriculture. Manual inspection of leaf diseases is time-consuming and requires expert knowledge, particularly when large cultivation areas need to be monitored. To tackle this problem, this study proposes a Cross-Attention Multiscale Parallel Convolutional Neural Network for automatic classification of sugarcane leaf diseases. This framework comprises a Parallel Feature Extraction Stem, cross-attention mechanism, Dense Connection Blocks, and Inception Blocks to learn disease features at various spatial scales. The PFES extracts fine and broad disease patterns using parallel convolutional branches, while the cross-attention mechanism refines the extracted features by emphasizing disease-related information. The DCB supports feature propagation and reuse, and the IB further captures multi-scale feature representations. The proposed model was tested on two benchmark datasets of leaf diseases for sugarcane plants. The proposed framework obtained an accuracy (98.79%), precision (98.31%), recall (97.81%), specificity (98.60%), and F1-Score (98.05%). For assessing the classification accuracy and consistency of the framework, the comparative study, ablation studies, and five-fold cross-validation are carried out. The results show that CA-MPCNN provides accurate sugarcane leaf disease classification and can support automated disease diagnosis in smart agriculture applications.","甘蔗叶片病害会对作物健康与产量造成不利影响，因此及时识别病害对智慧农业具有重要意义。人工检查叶片病害耗时且需要专业知识，尤其是在需要监测大面积种植区域时。为解决该问题，本研究提出一种用于甘蔗叶片病害自动分类的交叉注意力多尺度并行卷积神经网络（Cross-Attention Multiscale Parallel Convolutional Neural Network）。该框架由并行特征提取主干（Parallel Feature Extraction Stem）、交叉注意力机制、密集连接块（Dense Connection Blocks）和Inception块组成，用于学习不同空间尺度上的病害特征。PFES通过并行卷积分支提取细粒度与广域病害模式，而交叉注意力机制通过强调病害相关信息对提取的特征进行细化。DCB支持特征传播与复用，IB进一步捕获多尺度特征表示。所提出的模型在两个甘蔗植株叶片病害基准数据集上进行了测试。该框架取得了准确率（98.79%）、精确率（98.31%）、召回率（97.81%）、特异性（98.60%）和F1分数（98.05%）。为评估该框架的分类准确性和一致性，开展了对比研究、消融研究和五折交叉验证。结果表明，CA-MPCNN能够实现准确的甘蔗叶片病害分类，并可支持智慧农业应用中的自动化病害诊断。","Smart Science",{"impact":416,"substance":508,"depth":585,"authority":416,"freshness":760,"relevant":455,"comment":1422},"该研究提出一种面向甘蔗叶部病害自动分类的交叉注意力多尺度CNN，在两个基准数据集上准确率达98.79%，方法新颖、实验充分，但属于细分作物应用研究，产业影响有限，时效性强，值得进入每日精选。",[1424],{"name":1420,"url":1417},[590,591,1426,628,1427],"甘蔗","病害诊断",{"code":248,"slug":249,"name":250,"path":251,"parent_code":200},[],{"status":600,"locked":582,"version":4,"method":601,"evidence":1431},{"candidates":1432,"agricultural_evidence":605},[1433,1438,1443],{"code":248,"score":613,"supported":605,"title":1434,"abstract":1436,"tags":1437},[1435],"sugarcane",[1426,1435],[1426],{"code":16,"score":613,"supported":605,"title":1439,"abstract":1441,"tags":1442},[1440],"smart agriculture",[590,1440],[590],{"code":24,"score":69,"supported":582,"title":1444,"abstract":1445,"tags":1446},[],[],[591,628],[1448,1449],"甘蔗 叶部病害 智能诊断","CA-MPCNN 甘蔗 病害","甘蔗叶部病害智能诊断-5234","10.1080\u002F23080477.2026.2749739",{"doi":1451,"openalex_id":1453,"authors":1454,"venue":1420,"cited_by_count":424,"oa_url":11,"card":1461,"direction":750,"ingested_from":670},"W7222832245",[1455,1457,1459],{"name":1456,"orcid":11},"Bala Subramanian Chokkalingam",{"name":1458,"orcid":11},"Uvarani Meenrajan",{"name":1460,"orcid":11},"Shanmugapriya Sankaran",{"tldr":1462,"method":1463,"finding":1464,"direction":1028,"opportunity":1465},"提出交叉注意力多尺度并行CNN，实现甘蔗叶病自动分类。","并行卷积分支、交叉注意力、密集连接与Inception块，两个基准数据集验证。","模型准确率达98.79%，消融与五折交叉验证证明其有效稳定。","可探索田间复杂背景与轻量化部署，并扩展到多作物病害实时诊断。","2026-10-18T23:30:07.777036Z",{"id":1468,"title":1469,"url":1470,"summary":1471,"summary_zh":1472,"content":11,"source_name":1473,"source_url":1470,"published_at":1037,"category":581,"cover_url":11,"hotness":385,"is_selected":582,"score":1474,"score_detail":1475,"sources":1477,"tags":1479,"primary_topic":1481,"secondary_topics":1482,"classification":1483,"search_phrases":1502,"slug":1505,"view_count":455,"doi":1506,"paper":1507,"created_at":1515},5233,"Cotton