[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3057":3,"related-3057":63},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":62},3057,"Recent Horizons in Pyridine-Based Agrochemicals (2020-2026): From Molecular Design to Ecological Safety","https:\u002F\u002Fdoi.org\u002F10.1021\u002Facsagscitech.6c00690","Abstract Pyridine derivatives have emerged as indispensable pharmacophoric scaffolds in contemporary agrochemistry, driving breakthroughs across next-generation insecticides, plant growth regulators (PGRs), micronutrient chelators, and biostimulants. This review provides a comprehensive, critical synthesis of structural innovations, advanced formulations, and ecological safety profiles of pyridine-based agrochemicals reported from 2020 to 2026. We systematically evaluate how structure-activity relationship (SAR) optimizations and density functional theory (DFT) quantum modeling have enabled the precise engineering of legacy neonicotinoids and targeted pyridine platforms to maximize entomological target-site affinity such as within insect nicotinic acetylcholine and ryanodine receptors while proactively decoupling insecticidal potency from non-target ecotoxicity toward vital pollinators like honeybees (Apis mellifera). Beyond molecular architecture, this work highlights the pioneering integration of green chemistry paradigms and smart formulation technologies, focusing on biodegradable polymer networks (e.g., carboxymethyl cellulose\u002Fpoly(4-vinylpyridine) (CMC\u002FP4VP) hydrogels) engineered to stabilize micronutrient chelation, trigger biostimulatory foliage responses, assist in soil remediation, and mitigate environmental leaching via controlled diffusion mechanisms. Furthermore, we examine the deployment of advanced analytical tools, including surface-enhanced Raman spectroscopy (SERS), for ultra-sensitive trace monitoring of pyridinic residues in agro-ecosystems. By bridging fundamental chemical engineering with stringent ecological compliance and future artificial intelligence-driven molecular design, this review delivers essential, actionable perspectives for researchers and policymakers advancing sustainable, climate-smart agriculture.","吡啶衍生物已成为当代农业化学中不可或缺的药效团骨架，推动了新一代杀虫剂、植物生长调节剂（PGRs）、微量营养元素螯合剂及生物刺激素等领域的突破性进展。本综述对2020至2026年间报道的吡啶类农用化学品的结构创新、先进制剂技术及生态安全性进行了全面而批判性的综合评述。我们系统评估了构效关系（SAR）优化与密度泛函理论（DFT）量子建模如何实现对传统新烟碱类及靶向吡啶平台的精准设计，以最大化昆虫靶标位点亲和力（如昆虫烟碱型乙酰胆碱受体和兰尼碱受体），同时主动将杀虫效力与非靶标生态毒性解耦，尤其是对蜜蜂（Apis mellifera）等重要传粉昆虫的毒性。在分子结构之外，本工作重点介绍了绿色化学范式与智能制剂技术的前沿融合，聚焦于可生物降解聚合物网络（如羧甲基纤维素\u002F聚（4-乙烯基吡啶）（CMC\u002FP4VP）水凝胶）的设计，旨在稳定微量营养元素螯合、触发叶片生物刺激响应、辅助土壤修复，并通过控释扩散机制减少环境淋溶。此外，我们还探讨了先进分析工具（包括表面增强拉曼光谱（SERS））在农业生态系统中吡啶类残留超灵敏痕量监测方面的应用。