[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1609":3,"related-1609":92},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":30,"secondary_topics":36,"classification":43,"search_phrases":68,"slug":71,"view_count":21,"doi":72,"paper":73,"created_at":91},1609,"Make sorghum great again: navigating the politics of gene editing in the Make America Healthy Again movement","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1915746","Introduction Gene editing has emerged as a promising tool for developing climate-resilient crops, yet discussions of its adoption have focused primarily on technological performance and public acceptance, while paying less attention to the political realignments that increasingly shape agricultural innovation. The emergence of the Make America Healthy Again (MAHA) movement in the United States has disrupted traditional coalitions around agricultural biotechnology, creating new uncertainties for niche crops such as sorghum ( Sorghum bicolor ), whose future depends on both scientific innovation and consumer trust. This paper examines how shifting political alliances influence the prospects for gene-edited sorghum in the US. Methods We employ a mixed-methods case study combining a national survey of 402 sorghum producers with stakeholder mapping, participant observation, interviews, and analysis of news media, policy documents, and online discourse analysis to examine how political narratives shape perceptions of gene editing across the sorghum ecosystem. Results Although 63 percent of surveyed farmers reported being open to adopting gene-edited sorghum, their willingness was driven less by concerns about the technology itself than by uncertainty surrounding consumer acceptance,market access, trade policy, and the evolving political landscape. Our findings demonstrate that the future of gene-edited \u002F GE sorghum will depend not only on technological performance or public acceptance, but on whether trust, regulatory stability, and political alignment can be established among farmers, consumers, scientists, industry, and policymakers. Discussion By shifting attention from technology alone to the political economy of agricultural innovation, this study highlights the need for governance strategies that address stakeholder relationships alongside scientific advancement.","引言 基因编辑已成为开发气候适应性作物的一种前景广阔的工具，然而关于其应用的讨论主要聚焦于技术性能和公众接受度，而对日益塑造农业创新的政治重组关注不足。美国“让美国重回健康”（MAHA）运动的兴起，打破了围绕农业生物技术的传统联盟，为高粱（*Sorghum bicolor*）等小众作物带来了新的不确定性，其未来既取决于科学创新，也取决于消费者信任。本文考察了不断变化的政治联盟如何影响美国基因编辑高粱的前景。方法 我们采用混合方法的案例研究，将一项针对402名高粱种植者的全国性调查与利益相关者图谱绘制、参与式观察、访谈以及对新闻媒体、政策文件和在线话语的分析相结合，以考察政治叙事如何在高粱生态系统中塑造对基因编辑的认知。结果 尽管63%的受访农民表示愿意采用基因编辑高粱，但其意愿更多并非源于对技术本身的担忧，而是受制于围绕消费者接受度、市场准入、贸易政策以及不断演变的政治格局的不确定性。我们的研究结果表明，基因编辑\u002FGE高粱的未来不仅取决于技术性能或公众接受度，还取决于能否在农民、消费者、科学家、产业界和政策制定者之间建立信任、监管稳定性和政治一致性。讨论 通过将关注点从单纯的技术转向农业创新的政治经济学，本研究强调了治理策略在推动科学进步的同时，还需兼顾利益相关者关系的必要性。",null,"Frontiers in Sustainable Food Systems","2026-09-03T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,20,13,7,1,"研究揭示政治联盟对基因编辑作物商业化的影响，为农业创新治理提供新视角。",[24],{"name":10,"url":6},[26,27,28,29],"农业政策","基因编辑","高粱","政治经济",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"A5","tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains","A",[37],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},"F2","biotechnology","生物育种与基因技术","\u002Ftopic\u002Finputs-equipment\u002Fbiotechnology","F",{"status":44,"locked":14,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":51},[49,56,61],{"code":31,"score":50,"supported":51,"title":52,"abstract":54,"tags":55},15,true,[53],"sorghum",[28,53],[28],{"code":38,"score":50,"supported":51,"title":57,"abstract":59,"tags":60},[58],"gene