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Crop water requirements vary with crop species, growth stage, soil properties, evapotranspiration, weather, and plant water status; therefore, irrigation scheduling based on a single parameter is insufficient. Conventional water management systems often rely on uniform application, fixed scheduling, and limited real-time information, which overlooks spatial and temporal variation in soil, crop, and weather conditions and can result in water loss, over-irrigation, and under-irrigation. This review highlights studies on the integration of AI-driven sensing and imaging technologies, IoT-based precision irrigation, variable-rate technologies, digital twins, and agricultural robotics for crop water management. These approaches integrate field, crop, weather, and spatial information to support water-demand assessment, crop-stress monitoring, irrigation scheduling, site-specific application, and automated water management. The reviewed studies report water savings, improved water-use efficiency, crop performance, and more site-specific irrigation and input application across different crops and irrigation systems. The review also highlights the limitations of these systems, including sensing reliability, model transferability, connectivity, interoperability, cybersecurity, investment costs, and environmental effects. Overall, the integration of these technologies provides potential for timely and field-specific irrigation decisions and supports efficient and climate-resilient water management in agriculture.","农业灌溉系统会因径流、蒸发和深层渗漏而承受严重的水量损失。作物需水量随作物种类、生长阶段、土壤性质、蒸散量、天气和植株水分状况而变化；因此，仅基于单一参数的灌溉调度是不够的。传统的水管理系统往往依赖均匀灌溉、固定调度和有限的实时信息，忽视了土壤、作物和天气条件的时空变异，可能导致水分损失、过度灌溉和灌溉不足。本文综述了将人工智能驱动的传感与成像技术、基于物联网的精准灌溉、变量灌溉技术、数字孪生和农业机器人集成应用于作物水分管理的研究。这些方法整合了田块、作物、天气和空间信息，以支持需水评估、作物胁迫监测、灌溉调度、定点变量灌溉和自动化水分管理。所综述的研究报告了在不同作物和灌溉系统中实现的节水、水分利用效率提升、作物表现改善以及更具田块针对性的灌溉和投入品施用。本文还指出了这些系统的局限性，包括传感可靠性、模型可迁移性、连通性、互操作性、网络安全、投资成本和环境影响。总体而言，这些技术的集成可为及时且针对具体田块的灌溉决策提供可能，并支持农业中高效且具有气候韧性的水分管理。","Frontiers in Water","2026-10-12T00:00:00Z","论文",false,78,{"impact":361,"substance":508,"depth":361,"authority":584,"freshness":585,"relevant":455,"comment":586},13,9,"系统综述AI传感、物联网精准灌溉与农业机器人集成，方法视角新、结论可靠，对智慧农业水资源管理有参考价值。",[588],{"name":578,"url":575},[590,591,592,593,594],"智慧农业","农业人工智能","农业机器人","数字孪生","精准灌溉",{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},[],{"status":598,"locked":581,"version":4,"method":599,"evidence":600},"classified","rules",{"candidates":601,"agricultural_evidence":603},[602,609,615,621,625,629],{"code":152,"score":361,"supported":603,"title":604,"abstract":605,"tags":608},true,[153],[606,153,607],"灌溉","evapotranspiration",[594],{"code":144,"score":610,"supported":603,"title":611,"abstract":612,"tags":614},6,[],[613,145],"土壤",[],{"code":48,"score":610,"supported":603,"title":616,"abstract":617,"tags":620},[],[618,619],"物联网","IoT",[],{"code":16,"score":408,"supported":603,"title":622,"abstract":623,"tags":624},[],[593],[593,590],{"code":120,"score":400,"supported":603,"title":626,"abstract":627,"tags":628},[],[592],[592],{"code":24,"score":455,"supported":581,"title":630,"abstract":631,"tags":632},[],[],[591],[634,635],"精准灌溉 传感器 人工智能","农业机器人 水资源管理","精准灌溉传感器人工智能-4810","10.3389\u002Ffrwa.2026.1973897",{"doi":637,"openalex_id":639,"authors":640,"venue":578,"cited_by_count":424,"oa_url":649,"card":650,"direction":654,"ingested_from":656},"W7221563526",[641,644,646],{"name":642,"orcid":643},"Sachin