pest and disease multi-class classification with weather and temporal predictors features across Indian agroclimatic zones","https:\u002F\u002Fdoi.org\u002F10.25258\u002Fijddt.16.73s.45","The Pink Bollworm (Pectinophora gossypiella) remains one of the most destructive pests affecting cotton productivity, with infestation levels strongly driven by climatic fluctuations and crop phenology.This study presents a machine learning-based framework for early multi-classification and prediction of Pink Bollworm incidence using agro-meteorological variables.The dataset includes maximum and minimum air temperature, relative humidity, rainfall, sunshine hours, evaporation, and wind speed, all of which influence Pink Bollworm development, migration, and reproductive cycles.After preprocessing, applying cyclic encoding to temporal variables, and generating lagged climatic features, multiple machine learning algorithms-including SVM, Neural Networks, KNN, Regression models, Ensemble methods, and Naïve Bayes-were trained and systematically compared.SVM model demonstrated the best test accuracy, effectively capturing dynamic climate-pest interactions.The resulting MATLAB-based classification tool enables rapid assessment and supports climate-informed pest surveillance.A stratified 80:20 train-test split was employed, with 5-fold cross-validation on the training set for hyperparameter tuning, ensuring no data leakage between partitions.The validation and training accuracies of these models are 89.4% and 96 % respectively.Overall, the model offers a practical and interpretable solution for early detection of Pink Bollworm risk, thereby contributing to informed decision-making in integrated pest management (IPM) and precision agriculture.","棉红铃虫（Pectinophora gossypiella）仍是影响棉花产量最具破坏性的害虫之一，其发生程度受气候波动和作物物候的强烈驱动。本研究提出了一种基于机器学习的框架，利用农业气象变量对棉红铃虫的发生进行早期多分类与预测。数据集包括最高和最低气温、相对湿度、降雨量、日照时数、蒸发量和风速，这些因素均影响棉红铃虫的发育、迁移和繁殖周期。经过预处理、对时间变量应用循环编码并生成滞后气候特征后，训练并系统比较了多种机器学习算法，包括支持向量机（SVM）、神经网络、K近邻（KNN）、回归模型、集成方法和朴素贝叶斯。SVM模型表现出最佳的测试准确率，有效捕捉了气候与害虫之间的动态相互作用。所构建的基于MATLAB的分类工具可实现快速评估，并支持气候信息驱动的害虫监测。采用分层80：20的训练-测试划分，并在训练集上进行5折交叉验证以调优超参数，确保各分区之间无数据泄漏。这些模型的验证准确率和训练准确率分别为89.4%和96%。总体而言，该模型为棉红铃虫风险的早期检测提供了一种实用且可解释的解决方案，从而有助于综合害虫管理（IPM）和精准农业中的科学决策。","International Journal of Drug Delivery Technology",55,{"impact":173,"substance":361,"depth":345,"authority":719,"freshness":760,"relevant":455,"comment":1476},"研究用多类机器学习模型结合气象与时序特征预测棉铃虫风险，方法扎实但属细分领域应用研究，信源期刊权威性一般，公共影响有限。",[1478],{"name":1473,"url":1470},[590,629,781,1480,972],"农业气象",{"code":248,"slug":249,"name":250,"path":251,"parent_code":200},[],{"status":600,"locked":582,"version":4,"method":601,"evidence":1484},{"candidates":1485,"agricultural_evidence":605},[1486,1490,1494,1498],{"code":248,"score":613,"supported":605,"title":1487,"abstract":1488,"tags":1489},[783],[781,783],[781],{"code":16,"score":1256,"supported":605,"title":1491,"abstract":1492,"tags":1493},[],[983,984],[590],{"code":24,"score":1256,"supported":605,"title":1495,"abstract":1496,"tags":1497},[],[629,630],[629],{"code":184,"score":400,"supported":605,"title":1499,"abstract":1500,"tags":1501},[],[1480],[1480],[1503,1504],"棉铃虫 机器学习 预测","棉花 病虫害 