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This review synthesizes recent developments in the field using a systematic critical review approach. It proposes a systems-level framework for analyzing smart agriculture across robotic platforms, sensing and digital infrastructure, AI methods, public datasets and benchmarks, evaluation metrics, application domains, and deployment maturity. The review covers ground robots, UAVs, harvesting manipulators, multi-robot systems, RGB and spectral sensing, LiDAR, IoT networks, edge\u002Fcloud platforms, AI-based perception and decision-making, and multimodal vision–language models. The analysis shows that the literature has made clear progress in prototypes, datasets, and task-specific performance, but several gaps still limit practical deployment. These include weak cross-season and cross-region validation, fragmented benchmarking practices, limited deployment-oriented metrics, insufficient techno-economic reporting, and unresolved challenges related to interoperability and human–robot interaction. Public datasets and emerging multimodal benchmarks show that the field is moving toward greater standardization; however, strong benchmark performance does not yet consistently translate into reliable field operation. The review concludes by outlining future research directions in benchmark realism, field-robust AI, edge deployment, digital twins, privacy-preserving learning, human-centered autonomy, and long-term agronomic and economic 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智慧农业","自主农业机械集成框架-3653",{"doi":9,"openalex_id":9,"authors":288,"venue":9,"cited_by_count":74,"oa_url":9,"card":289,"direction":172,"ingested_from":294},[],{"tldr":290,"method":291,"finding":292,"direction":172,"opportunity":293},"综述自主农业机械，提出观测-异质性-基础设施三位一体集成框架。","文献综述，围绕多模态感知、AI决策、自主导航与多机协同分析。","单组件改进若无计算、通信与制度基础设施配套，难带来系统级性能提升。","可研究异构农机互操作协议与边缘计算协同，量化基础设施配套对系统级性能的增益。","agent","2026-09-28T00:03:02.555619Z",{"id":297,"title":298,"url":299,"summary":300,"summary_zh":9,"content":9,"source_name":301,"source_url":9,"published_at":302,"category":12,"cover_url":9,"hotness":13,"is_selected":54,"score":303,"score_detail":304,"sources":309,"tags":311,"primary_topic":313,"secondary_topics":317,"classification":318,"search_phrases":341,"slug":344,"view_count":84,"doi":9,"paper":345,"created_at":352},3252,"具身智能农业机器人关键技术与发展趋势——上海大学苗中华等\"感知-决策-模拟-进化-诊断\"五位一体研究框架","http:\u002F\u002Fwww.qikanzj.com\u002Fhek\u002Fnyjxxb\u002Fmulu\u002F455891.html","智慧农业(中英文)期刊发表上海大学苗中华、朱子煜、张伟、薛振锋、孙腾、张异凡、谢涛、何创新、李楠，山东农业大学苑进，北京市农林科学院赵春江，上海交通大学刘成良团队论文：全球农业智能化转型背景下，传统农业机器人面临非结构化环境、作业对象不确定性及控制参数时变等技术瓶颈，导致农业机器人实用效果差，产业化落地困难。具身智能作为实现通用人工智能的关键路径，为突破上述瓶颈提供了新范式。本文系统阐述具身智能理论驱动的农业机器人技术体系与发展路径，创新性构建\"感知-决策-模拟-进化-诊断\"五位一体研究框架。具身感知聚焦开放环境场景理解、作业目标主动感知及多模态融合感知；具身决策涵盖端到端导航、大模型驱动的机械臂操作及多机协同算法；具身模拟着力于高保真场景重建、生成及虚实迁移技术；具身进化整合无监督学习、强化学习及模仿学习机制；具身诊断构建作业状态监测与质量评估体系。","智慧农业(中英文)·上海大学","2026-09-22T00:00:00Z",91,{"impact":305,"substance":306,"depth":97,"authority":307,"freshness":13,"relevant":21,"comment":308},24,23,15,"核心期刊论文，提出具身智能驱动的农业机器人五位一体框架，方法体系新颖、团队权威，对智慧农业技术路线有较强参考价值。",[310],{"name":301,"url":299},[26,27,104,312,28],"具身智能",{"code":145,"slug":314,"name":315,"path":316,"parent_code":264},"agricultural-robots","机器人与无人农场","\u002Ftopic\u002Finputs-equipment\u002Fagricultural-robots",[],{"status":114,"locked":14,"version":34,"method":35,"evidence":319},{"candidates":320,"agricultural_evidence":54},[321,325,331,335],{"code":145,"score":183,"supported":54,"title":322,"abstract":323,"tags":324},[104],[104],[104],{"code":39,"score":326,"supported":54,"title":327,"abstract":328,"tags":330},5,[],[329],"大模型",[27,28],{"code":50,"score":60,"supported":54,"title":332,"abstract":333,"tags":334},[],[26],[26],{"code":336,"score":139,"supported":54,"title":337,"abstract":338,"tags":340},"J3",[],[339],"全球农业",[],[342,343],"上海大学 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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":19,"substance":361,"depth":184,"authority":98,"freshness":20,"relevant":21,"comment":362},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。",[364],{"name":358,"url":356},[26,27,28,257,366],"农业4.0",{"code":260,"slug":261,"name":262,"path":263,"parent_code":264},[],{"status":114,"locked":14,"version":34,"method":35,"evidence":370},{"candidates":371,"agricultural_evidence":54},[372,378,382],{"code":260,"score":97,"supported":54,"title":373,"abstract":376,"tags":377},[374,375],"农机","agricultural