[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1688":3,"related-1688":66},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":61,"slug":64,"view_count":20,"doi":8,"paper":8,"created_at":65},1688,"上海科学家给土壤装上\"AI 大脑\"——\"坤灵智壤-多智能体协同都市农田土壤健康诊断系统 V1.0\"上线","https:\u002F\u002Fnews.qq.com\u002Frain\u002Fa\u002FLNK2026090121779300","近日，上海市农业科学院生态环境保护研究所土壤修复与保育团队正式上线\"坤灵智壤-多智能体协同都市农田土壤健康诊断系统 V1.0\"。该系统基于多源数据构建了集土壤健康监测数据库、专业计算引擎与多智能体协同系统于一体的综合性智能平台，将 AI 技术与土壤健康评估深度融合，打造农田土壤\"数字医生\"，已建成覆盖 6 大类因子、25 个协变量、1629 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2](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fjwb_red_r1_c2.jpg)![Image 3](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fjwb_red_r1_c3.jpg) ![Image 4](https:\u002F\u002Fwww.dfcxb.com\u002Fresfile\u002F2026-09-03\u002F03\u002Fdfcxb-20260903-003_b.jpg) ![Image 5](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fjwb_red_r8_c2.jpg)![Image 6](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fjwb_red_r8_c3.jpg) 第03：**关注**[上一版 3](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fnode_106591.htm)[4 下一版](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fnode_106593.htm)[![Image 7](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fpdf.gif)](https:\u002F\u002Fwww.dfcxb.com\u002Fresfile\u002F2026-09-03\u002F03\u002Fdfcxb-20260903-003.pdf) 标题导航 ![Image 8](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fimage06.jpg) ![Image 9](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fcheckbox.gif)[嘉定双塘村用“小切口”激活“大资源”](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842663.htm) 一本积分账、一方小菜园、一家邻里餐厅……~~~ ![Image 10](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fcheckbox.gif)[土壤健康智能诊断系统上线](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm) ~~~ ![Image 11](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fcheckbox.gif)[上海的夏季结出了新鲜草莓](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842665.htm) ~~~**2026年09月03日****星期四 出版**![Image 12](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fimage01.jpg)[上一期](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)[下一期](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)![Image 13](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fimage02.jpg)[返回首页](https:\u002F\u002Fwww.dfcxb.com\u002Fpaperindex.htm) |[版面概览](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Findex_2026-09-03.htm) |[版面导航](javascript:;) |[标题导航](javascript:;) [3 上一篇](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842663.htm)[下一篇 4](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842665.htm)[放大![Image 14](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fzoomin.gif)](javascript:;)[缩小![Image 15](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fzoomout.gif)](javascript:;)[默认![Image 16](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fzoomact.gif)](javascript:;)[朗读![Image 17](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fsoundon.gif)](javascript:;) 土壤健康智能诊断系统上线 □记者 施勰贇 近日，上海市农业科学院生态环境保护研究所土壤修复与保育团队正式上线“坤灵智壤——多智能体协同都市农田土壤健康诊断系统V1.0”。 系统集土壤健康监测数据库、专业计算引擎与多智能体协同平台于一体，实现了土壤健康从数据感知到AI智能诊断决策的全链路管理，重点破解土壤监测数据分散、评价标准不一、分析精度不足、效率不高等痛点。 农田土壤是粮食安全的根基。然而，传统土壤健康评估长期依赖分散的人工经验和统计方法，存在数据异构难融、评价标准不一等短板。基层农技人员和种植户常面临“有数据、看不懂，有问题、难定位”的困境。 为响应国家耕地质量保护需求，上海市农业科学院生态环境保护研究所土壤修复与保育团队联合歌历伽谱（上海）科技发展有限责任公司、上海卓益得机器人有限公司研发推出“坤灵智壤系统”，将AI技术与土壤健康评估深度融合，推动土壤健康管理从“模糊经验”走向“精准施策”。 据介绍，该系统定位为面向农田土壤健康评估的AI专家，具备多源数据融合与智能质控、“1+7”多智能体协同精准诊断、空间智能制图与可解释推理、全天候智能守护四大核心功能。 与传统人工测算相比，该系统内置专业计算引擎，支持土壤全数据集与最小数据集自主切换，提供线性和非线性两种隶属度评价方案。通过多智能体协同和标准化任务流程定义，诊断报告中的每个数值均可追溯至具体计算步骤和原始数据，有效降低大模型“幻觉”风险。与通用大语言模型相比，系统在土壤健康评估的专业性、场景适配性和可解释性等方面提升25%至80%。 上海市农业科学院生态环境保护研究所相关负责人表示，该系统前期内测用户已覆盖浙江大学、上海交通大学、华东师范大学、上海理工大学、四川省农业科学院、四川农业大学、上海市嘉定区农业技术推广服务中心等多家单位。正式上线后，将向更多科研人员、农技人员、农业企业和基层农户开放服务，公众可通过相关平台与“坤灵智壤”智能体对话。 - [x] [3 上一篇](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842663.htm)[下一篇 4](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842665.htm)[放大![Image 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27](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fpdf.gif)](https:\u002F\u002Fwww.dfcxb.com\u002Fresfile\u002F2026-09-03\u002F04\u002Fdfcxb-20260903-004.pdf)\n[第05版：三农实用周刊](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fnode_106594.htm)[![Image 28](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fpdf.gif)](https:\u002F\u002Fwww.dfcxb.com\u002Fresfile\u002F2026-09-03\u002F05\u002Fdfcxb-20260903-005.pdf)\n[第06版：新主体](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fnode_106595.htm)[![Image 29](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fpdf.gif)](https:\u002F\u002Fwww.dfcxb.com\u002Fresfile\u002F2026-09-03\u002F06\u002Fdfcxb-20260903-006.pdf)\n[第07版：农技](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fnode_106596.htm)[![Image 30](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fpdf.gif)](https:\u002F\u002Fwww.dfcxb.com\u002Fresfile\u002F2026-09-03\u002F07\u002Fdfcxb-20260903-007.pdf)\n[第08版：禁毒](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fnode_106597.htm)[![Image 31](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fpdf.gif)](https:\u002F\u002Fwww.dfcxb.com\u002Fresfile\u002F2026-09-03\u002F08\u002Fdfcxb-20260903-008.pdf)\n\n[嘉定双塘村用“小切口”激活“大资源”](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842663.htm)\n[土壤健康智能诊断系统上线](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm)\n[上海的夏季结出了新鲜草莓](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842665.htm)\n\n![Image 32](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fd1.gif)![Image 33](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fd.gif)![Image 34](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fd1.gif)![Image 35](https:\u002F\u002Fwww.dfcxb.com\u002Fimage\u002Fd.gif)\n**日****一****二****三****四****五****六**\n[](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm)[](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm)[1](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F01\u002Fnode_1.htm)\n\n十九[2](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)[3](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fnode_1.htm)\n\n廿一[4](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F04\u002Fnode_1.htm)\n\n廿二[5](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)\n[6](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)[7](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)[8](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F08\u002Fnode_1.htm)\n\n廿六[9](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)[10](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)[11](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)[12](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)\n[13](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)[14](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)[15](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)[16](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)[17](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)[18](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)[19](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)\n[20](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)[21](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)[22](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)[23](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)[24](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)[25](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)[26](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)\n[27](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)[28](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)[29](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)[30](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm#)[](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm)[](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm)[](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm)\n[](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm)[](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm)[](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm)[](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm)[](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm)[](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm)[](https:\u002F\u002Fwww.dfcxb.com\u002Fhtml\u002F2026-09\u002F03\u002Fcontent_106592_19842664.htm)\n\n东方城乡报 