气象因子","棉铃虫机器学习预测-5233","10.25258\u002Fijddt.16.73s.45",{"doi":1506,"openalex_id":1508,"authors":1509,"venue":1473,"cited_by_count":424,"oa_url":1470,"card":1510,"direction":750,"ingested_from":670},"W7222826491",[],{"tldr":1511,"method":1512,"finding":1513,"direction":1028,"opportunity":1514},"用农业气象变量与时间特征构建机器学习模型，实现印度棉区棉铃虫多级发生预测。","气象数据加周期编码与滞后特征，比较SVM等模型，5折交叉验证。","SVM测试精度最高达89.4%，能有效刻画气候与虫害动态关系。","可引入遥感植被指数与实时气象物联网数据，构建跨区域可迁移的病虫害预警系统。","2026-10-18T23:30:07.725427Z",{"id":1517,"title":1518,"url":1519,"summary":1520,"summary_zh":1521,"content":11,"source_name":1522,"source_url":1519,"published_at":758,"category":581,"cover_url":11,"hotness":385,"is_selected":582,"score":45,"score_detail":1523,"sources":1525,"tags":1527,"primary_topic":1529,"secondary_topics":1530,"classification":1532,"search_phrases":1551,"slug":1554,"view_count":455,"doi":1555,"paper":1556,"created_at":1589},5164,"Lightweight Hybrid CNN-Transformer Network for Robust Maize Leaf Disease Detection and Edge Deployment","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1130.50","Early and accurate detection of maize leaf diseases is essential for improving crop productivity and supporting precision agriculture.However, deep learning models trained on controlled leaf-image datasets often experience performance degradation under real-world agricultural conditions because of illumination variation, background clutter, occlusion, leaf orientation, and imaging noise.To address these challenges, this study develops a lightweight deep learning framework for robust maize leaf disease classification and edge-oriented deployment.The framework employs MobileNetV3-Small for efficient extraction of disease-discriminative visual features and investigates lightweight feature-learning strategies for improving classification under challenging field conditions.Experiments were conducted using PlantVillage-Maize and Field-Maize datasets comprising 6,188 images from four categories: Maize Leaf Blight (MLB), Common Rust, Healthy, and Leaf Spot.The image-only MobileNetV3-Small model achieved 97.8% accuracy and a 97.4% F1-score on the controlled PlantVillage-Maize dataset, while its performance decreased to 86.1% accuracy and an 83.9% F1-score on Field-Maize, highlighting the domain gap between controlled and real-field imagery.Robustness was further evaluated under Gaussian noise, illumination variation, and partial occlusion, together with computational-efficiency and edge-deployment analyses.The findings demonstrate the importance of lightweight and robust visual feature learning for practical maize disease diagnosis under real-world agricultural imaging conditions.","早期准确检测玉米叶片病害对于提高作物生产力和支持精准农业至关重要。然而，在受控叶片图像数据集上训练的深度学习模型，在真实农业条件下往往会出现性能下降，原因包括光照变化、背景杂乱、遮挡、叶片朝向以及成像噪声。为应对这些挑战，本研究开发了一个轻量级深度学习框架，用于稳健的玉米叶片病害分类和面向边缘的部署。该框架采用MobileNetV3-Small高效提取具有病害判别力的视觉特征，并研究轻量级特征学习策略，以提升在复杂田间条件下的分类性能。实验使用PlantVillage-Maize和Field-Maize数据集进行，共包含来自四个类别的6，188张图像：玉米大斑病（Maize Leaf Blight，MLB）、普通锈病（Common Rust）、健康（Healthy）和叶斑病（Leaf Spot）。仅使用图像的MobileNetV3-Small模型在受控的PlantVillage-Maize数据集上达到了97.8%的准确率和97.4%的F1分数，而在Field-Maize上的性能下降至86.1%的准确率和83.9%的F1分数，凸显了受控图像与真实田间图像之间的域差距。研究进一步在高斯噪声、光照变化和部分遮挡条件下评估了稳健性，并进行了计算效率和边缘部署分析。研究结果表明，轻量级且稳健的视觉特征学习对于真实农业成像条件下的实际玉米病害诊断具有重要意义。","International journal of intelligent engineering and systems",{"impact":416,"substance":508,"depth":585,"authority":416,"freshness":760,"relevant":455,"comment":1524},"该研究提出轻量级玉米叶病检测框架，并揭示受控数据集与田间数据之间的性能差距，方法扎实、结论有参考价值，但属细分技术论文，公共影响有限。",[1526],{"name":1522,"url":1519},[590,591,1528,628,821],"边缘计算",{"code":224,"slug":225,"name":226,"path":227,"parent_code":200},[1531],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":600,"locked":582,"version":4,"method":601,"evidence":1533},{"candidates":1534,"agricultural_evidence":605},[1535,1539,1543,1547],{"code":224,"score":345,"supported":605,"title":1536,"abstract":1537,"tags":1538},[225],[226,225],[],{"code":24,"score":173,"supported":605,"title":1540,"abstract":1541,"tags":1542},[],[628,631],[591,628],{"code":16,"score":1256,"supported":605,"title":1544,"abstract":1545,"tags":1546},[],[983,984],[590],{"code":48,"score":455,"supported":582,"title":1548,"abstract":1549,"tags":1550},[],[],[1528],[1552,1553],"MobileNetV3 