editing",[27,58],[27],{"code":62,"score":63,"supported":51,"title":64,"abstract":65,"tags":67},"H7",3,[],[66],"气候适应",[],[69,70],"农业政策 基因编辑 政治经济 高粱","农业政策 基因编辑","农业政策基因编辑政治经济高粱-1609","10.3389\u002Ffsufs.2026.1915746",{"doi":72,"openalex_id":74,"authors":75,"venue":10,"cited_by_count":82,"oa_url":83,"card":84,"direction":88,"ingested_from":90},"W7207688230",[76,79],{"name":77,"orcid":78},"Gabriella Subia","https:\u002F\u002Forcid.org\u002F0000-0002-4265-7209",{"name":80,"orcid":81},"Lauren Gifford","https:\u002F\u002Forcid.org\u002F0000-0002-8130-4684",0,"https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1915746\u002Fpdf",{"tldr":85,"method":86,"finding":87,"direction":88,"opportunity":89},"研究美国MAHA运动政治联盟如何影响基因编辑高粱的采用前景。","混合方法案例研究：全国402名农户调查、利益相关者图谱、访谈、媒体与政策分析。","农户对基因编辑高粱接受度受消费者信任、市场准入及政治不确定性影响，而非技术本身。","农业人工智能与决策模型","可延伸研究政治经济因素如何影响其他作物基因编辑技术的采纳，探索跨利益相关者治理策略。","openalex","2026-09-04T23:30:08.026707Z",{"total":93,"page":21,"page_size":93,"items":94},6,[95,158,210,323,387,497],{"id":96,"title":97,"url":98,"summary":99,"summary_zh":9,"content":100,"source_name":101,"source_url":9,"published_at":102,"category":103,"cover_url":9,"hotness":13,"is_selected":14,"score":104,"score_detail":105,"sources":110,"tags":112,"primary_topic":117,"secondary_topics":123,"classification":125,"search_phrases":153,"slug":156,"view_count":82,"doi":9,"paper":9,"created_at":157},5428,"江苏省农科院畜牧所与乾宝牧业签约 联合攻坚 MSTN 基因编辑湖羊新种质","https:\u002F\u002Fnewias.jaas.ac.cn\u002Fxww\u002Fxwzx\u002Fart\u002F2026\u002Fart_2585242c87ed4a26b2c48902878d3086.html","10 月 16 日，江苏省农业科学院畜牧研究所与江苏乾宝牧业举行「MSTN 基因编辑湖羊新种质创制」项目合作签约仪式。院党委委员、副院长王冉与乾宝牧业董事长王跃程出席。乾宝牧业拥有国内单体规模最大的湖羊核心种群，其培育的乾宝湖羊作为国内唯一肉羊优秀母本入驻海南崖州湾国家实验室。双方将系统开展新种质性能评价，推动实验室成果走向养殖一线，联手打造肉羊生物育种创新样板。","10月16日，我院畜牧研究所与江苏乾宝牧业有限公司举行“MSTN基因编辑湖羊新种质创制”项目合作签约仪式。院党委委员、副院长王冉，乾宝牧业董事长王跃程、总裁蒋柏林出席活动，畜牧所相关负责人，以及双方科研骨干和相关负责人参加签约活动。\n\n王冉表示，种业是农业的“芯片”，畜禽种质创新是种业振兴的重要抓手，基因编辑等生物育种技术是畜禽育种变革的重要方向。期待双方以本次签约为契机，聚焦产业与企业实际需求，聚力破解技术转化落地难题，系统开展新种质性能评价，推动实验室科研成果走向养殖一线，以院企协同创新助力江苏肉羊种业高质量发展。\n\n王跃程介绍，湖羊是我国肉羊养殖的当家母本，也是江苏享誉全国的国宝级地方畜禽种质资源，更是乾宝牧业的核心资产。乾宝牧业深耕湖羊育种十余年，先后获评国家肉羊核心育种场、国家种业阵型企业、农业产业化国家重点龙头企业等，已成为国内湖羊产业领军企业，拥有国内单体规模最大的湖羊核心种群，种质资源领先行业一至两个代次，其培育的乾宝湖羊作为国内唯一肉羊优秀母本入驻海南崖州湾国家实验室。本次联合开展MSTN基因编辑湖羊创制，有望突破传统育种技术瓶颈，加速培育具有自主知识产权的突破性湖羊新种质。\n\n签约现场，相关科研人员汇报了“MSTN基因编辑湖羊新种质创制”项目实施方案，详细阐述种质群体创建流程与技术路线，厘清了双方任务分工与关键时间节点。双方还围绕生物安全管理规范、主体责任落实、后续项目申报等事项开展深入交流，进一步统一实施思路，细化落地推进举措。\n\n本次签约，标志着院企双方在动物生物育种领域的产学研协同攻关正式启动。下一步，双方将严格按照合作方案稳步推进各项工作，充分发挥畜牧所技术研发优势与乾宝牧业产业落地优势，加快基因编辑湖羊新种质创制步伐，打造肉羊生物育种创新样板，共同携手为我国湖羊种业升级、肉羊产业高质量发展提供科技支撑。","江苏省农业科学院 2026-10-20","2026-10-20T00:00:00Z","报道",68,{"impact":17,"substance":106,"depth":107,"authority":19,"freshness":108,"relevant":21,"comment":109},16,12,9,"省级科研机构与龙头企业围绕基因编辑湖羊新种质开展产学研合作，属细分领域实质进展，具备行业参考价值。",[111],{"name":101,"url":98},[113,114,27,115,116],"种业振兴","生物育种","院企合作","湖羊",{"code":118,"slug":119,"name":120,"path":121,"parent_code":122},"C2","ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants","C",[124],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":44,"locked":14,"version":45,"method":46,"evidence":126},{"candidates":127,"agricultural_evidence":51},[128,132,137,143,148],{"code":38,"score":106,"supported":51,"title":129,"abstract":130,"tags":131},[27],[114,27],[27,114],{"code":118,"score":50,"supported":51,"title":133,"abstract":134,"tags":136},[116],[116,135],"肉羊",[116],{"code":122,"score":138,"supported":51,"title":139,"abstract":141,"tags":142},11,[140],"畜牧",[140],[],{"code":144,"score":21,"supported":14,"title":145,"abstract":146,"tags":147},"F1",[],[],[113],{"code":149,"score":21,"supported":14,"title":150,"abstract":151,"tags":152},"G1",[],[],[115],[154,155],"江苏省农科院 