通过将基础化学工程与严格的生态合规要求及未来人工智能驱动的分子设计相衔接，本综述为推进可持续、气候智慧型农业的研究人员与政策制定者提供了重要且可操作的见解。",null,"ACS Agricultural Science & Technology","2026-09-20T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"系统综述吡啶类农药2020-2026年分子设计与生态安全进展，方法新颖、结论可靠，对绿色农药研发有参考价值，但属综述类论文，产业影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农药减量","生态安全","绿色农药","人工智能育种",[32,33],"吡啶类农药 分子设计","CMC P4VP 水凝胶 缓释","吡啶类农药分子设计-3057",0,"10.1021\u002Facsagscitech.6c00690",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":9,"card":54,"direction":60,"ingested_from":61},"W7213761670",[40,43,46,49,52],{"name":41,"orcid":42},"Hamdy Khamees Thabet","https:\u002F\u002Forcid.org\u002F0000-0001-8387-0404",{"name":44,"orcid":45},"Ahmed Abdou O. Abeed","https:\u002F\u002Forcid.org\u002F0000-0002-7284-4750",{"name":47,"orcid":48},"Mohamed R. Fouad","https:\u002F\u002Forcid.org\u002F0000-0002-4102-5111",{"name":50,"orcid":51},"Gameel A. M. Elhagali","https:\u002F\u002Forcid.org\u002F0000-0002-8053-9203",{"name":53,"orcid":9},"Mohamed S. A. El-Gaby",{"tldr":55,"method":56,"finding":57,"direction":58,"opportunity":59},"综述2020-2026年吡啶类农药从分子设计到生态安全的进展。","综述SAR优化、DFT建模、绿色制剂与SERS残留监测。","可精准设计高靶标活性且对蜜蜂低毒的吡啶农药，并实现可控释放。","农业绿色发展与碳","可探索AI驱动的吡啶分子设计及其对传粉昆虫的生态风险预测。","智慧农业 \u002F 农业物联网","openalex","2026-09-21T23:30:09.112694Z",{"total":64,"page":21,"page_size":64,"items":65},6,[66,104,168,203,234,263],{"id":67,"title":68,"url":69,"summary":70,"summary_zh":9,"content":9,"source_name":71,"source_url":69,"published_at":72,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":73,"score_detail":74,"sources":78,"tags":80,"search_phrases":84,"slug":87,"view_count":35,"doi":88,"paper":89,"created_at":103},2647,"Agricultural drone use, pesticide reduction, and biodiversity restoration: evidence from China","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10109-026-00522-6","Agricultural drone use, pesticide reduction, and biodiversity restoration: evidence from China。Journal of Geographical Systems","Journal of Geographical Systems","2026-09-15T00:00:00Z",77,{"impact":17,"substance":75,"depth":76,"authority":19,"freshness":20,"relevant":21,"comment":77},20,17,"基于中国证据的学术论文，探讨无人机施药与农药减量、生物多样性恢复的关系，方法新颖且结论具政策参考价值，但属细分领域研究，影响层级有限。",[79],{"name":71,"url":69},[26,81,82,27,83],"农业无人机","遥感","生物多样性",[85,86],"农业无人机 生物多样性 农药减量 智慧农业","农业无人机 生物多样性","农业无人机生物多样性农药减量智慧农业-2647","10.1007\u002Fs10109-026-00522-6",{"doi":88,"openalex_id":90,"authors":91,"venue":71,"cited_by_count":35,"oa_url":9,"card":9,"direction":60,"ingested_from":61},"W7213342483",[92,95,98,101],{"name":93,"orcid":94},"Jianjun Tang","https:\u002F\u002Forcid.org\u002F0000-0002-6994-2093",{"name":96,"orcid":97},"Jingru Chen","https:\u002F\u002Forcid.org\u002F0009-0009-5773-5482",{"name":99,"orcid":100},"Jingyang Yan","https:\u002F\u002Forcid.org\u002F0000-0001-8330-6272",{"name":102,"orcid":9},"Fei Li","2026-09-16T23:30:12.279104Z",{"id":105,"title":106,"url":107,"summary":108,"summary_zh":109,"content":9,"source_name":110,"source_url":107,"published_at":111,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":112,"score_detail":113,"sources":117,"tags":119,"search_phrases":123,"slug":126,"view_count":35,"doi":127,"paper":128,"created_at":167},1325,"Changing