关注 03 土壤健康智能诊断系统上线 2026-09-03 2 2026年09月03日 星期四","东方城乡报 2026-09-03","2026-09-03T00:00:00Z",68,{"impact":17,"substance":79,"depth":16,"authority":12,"freshness":19,"relevant":20,"comment":80},20,"上海农科院上线土壤健康AI诊断系统，技术细节与多方合作信息充实，对智慧农业有实质推进。",[82],{"name":75,"url":72},[25,26,27,29],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":87},{"candidates":88,"agricultural_evidence":45},[89,93,97],{"code":31,"score":44,"supported":45,"title":90,"abstract":91,"tags":92},[47],[47],[29],{"code":51,"score":52,"supported":13,"title":94,"abstract":95,"tags":96},[],[],[26,27],{"code":57,"score":20,"supported":13,"title":98,"abstract":99,"tags":100},[],[],[25],[62,63],"农业人工智能土壤健康多智能体智慧农业-1943","2026-09-09T00:03:55.045035Z",{"id":105,"title":106,"url":107,"summary":108,"summary_zh":109,"content":8,"source_name":110,"source_url":107,"published_at":111,"category":112,"cover_url":8,"hotness":12,"is_selected":45,"score":113,"score_detail":114,"sources":119,"tags":121,"primary_topic":125,"secondary_topics":126,"classification":127,"search_phrases":207,"slug":210,"view_count":211,"doi":212,"paper":213,"created_at":244},4832,"Applications and Future Prospects of Plant Multi-Omics Technologies in Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fijms27209034","Against the backdrop of global population growth, intensifying climate change, and limited arable land, ensuring food security and promoting sustainable agriculture have become critical issues. The limitations of conventional agricultural systems are becoming increasingly apparent, creating an urgent need for innovation driven by science and technology. In this context, plant multi-omics technologies serve as the foundational data and technical cornerstone of systems biology and precision agriculture. Integrating multi-level data from genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics offers substantial advantages for systematically understanding and improving plants. Unlike previous reviews that mainly catalog omics technologies or focus on individual crops, this review adopts a unified discovery-to-translation framework and critically evaluates the strength of evidence across applications. This review focuses on cutting-edge applications of multi-omics in three key areas: improving the traits of cereal crops (rice, wheat and maize), remediating soil health, and innovating products such as biofertilizers and biostimulants for agriculture. We systematically explore the roles of multi-omics in accelerating breeding processes, deciphering complex agronomic traits, restoring soil ecosystems, and constructing intelligent decision-support systems. The review also examines current challenges, including the complexity of data integration and the difficulties of predicting genotype-by-environment interactions. Furthermore, it outlines future directions involving deeper convergence with artificial intelligence, high-throughput phenomics, and digital twins. In summary, plant multi-omics technology is a vital tool for unravelling the mysteries of plant life and is also a key enabler of intelligent, precise and sustainable agricultural transformation.","在全球人口增长、气候变化加剧和耕地有限的背景下，保障粮食安全和促进可持续农业已成为关键议题。传统农业系统的局限性日益显现，迫切需要由科学和技术驱动的创新。在此背景下，植物多组学技术作为系统生物学和精准农业的基础数据与技术基石，整合基因组学、转录组学、蛋白质组学、代谢组学、表观基因组学和微生物组学等多层次数据，为系统理解和改良植物提供了显著优势。与以往主要罗列组学技术或聚焦单一作物的综述不同，本综述采用统一的“从发现到转化”框架，并批判性评估各应用中的证据强度。本综述聚焦多组学在三个关键领域的前沿应用：改良谷类作物（水稻、小麦和玉米）的性状、修复土壤健康，以及创新生物肥料和生物刺激素等农业产品。我们系统探讨了多组学在加速育种进程、解析复杂农艺性状、恢复土壤生态系统和构建智能决策支持系统中的作用。本综述还审视了当前挑战，包括数据整合的复杂性和预测基因型与环境互作的困难。此外，文章展望了未来方向，涉及与人工智能、高通量表型组学和数字孪生的更深层次融合。