乾宝牧业 湖羊","MSTN 基因编辑 湖羊","江苏省农科院乾宝牧业湖羊-5428","2026-10-21T00:10:00.642077Z",{"id":159,"title":160,"url":161,"summary":162,"summary_zh":9,"content":163,"source_name":164,"source_url":9,"published_at":9,"category":103,"cover_url":9,"hotness":13,"is_selected":14,"score":165,"score_detail":166,"sources":169,"tags":171,"primary_topic":176,"secondary_topics":177,"classification":183,"search_phrases":205,"slug":208,"view_count":82,"doi":9,"paper":9,"created_at":209},5220,"省农科院水稻高粱所与平昌县十年协同:酿酒专用粮全产业链加速成型","https:\u002F\u002Fwww.sctv.com\u002Fnews\u002F2110606011947991042","四川省农业科学院水稻高粱所深度融入平昌县白酒产业\"融圈建链\"战略,以科技成果转化示范基地为平台,携手小角楼酒业探索\"专家驻点+品种示范+技术集成+订单回收\"模式,推动科研成果从实验室走向田间,为秦巴山区乡村振兴提供科技动能。","秦巴山区，秋风送爽，平昌大地高粱红遍。从土兴镇的示范田到白衣镇的产业园区，从种植大户的丰收笑脸到小角楼酒业的原粮基地，一条以科技为引领、以院地企协同为支撑的酿酒专用粮全产业链正加速成型。近年来，四川省农业科学院水稻高粱所深度融入平昌县白酒产业“融圈建链”战略，以科技成果转化示范基地为平台，以驻村工作队为纽带，以良种良法为核心，携手小角楼酒业十年深耕，探索出“专家驻点+品种示范+技术集成+订单回收”的协同发展模式，将科技优势转化为产业优势，为秦巴山区乡村振兴和川东北白酒产业高质量发展注入强劲动能。","四川网络广播电视台(2026-10)",57,{"impact":106,"substance":167,"depth":107,"authority":108,"freshness":93,"relevant":21,"comment":168},14,"省级科研机构与县域、企业十年协同的产业链报道，有模式提炼但属地方宣传性通稿，信息增量有限。",[170],{"name":164,"url":161},[172,28,173,174,175],"乡村振兴","科技成果转化","酿酒专用粮","院地企协同",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[178],{"code":179,"slug":180,"name":181,"path":182,"parent_code":35},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice",{"status":44,"locked":14,"version":45,"method":46,"evidence":184},{"candidates":185,"agricultural_evidence":51},[186,190,194,200],{"code":31,"score":107,"supported":51,"title":187,"abstract":188,"tags":189},[28],[28],[28],{"code":179,"score":138,"supported":51,"title":191,"abstract":192,"tags":193},[181],[181],[],{"code":195,"score":196,"supported":51,"title":197,"abstract":198,"tags":199},"G",4,[],[172],[172],{"code":149,"score":196,"supported":51,"title":201,"abstract":202,"tags":204},[],[203],"成果转化",[173],[206,207],"四川省农科院 水稻高粱所 平昌县","平昌 酿酒专用粮 小角楼","四川省农科院水稻高粱所平昌县-5220","2026-10-18T00:05:23.650956Z",{"id":211,"title":212,"url":213,"summary":214,"summary_zh":215,"content":9,"source_name":216,"source_url":213,"published_at":217,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":218,"score_detail":219,"sources":222,"tags":224,"primary_topic":229,"secondary_topics":230,"classification":242,"search_phrases":294,"slug":297,"view_count":196,"doi":298,"paper":299,"created_at":322},3918,"UAV-based RGB imagery and supervised–unsupervised machine learning for estimating sorghum biomass and grain yield under water and nitrogen stress","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffenvs.2026.1928610","Precision agriculture emerged as a key strategy for rational management of water and nutrients amid growing food demand driven by global population expansion. Therefore, timely monitoring of field crops is crucial in maintaining adequate nutrient and water levels in the crop fields. Vegetation indices are increasingly being studied for their use in plant nutrient management and in guiding fertilizer recommendations. However, these indices were not adequately evaluated for nutrient management of crops grown with different water and nitrogen application levels. This study investigated the potential application of RGB imagery from low-cost Unmanned Aerial Vehicles (UAVs), combined with machine learning techniques, for quantifying water and nutrient stress in sorghum [ Sorghum bicolor (L.) Moench] crop performance. The field experimental setup at Prairie View A&M University, located in Southeast