machinery",[374],[],{"code":50,"score":184,"supported":54,"title":379,"abstract":380,"tags":381},[26,119],[],[26],{"code":39,"score":40,"supported":14,"title":383,"abstract":384,"tags":385},[],[],[27,28],[387,388],"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":391,"venue":9,"cited_by_count":74,"oa_url":9,"card":392,"direction":172,"ingested_from":294},[],{"tldr":393,"method":394,"finding":395,"direction":172,"opportunity":396},"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",{"id":399,"title":400,"url":401,"summary":402,"summary_zh":403,"content":9,"source_name":404,"source_url":401,"published_at":250,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":405,"score_detail":406,"sources":408,"tags":410,"primary_topic":413,"secondary_topics":414,"classification":415,"search_phrases":434,"slug":437,"view_count":139,"doi":438,"paper":439,"created_at":459},2913,"A quantum-inspired multi-objective learning framework for real-time sustainable aquaculture water quality prediction","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1827991","Management of sustainable aquaculture necessitates accurate prediction of water quality parameters, since environmental variability is an important factor in determining aquatic productivity and ecological stability. Traditional machine learning systems are usually limited by inefficient parameter optimization, feature overlap, and limited adaptability to non-linear and time-varying environmental trends. To address these issues, a new concept is proposed, which is called Quantum-Inspired Aquaculture Optimization Network (Q-AQUAOptNet). To improve the process of feature selection, hyperparameter optimization, and model stability before temporal prediction, the proposed architecture combines sustainability index modeling and quantum-inspired multi-objective evolutionary optimization. The optimization approach balances exploration and exploitation, enhancing the learning ability of the prediction model. Implementation of the framework was done through Python-based simulation tools in preprocessing, optimization, and performance evaluation. The created system had a Root Mean Square Error of 0.24, a Mean Absolute Error of 0.21, and an R 2 of 0.97, which means that it has effective predictive power and a great ability to explain the variance. Integrating evolutionary optimization and temporal learning improves generalization performance and reduces prediction uncertainty. The findings demonstrate that Q-AQUAOptNet provides effective predictive performance within a simulation-based framework and shows potential as a sustainability-oriented intelligent water-quality monitoring and decision support system for aquaculture applications.","可持续水产养殖管理需要准确预测水质参数，因为环境变异性是决定水生生产力和生态稳定性的重要因素。传统机器学习系统通常受限于参数优化效率低、特征重叠以及对非线性和时变环境趋势的适应能力有限。为解决这些问题，提出了一种新概念，称为量子启发水产养殖优化网络（Quantum-Inspired Aquaculture Optimization Network，Q-AQUAOptNet）。为在时间预测之前改进特征选择、超参数优化和模型稳定性，所提出的架构结合了可持续性指数建模和量子启发多目标进化优化。该优化方法平衡了探索与利用，增强了预测模型的学习能力。该框架的实现通过基于Python的仿真工具完成，涵盖预处理、优化和性能评估。所构建系统的均方根误差为0.24，平均绝对误差为0.21，R²为0.97，这意味着其具有有效的预测能力和较强的方差解释能力。将进化优化与时间学习相结合，提高了泛化性能并降低了预测不确定性。研究结果表明，Q-AQUAOptNet在基于仿真的框架内提供了有效的预测性能，并显示出作为面向可持续性的智能水质监测与决策支持系统应用于水产养殖的潜力。","Frontiers in Sustainable Food Systems",71,{"impact":183,"substance":18,"depth":184,"authority":98,"freshness":20,"relevant":21,"comment":407},"提出量子启发多目标优化网络用于水产养殖水质预测，仿真指标较优，属智慧渔业细分领域的方法学进展，但尚处仿真阶段、缺乏真实场景验证。",[409],{"name":404,"url":401},[26,27,29,411,412],"多目标优化","水质预测",{"code":39,"slug":108,"name":109,"path":110,"parent_code":111},[],{"status":114,"locked":14,"version":34,"method":35,"evidence":416},{"candidates":417,"agricultural_evidence":54},[418,424,430],{"code":45,"score":307,"supported":54,"title":419,"abstract":421,"tags":423},[420],"aquaculture",[422,420],"水产",[29],{"code":39,"score":13,"supported":54,"title":425,"abstract":426,"tags":429},[],[427,428,128],"机器学习","machine learning",[27],{"code":50,"score":21,"supported":14,"title":431,"abstract":432,"tags":433},[],[],[26],[435,436],"Q-AQUAOptNet 水产养殖 水质预测","农业人工智能 多目标优化 智慧农业 水产养殖","Q-AQUAOptNet水产养殖水质预测-2913","10.3389\u002Ffsufs.2026.1827991",{"doi":438,"openalex_id":440,"authors":441,"venue":404,"cited_by_count":74,"oa_url":401,"card":454,"direction":172,"ingested_from":81},"W7213532503",[442,445,448,450,452],{"name":443,"orcid":444},"Abdel‐Haleem Abdel‐Aty","https:\u002F\u002Forcid.org\u002F0000-0002-6763-2569",{"name":446,"orcid":447},"Ali Jaber Almalki","https:\u002F\u002Forcid.org\u002F0009-0004-1359-2612",{"name":449,"orcid":9},"Sara A. Ghorashi",{"name":451,"orcid":9},"Betty Wan Niu Voon",{"name":453,"orcid":9},"Mohamed Hafez",{"tldr":455,"method":456,"finding":457,"direction":172,"opportunity":458},"提出量子启发多目标优化网络Q-AQUAOptNet，实现水产养殖水质实时预测。","量子启发多目标进化优化结合时间预测与可持续指数建模，Python仿真。","RMSE 0.24、MAE 0.21、R² 0.97，预测精度高且泛化好。","可探索真实养殖场部署与多源传感器融合，验证量子启发优化在边缘端的实时性。","2026-09-19T23:30:07.985707Z"]