总之，植物多组学技术是揭示植物生命奥秘的重要工具，也是智能、精准和可持续农业转型的关键使能技术。","International Journal of Molecular Sciences","2026-10-12T00:00:00Z","论文",83,{"impact":115,"substance":116,"depth":17,"authority":18,"freshness":117,"relevant":20,"comment":118},22,21,8,"核心期刊综述，系统梳理植物多组学在精准农业中的应用与AI、数字孪生等未来方向，专业深度与信源权威度俱佳，值得进入每日精选。",[120],{"name":110,"url":107},[25,26,122,123,29,124],"种业振兴","精准农业","多组学",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":128},{"candidates":129,"agricultural_evidence":45},[130,137,142,149,156,163,170,177,184,190,197,202],{"code":57,"score":131,"supported":45,"title":132,"abstract":134,"tags":136},19,[133],"precision agriculture",[123,135,133],"数字孪生",[25,123],{"code":31,"score":138,"supported":45,"title":139,"abstract":140,"tags":141},7,[],[47,32],[29],{"code":143,"score":67,"supported":45,"title":144,"abstract":145,"tags":148},"A1",[],[146,147],"水稻","rice",[],{"code":150,"score":67,"supported":45,"title":151,"abstract":152,"tags":155},"A2",[],[153,154],"小麦","wheat",[],{"code":157,"score":67,"supported":45,"title":158,"abstract":159,"tags":162},"A3",[],[160,161],"玉米","maize",[],{"code":164,"score":67,"supported":45,"title":165,"abstract":166,"tags":169},"A8",[],[167,168],"粮食安全","food security",[],{"code":171,"score":67,"supported":45,"title":172,"abstract":173,"tags":176},"H7",[],[174,175],"气候变化","climate change",[],{"code":178,"score":67,"supported":45,"title":179,"abstract":180,"tags":183},"H8",[],[181,182],"可持续农业","sustainable agriculture",[],{"code":51,"score":185,"supported":45,"title":186,"abstract":187,"tags":189},4,[],[188],"artificial intelligence",[26],{"code":191,"score":192,"supported":45,"title":193,"abstract":194,"tags":196},"F3",3,[],[195],"肥料",[],{"code":198,"score":20,"supported":13,"title":199,"abstract":200,"tags":201},"F1",[],[],[122],{"code":203,"score":20,"supported":13,"title":204,"abstract":205,"tags":206},"F2",[],[],[124],[208,209],"植物多组学 精准农业","多组学 智能育种","植物多组学精准农业-4832",0,"10.3390\u002Fijms27209034",{"doi":212,"openalex_id":214,"authors":215,"venue":110,"cited_by_count":211,"oa_url":107,"card":236,"direction":242,"ingested_from":243},"W7221567543",[216,219,221,224,226,228,231,234],{"name":217,"orcid":218},"Tao Tong","https:\u002F\u002Forcid.org\u002F0000-0002-9003-9667",{"name":220,"orcid":8},"Wang Qingxia",{"name":222,"orcid":223},"Fanrong Zeng","https:\u002F\u002Forcid.org\u002F0000-0001-7742-5195",{"name":225,"orcid":8},"Shuzhen Ye",{"name":227,"orcid":8},"Zhijuan Ji",{"name":229,"orcid":230},"Zhong‐Hua Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7531-320X",{"name":232,"orcid":233},"Jiaoyu Wang","https:\u002F\u002Forcid.org\u002F0000-0002-0249-808X",{"name":235,"orcid":8},"Younan Ouyang",{"tldr":237,"method":238,"finding":239,"direction":240,"opportunity":241},"综述植物多组学在精准农业中的应用，涵盖作物改良、土壤修复和生物产品创新。","整合基因组、转录组、蛋白组、代谢组等多组学数据，采用发现到转化框架。","多组学可加速育种、解析复杂农艺性状、修复土壤生态并构建智能决策系统。","农业人工智能与决策模型","多组学与AI、高通量表型、数字孪生融合，解决基因型-环境互作预测难题。","数字乡村与农业信息化","openalex","2026-10-13T23:30:29.504594Z",{"id":246,"title":247,"url":248,"summary":249,"summary_zh":250,"content":8,"source_name":251,"source_url":248,"published_at":252,"category":112,"cover_url":8,"hotness":12,"is_selected":13,"score":253,"score_detail":254,"sources":256,"tags":258,"primary_topic":261,"secondary_topics":262,"classification":268,"search_phrases":296,"slug":299,"view_count":52,"doi":300,"paper":301,"created_at":313},3680,"AI-Driven Nutrient Optimization Systems for Precision Agriculture: A Data-Integrated Approach to Reducing Nutrient Runoff and Enhancing Soil Health in Agricultural Systems","https:\u002F\u002Fdoi.org\u002F10.36948\u002Fijfmr.2026.v08i05.85286","Nutrients play a fundamental role in plant growth, soil fertility, and overall agricultural productivity. However, inefficient fertilizer application in conventional farming systems often leads to nutrient imbalance, reduced soil quality, and increased environmental degradation. The emergence of precision agriculture and artificial intelligence (AI) has introduced new opportunities for improving nutrient management through data-driven decision-making. This study investigates an AI-driven nutrient optimization