Texas, applied four irrigation levels (0, 75, 100, and 125% of irrigation; the 100% irrigation level is a fully irrigated treatment with no water stress) and four nitrogen application rates (0, 90, 180, and 360 kg N ha −1 ). The RGB images were captured before harvesting the grain sorghum. Image segmentation for filtering sorghum crops from soil and weeds was performed using K-means clustering and a Convolutional Neural Network (CNN). The vegetation indices, including the Green-Red Vegetation Index (GRVI), Green Leaf Index (GLI), and Excess Green Index (ExG), derived from the segmented images were used to predict sorghum grain and biomass yields using multiple linear regression, Partial least squares regression, Ridge regression, and XGBoost. The results indicated that no single model consistently outperformed the others across both segmentation approaches and response variables, suggesting that the predictive information in the RGB indices is meaningful with selected ML approaches. For example, biomass prediction using ridge regression yielded a Pearson correlation coefficient (r) of 0.65 and an RMSE of 0.72 for K-means clustering-derived indices. The models exhibited moderate, stage-specific predictive performance, with no single regression method consistently outperforming the others across both segmentation approaches and response variables. Because imagery was acquired only once during the late reproductive period, the RGB indices primarily reflected canopy condition, maturity, and senescence at that time. The findings demonstrate the feasibility of a low-cost, single-date UAV RGB workflow for late-season sorghum assessment, while emphasizing the need for multi-temporal imagery and independent validation before application to season-long stress diagnosis or operational yield forecasting.","精准农业已成为在水资源与养分合理管理中的关键策略，以应对全球人口增长所驱动的日益增长的粮食需求。因此，及时监测田间作物对于维持作物田块中适宜的养分和水分水平至关重要。植被指数在植物养分管理和指导施肥推荐中的应用正日益受到研究。然而，这些指数尚未在不同水分和氮素施用水平下对作物养分管理进行充分评估。本研究探讨了来自低成本无人机（UAV）的RGB影像结合机器学习技术在量化高粱[Sorghum bicolor (L.) Moench]作物表现中水分和养分胁迫方面的潜在应用。位于德克萨斯州东南部的Prairie View A&M大学的田间试验设置了四个灌溉水平（灌溉量的0%、75%、100%和125%；100%灌溉水平为充分灌溉处理，无水分胁迫）和四个氮素施用量（0、90、180和360 kg N ha⁻¹）。RGB图像在收获籽粒高粱前采集。使用K-means聚类和卷积神经网络（CNN）进行图像分割，以将高粱作物与土壤和杂草分离。从分割图像中提取的植被指数，包括绿红植被指数（GRVI）、绿叶指数（GLI）和过量绿指数（ExG），被用于通过多元线性回归、偏最小二乘回归、岭回归和XGBoost预测高粱籽粒和生物量产量。结果表明，在两种分割方法和响应变量下，没有单一模型始终优于其他模型，这表明RGB指数中的预测信息在所选机器学习方法下具有意义。例如，使用岭回归进行生物量预测时，基于K-means聚类所得指数的Pearson相关系数（r）为0.65，RMSE为0.72。这些模型表现出中等、特定阶段的预测性能，在两种分割方法和响应变量下，没有单一回归方法始终优于其他方法。由于影像仅在生殖后期采集一次，RGB指数主要反映当时的冠层状况、成熟度和衰老程度。研究结果证明了低成本、","Frontiers in Environmental Science","2026-09-30T00:00:00Z",69,{"impact":107,"substance":18,"depth":106,"authority":19,"freshness":220,"relevant":21,"comment":221},8,"低成本无人机RGB影像结合机器学习估算高粱生物量与产量的方法学研究，结论审慎，对精准农业养分水分管理有参考价值，但属单点单期试验，影响范围有限。",[223],{"name":216,"url":213},[225,226,227,228,28],"智慧农业","农业人工智能","精准农业","遥感",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[231,237],{"code":232,"slug":233,"name":234,"path":235,"parent_code":236},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":238,"slug":239,"name":240,"path":241,"parent_code":42},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers",{"status":44,"locked":14,"version":45,"method":46,"evidence":243},{"candidates":244,"agricultural_evidence":51},[245,249,255,262,270,276,283,289],{"code":31,"score":50,"supported":51,"title":246,"abstract":247,"tags":248},[53],[28,53],[28],{"code":232,"score":50,"supported":51,"title":250,"abstract":252,"tags":254},[251],"machine