pest dynamics and pesticide use in cotton: Challenges and sustainable management","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.16195","Cotton (Gossypium spp.) is one of the world’s most important fibre crops, yet its sustainability is increasingly challenged by evolving pest complexes, intensified pesticide use, climate change and climate variability. The evidence indicates current knowledge on pest dynamics, pesticide dependence and ecological interactions in cotton agroecosystems, highlighting the shift from bollworm-dominated pest complexes to the increasing prevalence of secondary pests following the widespread adoption of Bacillus thuringiensis (Bt) cotton. Although Bt (Cry toxin)-based transgenic cotton technology initially reduced insecticide use against the bollworm complex, primarily lepidopteran species, the emergence of resistance among bollworms and the upsurge of certain sucking pests in recent decades have reinstated insecticide dependence for pest management. This has led to ecological imbalance, non-target toxicity, the decline of pollinator populations and natural enemy communities, biodiversity loss, environmental contamination, accumulation of residues in products and yield challenges, in addition to increased production costs. The review emphasises the critical role of integrated pest management (IPM) strategies that integrate biological control, habitat diversification, resistant cultivars and threshold-based pesticide applications to restore ecological stability. Alternative approaches such as botanical pesticides, nanoformulations and plant volatile-mediated pest regulation are highlighted as promising tools for sustainable pest control. Advances in precision agriculture, including artificial intelligence (AI), machine learning and unmanned aerial vehicle (UAV)-based pesticide delivery systems, have the potential to improve pest surveillance and reduce chemical inputs. Supply-chain procurement, certification, traceability and consumer demand can encourage lower-pesticide production, although these mechanisms require fair price incentives to avoid transferring compliance costs to smallholders. Sustainable cotton therefore requires region-specific, climate-resilient, evidence-based IPM supported by extension, market incentives, resistance monitoring and coordinated policy.","棉花（Gossypium spp.）是全球最重要的纤维作物之一，然而其可持续性正日益受到害虫群落演变、农药使用强度增加、气候变化及气候变率等因素的挑战。现有证据表明，当前对棉花农业生态系统中害虫动态、农药依赖及生态互作的认识，凸显了自苏云金芽孢杆菌（Bacillus thuringiensis, Bt）棉花广泛推广以来，害虫群落从以棉铃虫为主向次生害虫日益增多的转变。尽管基于Bt（Cry毒素）的转基因棉花技术最初减少了对棉铃虫复合体（主要为鳞翅目害虫）的杀虫剂使用，但近几十年来棉铃虫抗药性的出现及某些刺吸式害虫的激增，重新导致害虫管理对杀虫剂的依赖。这引发了生态失衡、非靶标毒性、传粉昆虫种群及天敌群落减少、生物多样性丧失、环境污染、产品中残留物积累及产量挑战，同时增加了生产成本。本综述强调了综合害虫管理（IPM）策略的关键作用，该策略整合生物防治、生境多样化、抗性品种及基于阈值的农药施用，以恢复生态稳定性。植物源农药、纳米制剂及植物挥发物介导的害虫调控等替代方法，被强调为可持续害虫防治的有前景工具。精准农业的进展，包括人工智能（AI）、机器学习及基于无人机（UAV）的农药施用系统，有望改善害虫监测并减少化学投入。