system designed to enhance fertilizer efficiency while reducing nutrient runoff and improving soil health. Findings indicate that AI-based systems significantly improve nutrient use efficiency by enabling site-specific fertilizer recommendations and adaptive management strategies. The integration of multi-source agricultural data enhances prediction accuracy for soil nutrient status and crop demand, while also supporting early identification of nutrient loss risks such as runoff and leaching. The study concludes that AI-driven nutrient optimization provides a sustainable and scalable approach for modern agriculture, supporting both productivity enhancement and environmental protection through intelligent, data-integrated farming systems.","养分在植物生长、土壤肥力和农业整体生产力中发挥着基础性作用。然而，传统农业系统中低效的施肥方式往往导致养分失衡、土壤质量下降以及环境退化加剧。精准农业与人工智能（AI）的出现，为通过数据驱动决策改善养分管理带来了新的机遇。本研究探讨了一种AI驱动的养分优化系统，旨在提高肥料利用效率，同时减少养分径流并改善土壤健康。研究结果表明，基于AI的系统通过实现特定地块的施肥建议和适应性管理策略，显著提高了养分利用效率。多源农业数据的整合提升了土壤养分状况和作物需求的预测精度，同时也有助于早期识别径流和淋溶等养分流失风险。研究得出结论，AI驱动的养分优化为现代农业提供了一种可持续且可扩展的方法，通过智能化、数据整合的农业系统，同时支持生产力提升和环境保护。","International Journal For Multidisciplinary Research","2026-09-26T00:00:00Z",61,{"impact":44,"substance":17,"depth":16,"authority":117,"freshness":117,"relevant":20,"comment":255},"论文提出AI驱动的养分优化系统，方法有新意但属学术探讨，产业落地影响有限，可作为智慧农业技术动态收录。",[257],{"name":251,"url":248},[25,26,259,29,260],"精准施肥","养分流失",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[263],{"code":191,"slug":264,"name":265,"path":266,"parent_code":267},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"status":38,"locked":13,"version":39,"method":40,"evidence":269},{"candidates":270,"agricultural_evidence":45},[271,275,279,286,290],{"code":31,"score":16,"supported":45,"title":272,"abstract":273,"tags":274},[32],[47,32],[29],{"code":57,"score":16,"supported":45,"title":276,"abstract":277,"tags":278},[133],[123,133],[25],{"code":191,"score":12,"supported":45,"title":280,"abstract":281,"tags":285},[],[282,195,283,284],"施肥","养分管理","fertilizer",[259],{"code":51,"score":185,"supported":45,"title":287,"abstract":288,"tags":289},[],[188],[26],{"code":291,"score":192,"supported":45,"title":292,"abstract":293,"tags":295},"I6",[],[294],"agricultural data",[],[297,298],"AI 养分优化 精准农业","养分流失 土壤健康","AI养分优化精准农业-3680","10.36948\u002Fijfmr.2026.v08i05.85286",{"doi":300,"openalex_id":302,"authors":303,"venue":251,"cited_by_count":211,"oa_url":248,"card":308,"direction":240,"ingested_from":243},"W7214553198",[304,306],{"name":305,"orcid":8},"Olufisayo Obebe",{"name":307,"orcid":8},"Ebenezer Akinola",{"tldr":309,"method":310,"finding":311,"direction":240,"opportunity":312},"研究AI驱动的养分优化系统，通过数据集成实现精准施肥，减少养分流失并改善土壤健康。","集成多源农业数据与AI算法，进行站点特异性施肥推荐和自适应管理。","AI系统显著提高养分利用效率，增强土壤养分与作物需求预测精度，并早期识别流失风险。","可探索多源数据融合的实时决策模型，以及在不同土壤气候条件下AI系统的可扩展性与长期环境效应评估。","2026-09-28T23:30:49.535963Z",{"id":315,"title":316,"url":317,"summary":318,"summary_zh":319,"content":8,"source_name":320,"source_url":317,"published_at":321,"category":112,"cover_url":8,"hotness":12,"is_selected":13,"score":322,"score_detail":323,"sources":327,"tags":329,"primary_topic":333,"secondary_topics":334,"classification":340,"search_phrases":355,"slug":358,"view_count":211,"doi":359,"paper":360,"created_at":374},5583,"Active-Labeling Evaluation for Computer Agriculture Nematode-Cyst Detection Under Sparse Soil-Sample Ground Truth: An Artificial Intelligence Annotation-Efficiency Bound","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544145","Active learning promises to reduce the annotation burden of training nematode-cyst detection models for precision agriculture, but the size of that reduction is not known when ground truth comes from sparse soil samples rather than exhaustive visual inspection. the present analysis derives an annotation-efficiency bound for pool-based active learning under sparse soil-sample ground truth. The detection task is formalised as a two-stage process in which a classifier selects candidate cysts from soil imagery and a laboratory assay confirms a