learning",[253,251],"机器学习",[226],{"code":238,"score":108,"supported":51,"title":256,"abstract":257,"tags":261},[],[258,259,260],"施肥","养分管理","fertilizer",[],{"code":263,"score":108,"supported":51,"title":264,"abstract":265,"tags":269},"H3",[],[266,267,268],"灌溉","水资源","irrigation",[],{"code":271,"score":220,"supported":51,"title":272,"abstract":273,"tags":275},"I1",[],[227,274],"precision agriculture",[225,227],{"code":277,"score":93,"supported":51,"title":278,"abstract":279,"tags":282},"H2",[],[280,281],"土壤","soil",[],{"code":284,"score":63,"supported":51,"title":285,"abstract":286,"tags":288},"A7",[],[287],"间作",[],{"code":290,"score":21,"supported":14,"title":291,"abstract":292,"tags":293},"I3",[],[],[228],[295,296],"Prairie View A&M University 高粱 无人机","高粱 生物量 产量 无人机 RGB","PrairieViewAMUniversity高粱无人机-3918","10.3389\u002Ffenvs.2026.1928610",{"doi":298,"openalex_id":300,"authors":301,"venue":216,"cited_by_count":82,"oa_url":213,"card":316,"direction":88,"ingested_from":90},"W7214994063",[302,304,307,309,312,314],{"name":303,"orcid":9},"Anoop Valiya Veettil",{"name":305,"orcid":306},"Ripendra Awal","https:\u002F\u002Forcid.org\u002F0000-0002-2453-2592",{"name":308,"orcid":9},"Ali Fares",{"name":310,"orcid":311},"Binita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-7093-4915",{"name":313,"orcid":9},"Almoutaz Elhassan",{"name":315,"orcid":9},"Atikur Rahman",{"tldr":317,"method":318,"finding":319,"direction":320,"opportunity":321},"用无人机RGB影像与机器学习估算水分氮胁迫下高粱生物量和产量。","低成本无人机RGB影像，K-means与CNN分割，多种回归模型预测。","单一模型未持续最优，RGB指数在特定时期具中等预测能力。","农业遥感与作物表型","可研究多时相RGB影像与深度学习融合，提升胁迫下作物产量预测精度。","2026-10-01T23:30:54.379150Z",{"id":324,"title":325,"url":326,"summary":327,"summary_zh":328,"content":9,"source_name":329,"source_url":326,"published_at":217,"category":12,"cover_url":9,"hotness":330,"is_selected":14,"score":331,"score_detail":332,"sources":334,"tags":342,"primary_topic":346,"secondary_topics":347,"classification":349,"search_phrases":371,"slug":374,"view_count":63,"doi":375,"paper":376,"created_at":386},3912,"An Integrated Computer Vision and Machine Learning Framework for Sorghum Disease Severity Assessment and Yield Prediction","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23054047","An Integrated Computer Vision and Machine Learning Framework for Sorghum Disease Severity Assessment and Yield Prediction presents a technology-driven approach for automated crop health monitoring and yield estimation in sorghum cultivation. The study integrates computer vision techniques and machine learning algorithms to identify and quantify disease severity from crop images and to predict potential yield based on relevant visual and agronomic characteristics. The framework aims to reduce the limitations of conventional manual disease assessment, which can be time-consuming and subject to human variability. Image processing and feature extraction techniques are employed to capture disease-related characteristics, while machine learning models support classification, severity estimation, and yield prediction. By combining disease assessment with yield forecasting, the proposed framework provides a comprehensive approach to precision agriculture and data-driven crop management. The work demonstrates the potential of artificial intelligence and computer vision to support timely decision-making, improve crop monitoring, and contribute to sustainable and efficient sorghum production.","一个集成的计算机视觉与机器学习框架用于高粱病害严重度评估与产量预测，提出了一种技术驱动的方法，用于高粱种植中的自动化作物健康监测与产量估算。该研究整合了计算机视觉技术和机器学习算法，从作物图像中识别和量化病害严重度，并基于相关视觉和农艺特征预测潜在产量。该框架旨在减少传统人工病害评估的局限性，传统方法可能耗时且受人为变异影响。研究采用图像处理和特征提取技术捕捉病害相关特征，同时机器学习模型支持分类、严重度估计和产量预测。通过将病害评估与产量预测相结合，所提出的框架为精准农业和数据驱动的作物管理提供了一种综合方法。