玉米叶部病害","玉米叶片病害 边缘部署","MobileNetV3玉米叶部病害-5164","10.22266\u002Fijies2026.1130.50",{"doi":1555,"openalex_id":1557,"authors":1558,"venue":1522,"cited_by_count":424,"oa_url":1519,"card":1584,"direction":668,"ingested_from":670},"W7222602850",[1559,1562,1565,1567,1569,1572,1574,1576,1579,1581],{"name":1560,"orcid":1561},"Awwab Mohammad","https:\u002F\u002Forcid.org\u002F0000-0002-1148-5678",{"name":1563,"orcid":1564},"Punith kumar","https:\u002F\u002Forcid.org\u002F0009-0005-5676-4194",{"name":1566,"orcid":11},"Bhavani K",{"name":1568,"orcid":11},"Sailee N. Panchbhai",{"name":1570,"orcid":1571},"Sagarkumar S. Badhiye","https:\u002F\u002Forcid.org\u002F0000-0002-0710-3761",{"name":1573,"orcid":11},"Manoj Kumar S B",{"name":1575,"orcid":11},"Dharamvir",{"name":1577,"orcid":1578},"Sumit Kumar Sar","https:\u002F\u002Forcid.org\u002F0000-0003-0384-9088",{"name":1580,"orcid":11},"Rani Chhagan Dalvi",{"name":1582,"orcid":1583},"Yogesh Harishchandra Bhosale","https:\u002F\u002Forcid.org\u002F0000-0001-6901-1419",{"tldr":1585,"method":1586,"finding":1587,"direction":1028,"opportunity":1588},"构建轻量MobileNetV3-Small框架实现玉米叶片病害分类并评估边缘部署。","MobileNetV3-Small，PlantVillage-Maize与Fie","受控数据准确率97.8%，田间降至86.1%，揭示明显域差距。","可研究跨域自适应与田间鲁棒特征学习，缩小受控与真实场景性能差距。","2026-10-17T23:30:28.248539Z",{"id":1591,"title":1592,"url":1593,"summary":1594,"summary_zh":1595,"content":11,"source_name":1596,"source_url":1593,"published_at":758,"category":581,"cover_url":11,"hotness":385,"is_selected":582,"score":1597,"score_detail":1598,"sources":1600,"tags":1602,"primary_topic":1605,"secondary_topics":1606,"classification":1608,"search_phrases":1624,"slug":1627,"view_count":455,"doi":1628,"paper":1629,"created_at":1663},5150,"MSFANet: a multi-scale feature aggregation network for tobacco pest recognition","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1871587","Introduction Tobacco is a globally significant economic crop, and pest infestation represents one of the primary factors contributing to declines in both yield and quality. However, tobacco pests encompass a wide variety of species that differ substantially in morphology and scale. Field-captured images are frequently subject to complex background interference and class imbalance, presenting considerable challenges for automated recognition. Existing deep learning approaches remain limited in their capacity for multi-scale feature extraction and discriminative feature representation under such conditions. To address these challenges, this study proposes a Multi-Scale Feature Aggregation Network (MSFANet) for tobacco pest recognition, formulated here as a single-label image classification task. Methods To address scale variation and complex field backgrounds, two task-oriented modules were developed. The Multi-Scale Pyramid Pooling (MSPP) module aggregates contextual information across multiple receptive fields through parallel feature extraction and adaptive cross-branch fusion. The Hierarchical Feature Fusion Block (HFFB) subsequently refines the feature representations through spatial gating and partial convolution. An auxiliary classification head and Focal Loss were incorporated during training to provide intermediate supervision and address class imbalance, respectively. Results The effectiveness of MSFANet was evaluated on a dataset comprising 13 tobacco-associated pest and natural-enemy categories. The model achieved accuracy, precision, recall, and F1-score of 97.63%, 97.89%, 97.59%, and 97.71%, respectively, with a parameter count of 1.21 M. Among the