small labelled subset. Under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64, the bound is evaluated across a design grid of 8, 16, 24 and 32 clusters and effect sizes of 3, 5 and 8 percentage points, with 2000 replications per cell. The annotation-efficiency gain is bounded by the ratio of label noise to cluster-level variance, and that 52 clusters are required to detect a 5-percentage-point improvement at 80% power. The bound provides a design rule for allocating annotation effort between active selection and confirmatory soil sampling.","主动学习有望减轻精准农业中训练线虫囊检测模型所需的标注负担，但当真实标签来自稀疏土壤样本而非详尽的视觉检查时，这种负担减轻的幅度尚不清楚。本分析推导了在稀疏土壤样本真实标签下基于池的主动学习的标注效率界限。检测任务被形式化为两阶段过程：分类器从土壤图像中筛选候选线虫囊，实验室检测确认一个小的已标注子集。在设定的方差结构下——组间标准差为6.0、残差标准差为4.5、组内相关系数为0.64——该界限在8、16、24和32个簇的设计网格以及3、5和8个百分点的效应量下进行评估，每个单元格重复2000次。标注效率增益受标签噪声与簇级方差之比的约束，并且在80%检验功效下检测5个百分点的改进需要52个簇。该界限为在主动选择与验证性土壤采样之间分配标注工作量提供了设计规则。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",64,{"impact":117,"substance":79,"depth":324,"authority":325,"freshness":12,"relevant":20,"comment":326},17,9,"该研究给出稀疏土壤样本下主动学习标注效率的理论边界与聚类设计规则，方法新颖、结论具体，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[328],{"name":320,"url":317},[25,26,330,331,332],"主动学习","线虫检测","土壤采样",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[335],{"code":51,"slug":336,"name":337,"path":338,"parent_code":339},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":38,"locked":13,"version":39,"method":40,"evidence":341},{"candidates":342,"agricultural_evidence":45},[343,347,351],{"code":31,"score":18,"supported":45,"title":344,"abstract":345,"tags":346},[32],[47,32],[],{"code":51,"score":325,"supported":45,"title":348,"abstract":349,"tags":350},[188],[],[26],{"code":57,"score":138,"supported":45,"title":352,"abstract":353,"tags":354},[],[123,133],[25],[356,357],"线虫孢囊 检测 主动学习","稀疏土壤样本 标注效率","线虫孢囊检测主动学习-5583","10.5281\u002Fzenodo.23544145",{"doi":359,"openalex_id":361,"authors":362,"venue":320,"cited_by_count":211,"oa_url":317,"card":369,"direction":240,"ingested_from":243},"W7223082195",[363,365,367],{"name":364,"orcid":8},"Amine Ben Salah",{"name":366,"orcid":8},"Ines Trabelsi",{"name":368,"orcid":8},"Mohamed Aziz Khelifi",{"tldr":370,"method":371,"finding":372,"direction":240,"opportunity":373},"推导稀疏土壤样本真值下主动学习标注效率的理论上界。","基于方差结构与设计网格的统计模拟，2000次重复。","标注效率增益受标签噪声与簇间方差之比限制，检测5个百分点提升需52簇。","可将该上界推广到其他稀疏真值农业检测任务，并探索主动选择与确认采样的最优分配策略。","2026-10-22T11:20:58.901747Z",{"id":376,"title":377,"url":378,"summary":379,"summary_zh":380,"content":8,"source_name":381,"source_url":378,"published_at":382,"category":112,"cover_url":8,"hotness":12,"is_selected":13,"score":383,"score_detail":384,"sources":387,"tags":389,"primary_topic":393,"secondary_topics":398,"classification":401,"search_phrases":427,"slug":430,"view_count":211,"doi":431,"paper":432,"created_at":453},5006,"Mapping Soil Salinity in Arid Agricultural Regions Using Multi-Source Remote Sensing and a Long-Range Dependency-Enhanced Deep Learning Framework","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101966","Soil salinity estimation across arid agricultural regions is challenging due to pronounced environmental heterogeneity and complex dependencies among multi-source variables. This study proposes a Long-Range Dependency-Enhanced Network (LRD-Net) for regional-scale soil salinity estimation in multiple agricultural oasis regions using multi-source remote sensing and environmental covariates. A dataset comprising 180 field samples from three agricultural oasis regions in Xinjiang, China, was constructed with 25 covariates derived from spectral, topographic, climatic, and soil property data. LRD-Net consists of three stages: a multi-scale feature fusion backbone based on depthwise separable convolutions and cross-scale interaction; a Long-Range Dependency Enhancement Module (LDEM) with an asymmetric dual-branch architecture that captures forward and reverse-aware dependencies through reverse alignment and cross-directional fusion; and multi-scale attention refinement with a