该工作展示了人工智能和计算机视觉在支持及时决策、改善作物监测以及促进可持续和高效高粱生产方面的潜力。","International Journal of Emerging Technologies and Innovative Research",55,56,{"impact":107,"substance":106,"depth":167,"authority":93,"freshness":220,"relevant":21,"comment":333},"论文提出计算机视觉与机器学习结合的高粱病害严重度评估与产量预测框架，方法有一定新意但期刊权威性一般，属细分领域技术进展。",[335,336,338,340],{"name":329,"url":326},{"name":329,"url":337},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23054048",{"name":329,"url":339},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23053752",{"name":329,"url":341},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23053753",[225,226,343,344,28,345],"计算机视觉","产量预测","病害识别",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[348],{"code":232,"slug":233,"name":234,"path":235,"parent_code":236},{"status":44,"locked":14,"version":45,"method":46,"evidence":350},{"candidates":351,"agricultural_evidence":51},[352,359,363,367],{"code":232,"score":353,"supported":51,"title":354,"abstract":356,"tags":358},28,[251,355],"computer vision",[253,343,251,357,355],"artificial intelligence",[226,345,343],{"code":31,"score":50,"supported":51,"title":360,"abstract":361,"tags":362},[53],[28,53],[28],{"code":271,"score":20,"supported":51,"title":364,"abstract":365,"tags":366},[],[227,274],[225],{"code":290,"score":21,"supported":14,"title":368,"abstract":369,"tags":370},[],[],[344],[372,373],"高粱 病害识别 产量预测","计算机视觉 作物病害评估","高粱病害识别产量预测-3912","10.5281\u002Fzenodo.23054047",{"doi":375,"openalex_id":377,"authors":378,"venue":329,"cited_by_count":82,"oa_url":326,"card":381,"direction":88,"ingested_from":90},"W7214900332",[379],{"name":380,"orcid":9},"Anantjit Publication",{"tldr":382,"method":383,"finding":384,"direction":88,"opportunity":385},"融合计算机视觉与机器学习，实现高粱病害严重度评估与产量预测。","图像处理与特征提取结合机器学习分类回归模型。","该框架可自动量化病害严重度并预测产量，减少人工评估偏差。","可探索多作物迁移学习与田间实时轻量化部署，提升泛化性。","2026-10-01T23:30:48.742698Z",{"id":388,"title":389,"url":390,"summary":391,"summary_zh":392,"content":9,"source_name":10,"source_url":390,"published_at":393,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":394,"score_detail":395,"sources":398,"tags":400,"primary_topic":405,"secondary_topics":409,"classification":416,"search_phrases":465,"slug":468,"view_count":21,"doi":469,"paper":470,"created_at":496},3763,"Improving drought tolerance and water use efficiency in dryland wheat and sorghum in the Texas High Plains","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1952813","Texas High Plains (THP) is one of the most productive agricultural regions in the USA, and the economic value of annual total crop production is nearly $2 billion. The area also serves as a major livestock center for fed cattle and, increasingly, other feeding industries such as dairy. The THP has a semi-arid climate with annual precipitation averaging about 480 mm and the seasonal precipitation only can meet part of the evapotranspiration (ET) requirement for high crop yields. For field crop production, drought is the most common factor limiting yield and water use efficiency (WUE). In this review, we focus on research progress and future challenges of dryland cropping systems in the area. Due to the low growing season precipitation and uneven monthly distributions, dryland crops are largely produced under crop-fallow conditions and wheat-fallow and wheat-sorghum fallow are common. Over the decades, both wheat and grain sorghum yields have increased from the level of 1,000–2,000 kg ha −1 in 1950s to >4,000 kg ha −1 in current time. Although improved management practices (e.g., conservation tillage and residue management) have contributed to increased yield and WUE, genetic improvement of drought tolerance