models trained from random initialization, MSFANet shared the highest accuracy while using the fewest parameters. Discussion By combining multi-scale contextual aggregation with hierarchical feature refinement, MSFANet achieved accurate classification across the 13 tobacco-associated categories under complex field conditions. The proposed method provides effective technical support for automated and precision-oriented pest monitoring in tobacco fields, offering practical value for advancing precise pest management and the broader development of smart agriculture.","引言 烟草是全球重要的经济作物，虫害侵染是导致其产量和品质下降的主要因素之一。然而，烟草害虫种类繁多，形态和尺度差异显著。田间采集的图像常受到复杂背景干扰和类别不平衡的影响，给自动化识别带来了相当大的挑战。现有深度学习方法在此类条件下进行多尺度特征提取和判别性特征表示的能力仍然有限。为应对这些挑战，本研究提出了一种用于烟草害虫识别的多尺度特征聚合网络（Multi-Scale Feature Aggregation Network，MSFANet），并将其形式化为单标签图像分类任务。方法 针对尺度变化和复杂田间背景，开发了两个面向任务的模块。多尺度金字塔池化（Multi-Scale Pyramid Pooling，MSPP）模块通过并行特征提取和自适应跨分支融合，聚合多个感受野的上下文信息。分层特征融合模块（Hierarchical Feature Fusion Block，HFFB）随后通过空间门控和部分卷积对特征表示进行细化。训练过程中引入辅助分类头和Focal Loss，分别提供中间监督和处理类别不平衡。结果 在包含13个烟草相关害虫及天敌类别的数据集上评估了MSFANet的有效性。模型的准确率、精确率、召回率和F1分数分别为97.63%、97.89%、97.59%和97.71%，参数量为1.21 M。在随机初始化训练的模型中，MSFANet以最少的参数量取得了最高准确率。讨论 通过结合多尺度上下文聚合与分层特征细化，MSFANet在复杂田间条件下实现了对13个烟草相关类别的准确分类。所提方法为烟草田间害虫的自动化和精准监测提供了有效的技术支持，对推进精准害虫管理和智慧农业的更广泛发展具有实用价值。","Frontiers in Plant Science",76,{"impact":585,"substance":681,"depth":682,"authority":683,"freshness":760,"relevant":455,"comment":1599},"该论文提出轻量化多尺度特征聚合网络，在烟草害虫识别上取得97%以上精度且参数量仅1.21M，方法新颖、数据扎实，对智慧农业虫情监测有实用价值，值得进入每日精选。",[1601],{"name":1596,"url":1593},[590,591,628,1603,1604],"病虫害识别","烟草",{"code":248,"slug":249,"name":250,"path":251,"parent_code":200},[1607],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":600,"locked":582,"version":4,"method":601,"evidence":1609},{"candidates":1610,"agricultural_evidence":605},[1611,1616,1620],{"code":248,"score":613,"supported":605,"title":1612,"abstract":1614,"tags":1615},[1613],"tobacco",[1604,1613],[1604],{"code":24,"score":760,"supported":605,"title":1617,"abstract":1618,"tags":1619},[],[628,631],[591,628,1603],{"code":16,"score":1256,"supported":605,"title":1621,"abstract":1622,"tags":1623},[],[590,1440],[590],[1625,1626],"MSFANet 烟草害虫识别","多尺度特征聚合 害虫识别","MSFANet烟草害虫识别-5150","10.3389\u002Ffpls.2026.1871587",{"doi":1628,"openalex_id":1630,"authors":1631,"venue":1596,"cited_by_count":424,"oa_url":1657,"card":1658,"direction":750,"ingested_from":670},"W7222603139",[1632,1634,1636,1638,1640,1642,1644,1646,1649,1652,1655],{"name":1633,"orcid":11},"Shun He",{"name":1635,"orcid":11},"Sifan Li",{"name":1637,"orcid":11},"Xinjian Li",{"name":1639,"orcid":11},"Qianqian Luo",{"name":1641,"orcid":11},"Dong Sufen",{"name":1643,"orcid":11},"Lu Lei",{"name":1645,"orcid":11},"Xingjiang Chen",{"name":1647,"orcid":1648},"Liuti Cai","https:\u002F\u002Forcid.org\u002F0000-0002-9151-8002",{"name":1650,"orcid":1651},"Jian‐Yu Meng","https:\u002F\u002Forcid.org\u002F0000-0002-5796-1086",{"name":1653,"orcid":1654},"Ning Lu","https:\u002F\u002Forcid.org\u002F0000-0003-0214-5038",{"name":1656,"orcid":11},"Hancheng Wang","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1871587\u002Fpdf",{"tldr":1659,"method":1660,"finding":1661,"direction":1028,"opportunity":1662},"提出MSFANet多尺度特征聚合网络，实现复杂田间背景下烟草害虫的自动识别。","多尺度金字塔池化MSPP、分层特征融合HFFB、辅助分类头与Focal Loss","模型准确率97.63%、参数量仅1.21M，在随机初始化模型中精度最高且参数最少。","可探索轻量多尺度网络在边缘设备部署及跨作物害虫迁移识别，结合类不平衡与复杂背景的鲁棒性研究。","2026-10-17T23:30:08.695455Z",{"id":1665,"title":1666,"url":1667,"summary":1668,"summary_zh":11,"content":11,"source_name":1669,"source_url":11,"published_at":1670,"category":581,"cover_url":11,"hotness":385,"is_selected":582,"score":1671,"score_detail":1672,"sources":1674,"tags":1676,"primary_topic":1679,"secondary_topics":1680,"classification":1681,"search_phrases":1705,"slug":1708,"view_count":874,"doi":11,"paper":1709,"created_at":1716},5064,"Detection of Potato Plant Leaf Diseases using Convolutional Neural Networks","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.20544","Haimanot Bitew Alemayehu、Simachew Melaku Ayalew、Chilotie Desalew Mengesha、Ayisheshim Almaw Bogale、Anteneh Birhan Yenew、Abrham Tegegne Bogale在arXiv 2026-10-15上传的预印本,面向埃塞俄比亚粮食安全作物马铃薯,提出可部署于边缘设备的轻量卷积神经网络,完成健康、早疫病、晚疫病三类分类。