供应链采购、认证、可追溯性及消费者需求可促进低农药生产，但这些机制需辅以公平价格激励，以避免将合规成本转嫁给小农户。因此，可持续棉花生产需要区域特异性、气候韧性、基于证据的IPM，并辅以推广服务、市场激励、抗性监测及协调政策支持。","Plant Science Today","2026-08-31T00:00:00Z",72,{"impact":17,"substance":18,"depth":17,"authority":114,"freshness":115,"relevant":21,"comment":116},12,2,"综述性论文，系统分析棉花害虫动态与农药依赖问题，提出IPM与精准农业等可持续路径，对农业绿色发展有参考价值。",[118],{"name":110,"url":107},[26,120,121,27,122],"农业人工智能","棉花","病虫害防治",[124,125],"农业人工智能 病虫害防治 农药减量 智慧农业","农业人工智能 病虫害防治","农业人工智能病虫害防治农药减量智慧农业-1325","10.14719\u002Fpst.16195",{"doi":127,"openalex_id":129,"authors":130,"venue":110,"cited_by_count":35,"oa_url":107,"card":161,"direction":166,"ingested_from":61},"W7204806269",[131,134,137,140,143,146,149,152,155,158],{"name":132,"orcid":133},"V Veeragowtham","https:\u002F\u002Forcid.org\u002F0009-0002-9822-4901",{"name":135,"orcid":136},"A. Suganthi","https:\u002F\u002Forcid.org\u002F0000-0001-7013-4459",{"name":138,"orcid":139},"M. Murugan","https:\u002F\u002Forcid.org\u002F0000-0002-7485-1153",{"name":141,"orcid":142},"P. Renukadevi","https:\u002F\u002Forcid.org\u002F0000-0001-9665-1681",{"name":144,"orcid":145},"S. Vellaikumar","https:\u002F\u002Forcid.org\u002F0000-0001-9277-457X",{"name":147,"orcid":148},"Ali Iqra","https:\u002F\u002Forcid.org\u002F0009-0008-6095-9471",{"name":150,"orcid":151},"J Kousika","https:\u002F\u002Forcid.org\u002F0000-0002-2455-5268",{"name":153,"orcid":154},"Sheela Venugopal","https:\u002F\u002Forcid.org\u002F0000-0002-4689-4105",{"name":156,"orcid":157},"Prakash Kolanchi","https:\u002F\u002Forcid.org\u002F0000-0003-1244-199X",{"name":159,"orcid":160},"Murugesh Dharani","https:\u002F\u002Forcid.org\u002F0009-0004-6705-1344",{"tldr":162,"method":163,"finding":164,"direction":58,"opportunity":165},"综述棉花害虫动态与农药使用变化，提出可持续管理策略。","综述文献，分析Bt棉影响、害虫抗性及IPM、精准农业等技术。","Bt棉导致次生害虫上升和抗性，需综合IPM与精准农业减少农药。","可研究AI与无人机在棉花害虫监测与精准施药中的实际应用效果及小农采纳机制。","农业人工智能与决策模型","2026-09-01T23:30:34.470501Z",{"id":169,"title":170,"url":171,"summary":172,"summary_zh":9,"content":9,"source_name":173,"source_url":9,"published_at":174,"category":12,"cover_url":9,"hotness":13,"is_selected":175,"score":176,"score_detail":177,"sources":183,"tags":185,"search_phrases":190,"slug":193,"view_count":35,"doi":9,"paper":194,"created_at":202},3125,"Full-Season Agentic Farm System FAIRY: Event-Driven Multi-Agent Orchestration for Soybean Production（FAIRY 全季节智能体农场系统：大豆生产的事件驱动多智能体编排）","https:\u002F\u002Faiagentstore.ai\u002Fai-agent-news\u002Ftopic\u002Fagriculture-food\u002F2026-08-11","哈尔滨工业大学研究人员发布并部署全栈、事件驱动的智能体引擎 FAIRY 于中国运行中的大豆研究农场。FAIRY 集成传感器、无人机、卫星植被产品、机械 API、作物过程模型与多智能体编排层，执行起垄→播种→灌溉→病虫害防治→收获→干燥→存储工作流，并在 64 垄研究场上跨 100 个全季节场景评估 9 个智能体控制器。这是智能体系统能够在大农业时间尺度和延迟结果下进行推理的最清晰演示之一，将农业中的智能体工作从实验室演示推进到全过程评估。