also played an important role, particularly the winter wheat. Multiple-year field studies indicated that selecting cultivars with higher biomass and greater early vigor are beneficial to wheat production under drought. We further found that newer drought tolerant (DT) cultivars used soil water more efficiently than a relative older cultivar. Also, new DT cultivars can extract more water from deeper soil profile and have cooler canopy temperature under drought. In recent years, we are also evaluating remote-sensing tools at both ground and aerial levels (airplane and drone) to characterize wheat genotypes for drought tolerance. We demonstrated that infrared thermometer, thermal imaging, and spectral reflectance data can be used to characterize genotypic variation during growing season.","德克萨斯高平原（THP）是美国最具生产力的农业区之一，年度作物总产量的经济价值接近20亿美元。该地区还是主要的肉牛育肥中心，并且日益成为乳业等其他饲养产业的聚集地。THP属于半干旱气候，年均降水量约480毫米，季节性降水仅能满足高产作物蒸散（ET）需求的一部分。对于大田作物生产而言，干旱是限制作物产量和水分利用效率（WUE）的最常见因素。本文综述了该地区旱作种植制度的研究进展与未来挑战。由于生长季降水少且月分布不均，旱地作物主要在作物-休闲制度下生产，小麦-休闲和小麦-高粱休闲是常见模式。数十年来，小麦和高粱产量均从20世纪50年代的1,000–2,000 kg ha⁻¹提高至当前的4,000 kg ha⁻¹以上。尽管改进的管理措施（如保护性耕作和残茬管理）促进了产量和WUE的提升，但耐旱性的遗传改良也发挥了重要作用，尤其是冬小麦。多年田间研究表明，选择生物量较高和早期长势较强的品种有利于干旱条件下的小麦生产。我们进一步发现，较新的耐旱（DT）品种比相对较老的品种更有效地利用土壤水分。此外，新的DT品种能够从更深土层中提取更多水分，并在干旱条件下具有更低的冠层温度。近年来，我们还在评估地面和航空（飞机和无人机）尺度的遥感工具，以表征小麦基因型的耐旱性。我们证明，红外温度计、热成像和光谱反射数据可用于表征生长季内的基因型变异。","2026-09-28T00:00:00Z",80,{"impact":17,"substance":396,"depth":17,"authority":167,"freshness":220,"relevant":21,"comment":397},22,"综述系统梳理了德州高原旱地小麦高粱抗旱与水分利用效率的研究进展，含多年田间数据与遥感表型新方法，对旱作农业与智慧育种有参考价值。",[399],{"name":10,"url":390},[401,402,28,403,404],"小麦","旱作农业","节水灌溉","遥感育种",{"code":406,"slug":407,"name":401,"path":408,"parent_code":35},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat",[410,411],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":62,"slug":412,"name":413,"path":414,"parent_code":415},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":44,"locked":14,"version":45,"method":46,"evidence":417},{"candidates":418,"agricultural_evidence":51},[419,424,428,434,440,444,450,455,461],{"code":406,"score":17,"supported":51,"title":420,"abstract":421,"tags":423},[407],[401,422,407],"winter wheat",[401],{"code":31,"score":50,"supported":51,"title":425,"abstract":426,"tags":427},[53],[28,53],[28],{"code":62,"score":167,"supported":51,"title":429,"abstract":431,"tags":433},[430],"drought",[432,430],"干旱",[],{"code":118,"score":93,"supported":51,"title":435,"abstract":436,"tags":439},[],[437,438],"肉牛","cattle",[],{"code":277,"score":93,"supported":51,"title":441,"abstract":442,"tags":443},[],[280,281],[],{"code":263,"score":445,"supported":51,"title":446,"abstract":447,"tags":449},5,[],[448],"evapotranspiration",[402,403],{"code":122,"score":63,"supported":51,"title":451,"abstract":452,"tags":454},[],[453],"livestock",[],{"code":456,"score":63,"supported":51,"title":457,"abstract":458,"tags":460},"H8",[],[459],"保护性耕作",[],{"code":290,"score":63,"supported":51,"title":462,"abstract":463,"tags":464},[],[228],[],[466,467],"Texas High Plains 小麦 高粱 抗旱","旱地小麦 水分利用效率 遥感","TexasHighPlains小麦高粱抗旱-3763","10.3389\u002Ffsufs.2026.1952813",{"doi":469,"openalex_id":471,"authors":472,"venue":10,"cited_by_count":82,"oa_url":490,"card":491,"direction":320,"ingested_from":90},"W7214633314",[473,475,478,481,484,486,488],{"name":474,"orcid":9},"薛青武",{"name":476,"orcid":477},"Sushil Thapa","https:\u002F\u002Forcid.org\u002F0000-0002-5589-8124",{"name":479,"orcid":480},"Shuyu Liu","https:\u002F\u002Forcid.org\u002F0000-0003-4748-2900",{"name":482,"orcid":483},"Jackie Rudd","https:\u002F\u002Forcid.org\u002F0000-0001-9186-3386",{"name":485,"orcid":9},"Jourdan