模型在PlantVillage(2,215张图像)训练,测试准确率98.66%;在埃塞俄比亚Banja镇田间独立采集的247张图像测试中准确率为90.28%,体现独立田间验证相较受控基准更接近真实部署条件。模型仅含446,787个可训练参数、约1.70MB,训练周期较VGG16和MobileNetV2更短。【预印本未审稿;仅在埃塞俄比亚一地进行田间验证,跨地区泛化能力未测】","arXiv preprint 2026-10-15","2026-10-15T00:00:00Z",71,{"impact":613,"substance":508,"depth":682,"authority":760,"freshness":385,"relevant":455,"comment":1673},"预印本提出仅1.7MB的轻量CNN实现马铃薯早疫病、晚疫病识别，含独立田间验证，方法扎实但尚未审稿、泛化性待验证，属细分领域技术进展。",[1675],{"name":1669,"url":1667},[590,591,1677,1528,1678],"马铃薯","病害识别",{"code":240,"slug":241,"name":242,"path":243,"parent_code":200},[],{"status":600,"locked":582,"version":4,"method":601,"evidence":1682},{"candidates":1683,"agricultural_evidence":605},[1684,1689,1693,1697,1701],{"code":240,"score":416,"supported":605,"title":1685,"abstract":1687,"tags":1688},[1686],"potato",[1677],[1677],{"code":263,"score":874,"supported":605,"title":1690,"abstract":1691,"tags":1692},[],[923],[],{"code":24,"score":69,"supported":582,"title":1694,"abstract":1695,"tags":1696},[],[],[591,1678],{"code":16,"score":455,"supported":582,"title":1698,"abstract":1699,"tags":1700},[],[],[590],{"code":48,"score":455,"supported":582,"title":1702,"abstract":1703,"tags":1704},[],[],[1528],[1706,1707],"埃塞俄比亚 马铃薯 晚疫病 检测","轻量CNN 马铃薯 叶片病害","埃塞俄比亚马铃薯晚疫病检测-5064",{"doi":11,"openalex_id":11,"authors":1710,"venue":11,"cited_by_count":424,"oa_url":11,"card":1711,"direction":1028,"ingested_from":804},[],{"tldr":1712,"method":1713,"finding":1714,"direction":1028,"opportunity":1715},"提出轻量CNN实现马铃薯早疫病与晚疫病三分类，并在埃塞俄比亚田间验证。","轻量卷积神经网络，PlantVillage 2215张训练，Banja镇247张","测试集准确率98.66%，独立田间测试降至90.28%，模型仅1.70MB。","可研究跨地区\u002F多作物泛化、田间复杂背景鲁棒性及边缘设备实时部署优化。","2026-10-16T00:14:01.369956Z",{"id":1718,"title":1719,"url":1720,"summary":1721,"summary_zh":1722,"content":11,"source_name":1723,"source_url":1720,"published_at":1724,"category":581,"cover_url":11,"hotness":385,"is_selected":582,"score":1725,"score_detail":1726,"sources":1728,"tags":1730,"primary_topic":1733,"secondary_topics":1734,"classification":1736,"search_phrases":1755,"slug":1758,"view_count":455,"doi":1759,"paper":1760,"created_at":1785},4993,"Effects of within-season temporal aggregation of weather variables on performance and interpretability in rice yield machine learning models","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-75646-2","While crop responses are inherently stage-dependent, machine learning yield models often rely on weather variables aggregated over the entire growing season. This study evaluates how within-season temporal aggregation of weather variables affects both predictive performance and interpretability in machine learning-based rice yield modeling. Using CYGMA (Crop Yield Growth Model with Assumptions on Climate and Socioeconomics)-simulated potential yield as a synthetic response variable, we minimized confounding effects from observational noise and management variability, enabling a clearer evaluation of the role of within-season weather aggregation. Daily weather variables were averaged over the rice growing period after dividing each season into multiple equal-length segments, and several machine learning algorithms were trained and evaluated using temporally independent validation. Increasing within-season