同期 arXiv 推出 HarvestBench 基准将 LLM 驱动智能体置于农场网格世界（拖拉机面临动物选择绕行或碾压），结果显示模型差异巨大、对道德简报高度敏感、避免意愿具有可衡量的价格弹性。","Harbin Institute of Technology \u002F arXiv","2026-09-18T00:00:00Z",true,89,{"impact":178,"substance":179,"depth":180,"authority":19,"freshness":181,"relevant":21,"comment":182},24,23,19,9,"哈工大在真实大豆农场部署全季节事件驱动多智能体系统并配套 HarvestBench 基准，是农业智能体从演示走向全过程评估的标志性进展，专业深度与信息增量俱佳。",[184],{"name":173,"url":171},[26,186,120,187,188,189],"无人农场","农业遥感","多智能体","大豆生产",[191,192],"哈工大 FAIRY 大豆","HarvestBench 智能体 农场","哈工大FAIRY大豆-3125",{"doi":9,"openalex_id":9,"authors":195,"venue":9,"cited_by_count":35,"oa_url":9,"card":196,"direction":166,"ingested_from":201},[],{"tldr":197,"method":198,"finding":199,"direction":166,"opportunity":200},"部署全季节事件驱动多智能体系统FAIRY，在大豆农场完成从起垄到存储的全流程评估。","集成传感器、无人机、卫星、作物模型与机械API，用9个智能体控制器在64垄100","智能体系统能在大农业时间尺度下推理，模型差异大且对道德简报敏感。","可研究多智能体在长周期、延迟反馈农业任务中的鲁棒性与伦理约束机制。","agent","2026-09-22T00:05:38.611717Z",{"id":204,"title":205,"url":206,"summary":207,"summary_zh":9,"content":9,"source_name":208,"source_url":9,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":209,"score_detail":210,"sources":214,"tags":216,"search_phrases":221,"slug":224,"view_count":35,"doi":9,"paper":225,"created_at":233},3122,"Advances in Information Sensing and Intelligent Monitoring of Field Crops Throughout the Full Growth Cycle（大田作物全生育期信息感知与智能监测研究进展）","https:\u002F\u002Fwww.mdpi.com\u002F2073-4395\u002F16\u002F18\u002F1852","江苏大学农业工程学院 Tang Ruifan 等在《Agronomy》16(18): 1852 发表综述（2026-09-20 发表）：大田作物在不同生育阶段持续变化、呈现显著空间异质性、需在短作业窗口内进行管理。研究以生育阶段为主线组织文献，通过\"农业需求—可观测变量—感知平台—数据处理方法—验证设计—状态解释—管理或装备输出\"通用链条分析。从卫星遥感、无人机感知、地面与近端感知、田间物联网、机载传感器、多源融合、作物模型与机器学习方法按空间支撑、时间连续性、尺度匹配、田间稳健性、迁移条件、不确定性与操作适用性比较。综述报告作物表型反演、田间环境表征、生物胁迫识别在特定条件下已建立；跨阶段状态继承、一致参考测量、独立验证、监测结果向可执行任务转化仍不充分。提出生命周期导向的信息处理视角，未来应加强跨作物跨区域验证、机理性与数据驱动模型协同、不确定性报告、互操作性和田间反馈。","MDPI Agronomy",76,{"impact":211,"substance":75,"depth":17,"authority":212,"freshness":181,"relevant":21,"comment":213},16,13,"江苏大学团队在核心期刊发表的综述，系统梳理大田作物全生育期感知与监测技术链条，专业深度与信息增量较高，但属学术综述、产业影响有限，适合进入主题聚合而非头条精选。",[215],{"name":208,"url":206},[26,217,218,219,220],"农业物联网","作物表型","遥感监测","大田作物",[222,223],"江苏大学 大田作物 智能监测","Agronomy 作物全生育期 信息感知","江苏大学大田作物智能监测-3122",{"doi":9,"openalex_id":9,"authors":226,"venue":9,"cited_by_count":35,"oa_url":9,"card":227,"direction":231,"ingested_from":201},[],{"tldr":228,"method":229,"finding":230,"direction":231,"opportunity":232},"综述大田作物全生育期信息感知与智能监测，按生育阶段梳理技术并指出转化不足。","以生育阶段为主线，比较卫星、无人机、地面物联网、模型与机器学习等方法。","表型反演与胁迫识别已有条件建立，但跨阶段继承、独立验证与可执行转化不足。","农业遥感与作物表型","可研究跨生育阶段状态继承建模、一致参考测量与监测结果向田间作业指令的转化。","2026-09-22T00:05:38.276747Z",{"id":235,"title":236,"url":237,"summary":238,"summary_zh":9,"content":9,"source_name":239,"source_url":9,"published_at":240,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":241,"score_detail":242,"sources":245,"tags":247,"search_phrases":251,"slug":254,"view_count":35,"doi":9,"paper":255,"created_at":262},3121,"Optimization of Farmland Management Zoning in the Black Soil Region: A Climate Adaptability Assessment Considering Crop Growth Response and Topographic Characteristics（黑土区农田管理分区优化：考虑作物生长响应与地形特征的气候适应性评估）","https:\u002F\u002Fwww.mdpi.com\u002F2072-4292\u002F18\u002F18\u002F3260","吉林农业大学 Han Yongqi 等联合中科院东北地理与农业生态研究所、东北农业大学在《Remote Sensing》18(18): 3260 发表论文（2026-09-21 发表）。