Bell",{"name":487,"orcid":9},"Thomas Marek",{"name":489,"orcid":9},"Amir Ibrahim","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1952813\u002Fpdf",{"tldr":492,"method":493,"finding":494,"direction":320,"opportunity":495},"综述美国德州高平原旱地小麦和高粱提高耐旱性与水分利用效率的研究进展与挑战。","多年田间试验、保护性耕作、耐旱品种选育及地面与航空遥感表型工具。","耐旱新品种能更高效利用土壤水、深层取水且冠层温度更低，遥感可表征基因型变异。","可结合无人机热成像与光谱反射构建旱地作物耐旱表型高通量筛选模型，加速品种选育。","2026-09-29T23:30:07.283028Z",{"id":498,"title":499,"url":500,"summary":501,"summary_zh":9,"content":502,"source_name":503,"source_url":9,"published_at":504,"category":103,"cover_url":9,"hotness":13,"is_selected":14,"score":505,"score_detail":506,"sources":508,"tags":510,"primary_topic":513,"secondary_topics":518,"classification":520,"search_phrases":555,"slug":558,"view_count":445,"doi":9,"paper":9,"created_at":559},3646,"三项鄂研农业科技成果入选国家'火花技术'——新型高效微生物杀螨剂、油菜抗裂角产品、SrCas12a-2水产基因编辑系统","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7686835905016971830","日前中国农学会公布2026年度37项农业'火花技术'培育成果库名单，湖北报送的'新型高效微生物杀虫剂''油菜抗裂角落粒减损增产产品以及配套技术''基于SrCas12a-2新型工具酶的水产基因编辑系统'三项技术入选，标志着湖北在农业新质生产力培育上获得新突破。'火花技术'是中国农学会受农业农村部委托、面向农业前沿交叉领域开展的成果征集工作，从入选条件看，这些成果聚焦解决农业生产技术难题或产业发展'卡脖子'问题，代表着未来农业发展方向。","## ​三项鄂研农业科技成果入选国家“火花技术”\n\n2026-09-18 19:29·[湖北日报](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAA-SydD1UcngRIUmCAvEz2QUJepLf3_9oqGYWeljHdMAo\u002F?source=tuwen_detail)\n\n湖北日报讯（记者汪彤、通讯员杨文）日前，中国农学会公布2026年度37项农业“火花技术”培育成果库名单，湖北报送的“新型高效微生物杀虫剂”“油菜抗裂角落粒减损增产产品以及配套技术”“基于SrCas12a-2新型工具酶的水产基因编辑系统”三项技术入选，标志着湖北在农业新质生产力培育上获得新突破。\n\n“火花技术”是中国农学会受农业农村部委托，面向农业前沿交叉领域开展的成果征集工作，旨在挖掘处于萌芽状态或成长阶段、具有前瞻性和颠覆性的农业新技术、新产品、新装备。从入选条件看，这些成果聚焦解决农业生产技术难题或产业发展“卡脖子”问题，代表着未来农业发展方向。\n\n近年来，我国果蔬、粮棉等作物受植食性害螨危害日趋严重。湖北省农科院联合企业，从特色微生物资源中筛选获得高毒力杀螨菌株，突破高密度发酵与高活性稳定化制剂关键技术，创制出新型高效微生物杀螨剂，防效优异、持效期长、环境相容性好，可替代部分化学农药，用于多类作物的害螨绿色防控。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002Ff565e784ee9e46b8aace9dbc05059370~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1791158595&x-signature=ng5a%2BAGyulsVpwhFvav%2FWiZR86o%3D)\n油菜的角果成熟后稍一触碰就炸裂，籽粒弹射落地。炸果落粒带来的损失，一直是油菜全程机械化生产中的痛点和难点。华中农业大学联合企业研发出一种抗裂角专用粘合剂，在角果定型时喷施，可在角果皮表面形成一层柔性保护膜，收获时能有效抑制炸裂，从源头减少落粒，机收损失率降低5％左右。\n\n武汉尚睿生物科技有限公司“基于SrCas12a-2新型工具酶的水产基因编辑系统”，有望成为水产育种家的“新帮手”。据了解，该系统突破了传统水产育种方式周期长、改良性状不确定等局限，可实现特定基因的精准敲除与修饰，快速培育出具有优良性状的水生生物品系，大幅缩短育种周期、提升改良效率。","湖北日报（今日头条转载）2026-09-18","2026-09-18T00:00:00Z",70,{"impact":396,"substance":18,"depth":50,"authority":13,"freshness":63,"relevant":21,"comment":507},"国家级火花技术名单公布，三项湖北成果覆盖绿色防控、油菜机收减损与水产基因编辑，信息增量足但已过时效窗口。",[509],{"name":503,"url":500},[113,203,511,27,512],"绿色防控","农业科技",{"code":514,"slug":515,"name":516,"path":517,"parent_code":35},"A4","soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds",[519],{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},{"status":44,"locked":14,"version":45,"method":46,"evidence":521},{"candidates":522,"agricultural_evidence":51},[523,527,532,538,542,547,551],{"code":38,"score":107,"supported":51,"title":524,"abstract":525,"tags":526},[27],[27],[27],{"code":514,"score":138,"supported":51,"title":528,"abstract":530,"tags":531},[529],"油菜",[529],[],{"code":533,"score":138,"supported":51,"title":534,"abstract":536,"tags":537},"D",[535],"水产",[535],[],{"code":144,"score":21,"supported":14,"title":539,"abstract":540,"tags":541},[],[],[113],{"code":543,"score":21,"supported":14,"title":544,"abstract":545,"tags":546},"F4",[],[],[511],{"code":149,"score":21,"supported":14,"title":548,"abstract":549,"tags":550},[],[],[203],{"code":456,"score":21,"supported":14,"title":552,"abstract":553,"tags":554},[],[],[511],[556,557],"湖北 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