temporal resolution improved model performance up to a moderate level, after which gains diminished. Interpretation using SHAP (SHapley Additive exPlanations) and response diagnostics consistently indicated that yield sensitivity concentrates in specific windows of the season, with temperature-related effects emerging most strongly early in the season and humidity-related effects later. Finer temporal aggregation sharpened the identification of window-specific nonlinear and threshold-like responses that were obscured under coarse seasonal averages. Overall, moderate within-season aggregation provides a practical balance between predictive skill and mechanistic interpretability, supporting more reliable identification of phenology-relevant meteorological risks for climate-impact assessment and decision support.","尽管作物响应本质上具有阶段依赖性，但机器学习产量模型往往依赖于整个生长季聚合的天气变量。本研究评估了天气变量的季节内时间聚合如何影响基于机器学习的水稻产量建模的预测性能与可解释性。以CYGMA（Crop Yield Growth Model with Assumptions on Climate and Socioeconomics，气候与社会经济假设下的作物产量生长模型）模拟的潜在产量作为合成响应变量，我们最大限度地减少了观测噪声和管理变异性带来的混杂效应，从而能够更清晰地评估季节内天气聚合的作用。将每个生长季划分为多个等长时段后，对水稻生长期内的逐日天气变量进行平均，并采用时间独立验证对多种机器学习算法进行训练和评估。提高季节内时间分辨率可提升模型性能，直至达到中等水平，此后增益递减。利用SHAP（SHapley Additive exPlanations，沙普利加性解释）和响应诊断进行的解释一致表明，产量敏感性集中在季节的特定窗口，温度相关效应在季节早期最为显著，湿度相关效应则在后期出现。更精细的时间聚合增强了对窗口特异性非线性和阈值型响应的识别，而这些响应在粗粒度季节平均下被掩盖。总体而言，中等程度的季节内聚合在预测能力与机制可解释性之间提供了实用平衡，有助于更可靠地识别与物候相关的气象风险，以支持气候影响评估和决策支持。","Scientific Reports","2026-10-13T00:00:00Z",72,{"impact":416,"substance":508,"depth":361,"authority":345,"freshness":173,"relevant":455,"comment":1727},"该研究通过CYGMA模拟数据系统评估了生长季内气象变量时间聚合对水稻产量机器学习模型性能与可解释性的影响，方法新颖、结论可靠，对农业气象风险识别与决策支持有参考价值，但属学术论文，公共影响范围有限。",[1729],{"name":1723,"url":1720},[590,591,1731,1480,1732],"可解释AI","水稻产量预测",{"code":208,"slug":209,"name":210,"path":211,"parent_code":200},[1735],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":600,"locked":582,"version":4,"method":601,"evidence":1737},{"candidates":1738,"agricultural_evidence":605},[1739,1743,1747,1751],{"code":24,"score":979,"supported":605,"title":1740,"abstract":1741,"tags":1742},[630],[629,630,632],[591,1731],{"code":208,"score":345,"supported":605,"title":1744,"abstract":1745,"tags":1746},[209],[210,209],[],{"code":184,"score":455,"supported":582,"title":1748,"abstract":1749,"tags":1750},[],[],[1480],{"code":16,"score":455,"supported":582,"title":1752,"abstract":1753,"tags":1754},[],[],[590],[1756,1757],"水稻产量 机器学习 气象","SHAP 水稻 产量预测","水稻产量机器学习气象-4993","10.1038\u002Fs41598-026-75646-2",{"doi":1759,"openalex_id":1761,"authors":1762,"venue":1723,"cited_by_count":424,"oa_url":1779,"card":1780,"direction":750,"ingested_from":670},"W7221657053",[1763,1765,1767,1769,1771,1773,1776],{"name":1764,"orcid":11},"Noh-Hyun Lee",{"name":1766,"orcid":11},"Wonjae Jeong",{"name":1768,"orcid":11},"Hyeon-Ji Yang",{"name":1770,"orcid":11},"Hana Cho",{"name":1772,"orcid":11},"Hyeon-Yeong Ju",{"name":1774,"orcid":1775},"Toshichika Iizumi","https:\u002F\u002Forcid.org\u002F0000-0002-0611-4637",{"name":1777,"orcid":1778},"Ki‐Tae Kim","https:\u002F\u002Forcid.org\u002F0000-0002-8249-5283","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75646-2_reference.pdf",{"tldr":1781,"method":1782,"finding":1783,"direction":1028,"opportunity":1784},"评估生长季内天气变量时间聚合粒度对水稻产量机器学习模型预测与可解释性的影响。","用CYGMA模拟潜在产量作响应变量，按等长时段聚合逐日天气，训练多种机器学习模型","适度提高季内时间分辨率可提升性能，产量敏感性集中在特定窗口，温度效应偏早、湿度效应偏晚。","可探索不同作物与气候区下最优聚合粒度，并将窗口特异性气象风险嵌入气候影响评估与决策支持系统。","2026-10-15T23:30:10.483881Z",{"id":1787,"title":1788,"url":1789,"summary":1790,"summary_zh":11,"content":11,"source_name":1791,"source_url":11,"published_at":11,"category":581,"cover_url":11,"hotness":385,"is_selected":582,"score":1671,"score_detail":1792,"sources":1794,"tags":1796,"primary_topic":1799,"secondary_topics":1800,"classification":1802,"search_phrases":1823,"slug":1826,"view_count":424,"doi":11,"paper":1827,"created_at":1834},4966,"基于改进YOLO 11n模型的棉花田间复杂环境障碍物检测方法","http:\u002F\u002Fwww.qikanzj.com\u002Fhek\u002Fnyjxxb\u002Fmulu\u002F455619.html","韩科立、王振坤等(中国农业机械化科学研究院集团有限公司;现代农装科技股份有限公司;农业装备技术全国重点实验室)针对棉花田间复杂环境障碍物被遮挡致准确检测难、边缘设备算力有限的问题,提出一种基于改进YOLO 11n模型的田间障碍物检测方法。