regression head for continuous salinity estimation. Under the region-stratified random-split evaluation, LRD-Net achieved an R2 of 0.905, an RMSE of 1.207 g·kg−1, and an MAE of 0.868 g·kg−1. Performance decreased under more spatially independent validation, with an R2 of 0.874 under spatial cross-validation and LORO test R2 values ranging from 0.739 to 0.801, indicating that prediction across spatially and geographically independent samples is more challenging than region-stratified random-split evaluation. The proposed framework effectively integrates multi-source information and captures complementary long-range dependencies, providing a potential approach for regional-scale soil salinity estimation within the studied arid agricultural oasis environments and supporting soil salinization monitoring and sustainable land management in the study region.","干旱农业区域土壤盐分估算面临显著的环境异质性和多源变量间复杂依赖关系的挑战。本研究提出了一种长程依赖增强网络（LRD-Net），利用多源遥感与环境协变量对多个农业绿洲区域进行区域尺度土壤盐分估算。研究构建了包含中国新疆三个农业绿洲区域180个野外样本的数据集，并衍生出25个协变量，涵盖光谱、地形、气候和土壤属性数据。LRD-Net由三个阶段组成：基于深度可分离卷积和跨尺度交互的多尺度特征融合骨干网络；具有非对称双分支架构的长程依赖增强模块（LDEM），通过反向对齐和跨方向融合捕获前向与反向感知依赖关系；以及多尺度注意力细化与回归头，用于连续盐分估算。在区域分层随机划分评估下，LRD-Net取得了R²为0.905、RMSE为1.207 g·kg⁻¹、MAE为0.868 g·kg⁻¹的性能。在空间独立性更强的验证下性能有所下降，空间交叉验证R²为0.874，留一区域测试R²范围为0.739至0.801，表明跨空间和地理独立样本的预测比区域分层随机划分评估更具挑战性。所提出的框架有效整合了多源信息并捕获了互补的长程依赖关系，为研究区干旱农业绿洲环境下的区域尺度土壤盐分估算提供了潜在方法，并支持研究区土壤盐渍化监测与可持续土地管理。","Land","2026-10-14T00:00:00Z",72,{"impact":44,"substance":116,"depth":324,"authority":385,"freshness":325,"relevant":20,"comment":386},13,"方法新颖、数据扎实的遥感土壤盐渍化估算研究，对干旱区盐碱地监测与治理有参考价值，但属细分领域学术进展，公共影响有限。",[388],{"name":381,"url":378},[25,26,390,391,392],"遥感","土壤盐渍化","新疆农业",{"code":394,"slug":395,"name":396,"path":397,"parent_code":339},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[399,400],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":51,"slug":336,"name":337,"path":338,"parent_code":339},{"status":38,"locked":13,"version":39,"method":40,"evidence":402},{"candidates":403,"agricultural_evidence":45},[404,409,413,418,423],{"code":394,"score":16,"supported":45,"title":405,"abstract":407,"tags":408},[406],"remote sensing",[390,406],[390],{"code":31,"score":18,"supported":45,"title":410,"abstract":411,"tags":412},[32],[47,32],[],{"code":51,"score":325,"supported":45,"title":414,"abstract":416,"tags":417},[415],"deep learning",[],[26],{"code":171,"score":192,"supported":45,"title":419,"abstract":420,"tags":422},[],[421],"干旱",[],{"code":57,"score":20,"supported":13,"title":424,"abstract":425,"tags":426},[],[],[25],[428,429],"新疆 农业绿洲 土壤盐渍化 遥感","LRD-Net 土壤盐分 估算","新疆农业绿洲土壤盐渍化遥感-5006","10.3390\u002Fland15101966",{"doi":431,"openalex_id":433,"authors":434,"venue":381,"cited_by_count":211,"oa_url":378,"card":447,"direction":451,"ingested_from":243},"W7221816183",[435,437,440,442,444],{"name":436,"orcid":8},"Mingjie Qian",{"name":438,"orcid":439},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":441,"orcid":8},"Peng Wang",{"name":443,"orcid":8},"Hangyuan Liu",{"name":445,"orcid":446},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":448,"method":449,"finding":450,"direction":451,"opportunity":452},"提出LRD-Net深度学习框架，融合多源遥感与环境协变量，估算新疆干旱绿洲区土壤盐分。","构建180个样点、25个协变量的数据集，设计含长程依赖增强模块的多尺度网络。","区域分层随机划分下R²达0.905，但空间独立验证R²降至0.739-0.874，跨区域预测更难。","农业遥感与作物表型","可探索跨区域迁移学习与不确定性量化，提升空间独立场景下盐分制图泛化能力。","2026-10-15T23:30:29.667342Z",{"id":455,"title":456,"url":457,"summary":458,"summary_zh":459,"content":8,"source_name":460,"source_url":457,"published_at":461,"category":112,"cover_url":8,"hotness":12,"is_selected":13,"score":383,"score_detail":462,"sources":464,"tags":466,"primary_topic":468,"secondary_topics":469,"classification":471,"search_phrases":508,"slug":511,"view_count":20,"doi":512,"paper":513,"created_at":525},4896,"Site-Specific Precision Cover Cropping for Soil Health and Environmental Sustainability Using Remote Sensing and Multi-Criteria Analysis","https:\u002F\u002Fdoi.org\u002F10.21663\u002Feeg-d-26-00041","ABSTRACT Cover crops, which are plants or mixtures of plants grown primarily