针对精准农业管理分区对单日期影像依赖难以捕捉年际作物环境变化问题，研究评估 29 个特征组合（融合 Sentinel-2 多光谱、PCA、NDVI 和 DEM 数据）在黑土区友谊农场干旱、湿润和融合场景下的气候适应性。实施异构空间注意力网络（HSAN）和 K-means 聚类，以变异系数（CV）评估稳定性与适应性。结果显示 HSAN 在多源融合下优于 K-means，CV 分别为 11.303-14.774% 与 14.823-16.011%；多期 NDVI 数据是主导因素，相对 CV 减少 34.850-53.701%；DEM 贡献有限；PCA 增强稳定性；多期融合在极端气候年份提升分区生态一致性与适用性。","MDPI Remote Sensing","2026-09-21T00:00:00Z",79,{"impact":243,"substance":18,"depth":17,"authority":19,"freshness":13,"relevant":21,"comment":244},15,"黑土区精准农业管理分区研究，方法新颖、数据扎实，对农业遥感应用有参考价值。",[246],{"name":239,"url":237},[26,248,249,82,250],"精准农业","黑土区","管理分区",[252,253],"黑土区 管理分区 遥感","Sentinel-2 黑土区 气候适应性","黑土区管理分区遥感-3121",{"doi":9,"openalex_id":9,"authors":256,"venue":9,"cited_by_count":35,"oa_url":9,"card":257,"direction":231,"ingested_from":201},[],{"tldr":258,"method":259,"finding":260,"direction":231,"opportunity":261},"评估黑土区多源遥感特征组合在干旱湿润场景下的农田管理分区气候适应性。","融合Sentinel-2多光谱、NDVI、PCA与DEM，用HSAN和K-mea","HSAN优于K-means，多期NDVI主导稳定性提升，DEM贡献有限，PCA增强稳定性。","可探索多期时序特征与深度聚类在极端气候下的跨区域迁移及分区决策落地。","2026-09-22T00:05:38.189091Z",{"id":264,"title":265,"url":266,"summary":267,"summary_zh":9,"content":9,"source_name":268,"source_url":9,"published_at":174,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":269,"score_detail":270,"sources":273,"tags":275,"search_phrases":279,"slug":282,"view_count":35,"doi":9,"paper":283,"created_at":290},3120,"Autonomous Agricultural Machinery for Smart Agriculture: An Integrated Framework of Observation, Heterogeneity, and Intelligent Infrastructure（智慧农业自主农机：观测、异质性与智能基础设施一体化框架）","https:\u002F\u002Fwww.jstage.jst.go.jp\u002Farticle\u002Frdj\u002F5\u002F0\u002F5_576\u002F_article\u002F-char\u002Fen","Hongjin Li & Chunjiang Gao 在 Journal of Agricultural Information 5: 576-596 发表综述文章（J-STAGE 平台 2026-09-18 上线）：针对农业 4.0 时代自主农机核心问题——如何在复杂动态田间条件下将异构环境信息转化为可靠自适应可扩展的机器自主性，系统综述自主农机的技术基础、应用领域、性能优势与采用约束，并构建围绕\"观测、异质性、基础设施\"的一体化框架。研究不再按机器类型分类技术，而是考察多模态感知、AI 决策、自主导航控制、精准执行、多机协同之间的耦合关系，特别关注环境不确定性、实时决策、互操作性和系统级可扩展性的挑战。识别从孤立任务自动化向数据驱动自适应网络化农业自主性的渐进过渡，强调个体组件改进未必带来系统级性能提升，除非具备兼容的计算、通信、机械和制度基础设施。","J-STAGE \u002F Research Disclosure Journal",78,{"impact":17,"substance":271,"depth":76,"authority":212,"freshness":181,"relevant":21,"comment":272},21,"核心期刊综述，提出观测-异质性-基础设施一体化框架，对智慧农业自主农机研究有参考价值，但属学术综述、产业影响有限。",[274],{"name":268,"url":266},[26,120,276,277,278],"多模态感知","自主农机","农业4.0",[280,281],"自主农机 智慧农业 一体化框架","Hongjin Li Chunjiang Gao 自主农机","自主农机智慧农业一体化框架-3120",{"doi":9,"openalex_id":9,"authors":284,"venue":9,"cited_by_count":35,"oa_url":9,"card":285,"direction":60,"ingested_from":201},[],{"tldr":286,"method":287,"finding":288,"direction":60,"opportunity":289},"综述自主农机技术，提出观测、异质性、基础设施一体化框架。","系统综述多模态感知、AI决策、导航控制与多机协同的耦合关系。","组件改进未必提升系统性能，需兼容的计算、通信、机械与制度基础设施。","可研究异构农机互操作协议与边缘计算架构，填补系统级可扩展性验证空白。","2026-09-22T00:05:38.042546Z"]