采用StarNet作为主要特征提取网络并引入DBA模块增强多尺度特征交互能力;使用KAGNConv替换C3k2瓶颈结构增强灵活性与可解释性;集成分离与增强注意力模块SEAM至检测头增强遮挡场景能力。试验结果表明,改进模型YOLO 11n-SKS精确率、召回率、mAP50、mAP50-95分别提升2.3、2.1、1.3、1.4个百分点,达到91.7%、88.3%、91.9%、62.3%。","《农业机械学报》",{"impact":585,"substance":681,"depth":361,"authority":345,"freshness":69,"relevant":455,"comment":1793},"改进YOLO 11n实现棉花田间遮挡障碍物检测，方法新颖、指标提升明确，属农业人工智能细分领域实质进展，但发布日期未知、时效性偏弱。",[1795],{"name":1791,"url":1789},[590,591,781,1797,1798],"障碍物检测","采棉机",{"code":248,"slug":249,"name":250,"path":251,"parent_code":200},[1801],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"status":600,"locked":582,"version":4,"method":601,"evidence":1803},{"candidates":1804,"agricultural_evidence":605},[1805,1809,1813,1819],{"code":248,"score":416,"supported":605,"title":1806,"abstract":1807,"tags":1808},[781],[781],[781],{"code":24,"score":416,"supported":605,"title":1810,"abstract":1811,"tags":1812},[776],[776],[591],{"code":112,"score":719,"supported":605,"title":1814,"abstract":1815,"tags":1818},[],[1816,1817],"农业机械","机械化",[],{"code":16,"score":455,"supported":582,"title":1820,"abstract":1821,"tags":1822},[],[],[590],[1824,1825],"中国农业机械化科学研究院 采棉机 障碍物检测","YOLO 11n 棉花 田间障碍物","中国农业机械化科学研究院采棉机障碍物检测-4966",{"doi":11,"openalex_id":11,"authors":1828,"venue":11,"cited_by_count":424,"oa_url":11,"card":1829,"direction":1028,"ingested_from":804},[],{"tldr":1830,"method":1831,"finding":1832,"direction":1028,"opportunity":1833},"提出改进YOLO 11n模型，实现棉花田间复杂遮挡环境下障碍物高精度检测。","以StarNet为骨干，引入DBA、KAGNConv与SEAM注意力模块，改进Y","改进模型精确率91.7%、召回率88.3%、mAP50达91.9%，各项指标均提升。","可探索轻量化模型在边缘设备实时部署，并扩展至多作物多类障碍物检测。","2026-10-15T00:06:08.577388Z",{"technologies":1836,"keywords":1895},[1837,1839,1840,1842,1843,1844,1846,1848,1849,1851,1853,1854,1855,1856,1858,1859,1860,1862,1863,1865,1866,1868,1870,1872,1873,1875,1877,1879,1881,1883,1885,1887,1889,1891,1893],{"name":591,"count":1838},170,{"name":609,"count":1671},{"name":983,"count":1841},37,{"name":688,"count":516},{"name":592,"count":699},{"name":1845,"count":1136},"生物育种",{"name":1847,"count":681},"分子育种",{"name":629,"count":361},{"name":1850,"count":613},"基因编辑",{"name":1852,"count":683},"作物表型",{"name":628,"count":416},{"name":766,"count":524},{"name":1528,"count":1256},{"name":1857,"count":408},"农业机器人",{"name":1005,"count":400},{"name":765,"count":400},{"name":1861,"count":400},"计算机视觉",{"name":1043,"count":874},{"name":1864,"count":874},"大语言模型",{"name":971,"count":874},{"name":1867,"count":69},"GIS",{"name":1869,"count":69},"人工智能",{"name":1871,"count":69},"农业大模型",{"name":1308,"count":69},{"name":1874,"count":69},"无人机多光谱",{"name":1876,"count":69},"高光谱",{"name":1878,"count":455},"AI",{"name":1880,"count":455},"农业物联网",{"name":1882,"count":455},"区块链",{"name":1884,"count":455},"图像识别",{"name":1886,"count":455},"多模态",{"name":1888,"count":455},"多源遥感",{"name":1890,"count":455},"大模型",{"name":1892,"count":455},"联邦学习",{"name":1894,"count":455},"高光谱遥感",[1896,1898,1900,1902,1904,1905,1908,1910,1913,1916,1918,1920],{"name":1142,"count":1897},347,{"name":590,"count":1899},324,{"name":923,"count":1901},130,{"name":210,"count":1903},109,{"name":226,"count":899},{"name":1906,"count":1907},"单产提升",58,{"name":218,"count":1909},56,{"name":1911,"count":1912},"水稻育种",53,{"name":1914,"count":1915},"大豆",38,{"name":1917,"count":1915},"成果转化",{"name":1919,"count":447},"小麦育种",{"name":1921,"count":447},"杂交水稻"]