to enhance and protect soil health, are a key strategy in sustainable agriculture. Traditionally, they have been uniformly planted in a field typically during off-season when the main crops are not in competition. However, advancements in precision agriculture, particularly the integration of remote-sensing technology, demand more efficient seeding strategies based on field variability. The aims of this study were to assess the specific needs of a field in Aurora County, SD, and determine the type and optimal combinations of the cover crop mix. The cover crop species considered in this study included legumes, grasses, and brassicas. The remote-sensing data were extracted from Sentinel-2 satellite imagery. Soil samples were collected from 0 to 30 cm depth, and soil properties were measured. Additionally, topographic maps such as slope and aspect were extracted from light detection and ranging (LiDAR) data. The results of precision cover crop analyses showed that 6.5 percent (7.8 ha), 20.6 percent (24.7 ha), 38.1 percent (45.7 ha), 29.6 percent (35.5 ha), and 5.3 percent (6.3 ha) of the studied field should planted in the following mixtures of crops: rye, radish, and vetch; triticale, turnip, and vetch; triticale, radish, and clover; triticale, rapeseed, and pea; and barley, flax, and clover, respectively. The mixtures significantly improved soil properties by reducing compaction, increasing residue C\u002FN ratio, and enhancing weed suppression compared with the control. The resulting map should be interpreted as a decision-support recommendation rather than a fully validated field prescription.","摘要 覆盖作物（cover crops）是指主要为了提升和保护土壤健康而种植的植物或植物混合体，是可持续农业的一项关键策略。传统上，覆盖作物通常在非主作物竞争的季节以均匀方式播种于田间。然而，精准农业的进步，尤其是遥感技术的整合，要求基于田块变异性制定更高效的播种策略。本研究旨在评估南达科他州奥罗拉县某田块的具体需求，并确定覆盖作物混合物的类型及最优组合。本研究考虑的覆盖作物种类包括豆科、禾本科和十字花科植物。遥感数据提取自Sentinel-2卫星影像。土壤样品采集深度为0至30 cm，并测定了土壤性质。此外，从激光雷达（LiDAR）数据中提取了坡度和坡向等地形图。精准覆盖作物分析结果表明，研究田块中分别有6.5%（7.8 ha）、20.6%（24.7 ha）、38.1%（45.7 ha）、29.6%（35.5 ha）和5.3%（6.3 ha）的面积应种植以下作物混合物：黑麦、萝卜和野豌豆；小黑麦、芜菁和野豌豆；小黑麦、萝卜和三叶草；小黑麦、油菜和豌豆；以及大麦、亚麻和三叶草。与对照相比，这些混合物通过降低紧实度、提高残体碳氮比和增强杂草抑制效果，显著改善了土壤性质。所得图谱应被解读为决策支持建议，而非经过充分验证的田间处方。","Environmental and Engineering Geoscience","2026-10-13T00:00:00Z",{"impact":44,"substance":116,"depth":324,"authority":385,"freshness":325,"relevant":20,"comment":463},"该研究将遥感与多准则分析结合用于精准覆盖作物制图，方法新颖、数据具体，对农业信息化与可持续农业有参考价值，但属单点田间案例，影响范围有限。",[465],{"name":460,"url":457},[25,123,390,29,467],"覆盖作物",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[470],{"code":394,"slug":395,"name":396,"path":397,"parent_code":339},{"status":38,"locked":13,"version":39,"method":40,"evidence":472},{"candidates":473,"agricultural_evidence":45},[474,478,482,486,493,497,503],{"code":31,"score":16,"supported":45,"title":475,"abstract":476,"tags":477},[32],[47,32],[29],{"code":394,"score":44,"supported":45,"title":479,"abstract":480,"tags":481},[406],[390],[390],{"code":57,"score":117,"supported":45,"title":483,"abstract":484,"tags":485},[],[123,133],[25,123],{"code":487,"score":67,"supported":45,"title":488,"abstract":489,"tags":492},"A4",[],[490,491],"油菜","rapeseed",[],{"code":178,"score":67,"supported":45,"title":494,"abstract":495,"tags":496},[],[181,182],[],{"code":498,"score":192,"supported":45,"title":499,"abstract":500,"tags":502},"B1",[],[501],"萝卜",[],{"code":504,"score":20,"supported":13,"title":505,"abstract":506,"tags":507},"A7",[],[],[467],[509,510],"Sentinel-2 遥感 覆盖作物","精准覆盖作物 土壤健康","Sentinel-2遥感覆盖作物-4896","10.21663\u002Feeg-d-26-00041",{"doi":512,"openalex_id":514,"authors":515,"venue":460,"cited_by_count":211,"oa_url":8,"card":520,"direction":451,"ingested_from":243},"W7221669942",[516,518],{"name":517,"orcid":8},"Ali Mirzakhani Nafchi",{"name":519,"orcid":8},"Salman Mirzaee",{"tldr":521,"method":522,"finding":523,"direction":451,"opportunity":524},"利用遥感与多准则分析为南达科他州田块制定精准覆盖作物混播方案。","Sentinel-2影像、LiDAR地形与土壤采样，结合多准则分析。","田块被划分为5个管理区，各推荐不同覆盖作物混播，显著改善土壤紧实度、残体C\u002FN比和抑草效果。","可验证该决策支持地图的田间效果，并融合实时物联网数据优化动态推荐。","2026-10-14T23:30:27.077312Z"]