Ghanghas","https:\u002F\u002Forcid.org\u002F0000-0001-9231-2527",{"name":645,"orcid":11},"Naveen Sihag",{"name":647,"orcid":648},"Parveen Dhanger","https:\u002F\u002Forcid.org\u002F0000-0001-5250-0275","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fwater\u002Farticles\u002F10.3389\u002Ffrwa.2026.1973897\u002Fpdf",{"tldr":651,"method":652,"finding":653,"direction":654,"opportunity":655},"综述AI传感、成像、物联网精准灌溉、数字孪生与机器人集成用于作物水分管理。","综述融合AI传感成像、IoT精准灌溉、变量技术、数字孪生与农业机器人。","集成方案可节水、提升水分利用效率与作物表现，但存在传感可靠性、模型迁移等局限。","智慧农业 \u002F 农业物联网","可研究多源传感与数字孪生融合的跨区域模型迁移及低成本、安全互操作的灌溉决策系统。","openalex","2026-10-13T23:30:10.375774Z",{"id":659,"title":660,"url":661,"summary":662,"summary_zh":663,"content":11,"source_name":664,"source_url":661,"published_at":665,"category":580,"cover_url":11,"hotness":385,"is_selected":581,"score":666,"score_detail":667,"sources":671,"tags":673,"primary_topic":677,"secondary_topics":678,"classification":680,"search_phrases":714,"slug":717,"view_count":69,"doi":718,"paper":719,"created_at":733},4561,"Smart Agriculture and Environmental Monitoring: An Edge-AI, IoT, and Federated Learning Framework for Climate-Resilient Farming","https:\u002F\u002Fdoi.org\u002F10.11648\u002Fj.ajese.20261004.11","This study presents a system architecture, algorithm, and evaluation protocol for smart agriculture and environmental monitoring. It is a method\u002Farchitecture contribution and does not report field trial results. World food production is still under stress from climate variability, the availability of freshwater resources, and the cost of farm inputs, offering an incentive to transition to data-driven and sensor-based farm management practices. Internet of Things (IoT)-based agricultural monitoring is well developed, but most systems deployed focus on a single parameter (e.g., soil moisture) or the parameter is inferred solely in the cloud, adding latency and fragility to the deployment of systems in rural areas. In this study, we propose an integrated architecture for low-power multiparameter environmental sensing, LoRaWAN\u002FNB-IoT connectivity, edge-based lightweight inference, and privacy-preserving federated learning with cross-farm model aggregation, which is further complemented by a digital twin layer for scenario simulation. The decision layer of the framework can combine data from ground sensors with UAV and satellite images for the multi-scale monitoring of crop health and environmental quality (air, water, and GHG proxies). We contribute the full system design, an algorithmic pipeline, a sensor and model configuration specification, and a pre-registered evaluation protocol intended to guide future field deployment; no field data have yet been collected and clearly labelled non-empirical templates showing the expected reporting format only, rather than measured outcomes. The architecture and protocol are instead grounded in, and cross-checked against, published state-of-the-art results, which report an accuracy range of 82–97% for federated crop-yield prediction and 20–30% water savings from sensor-controlled irrigation.","本研究提出了一套面向智慧农业与环境监测的系统架构、算法与评估协议。这是一项方法\u002F架构层面的贡献，不报告田间试验结果。世界粮食生产仍面临气候变率、淡水资源可得性以及农业投入成本带来的压力，这促使农业管理实践向数据驱动和基于传感器的方向转型。基于物联网（IoT）的农业监测已较为成熟，但大多数已部署系统仅关注单一参数（如土壤水分），或仅在云端推断参数，这增加了农村地区系统部署的延迟和脆弱性。在本研究中，我们提出了一种集成架构，涵盖低功耗多参数环境感知、LoRaWAN\u002FNB-IoT连接、基于边缘的轻量级推理，以及具有跨农场模型聚合的隐私保护联邦学习，并进一步辅以用于情景模拟的数字孪生层。该框架的决策层可将地面传感器数据与无人机和卫星图像相结合，用于作物健康和环境质量（空气、水和温室气体代理指标）的多尺度监测。我们贡献了完整的系统设计、算法流程、传感器与模型配置规范，以及一份预注册的评估协议，旨在指导未来的田间部署；目前尚未收集田间数据，并明确标注了非经验性模板，仅展示预期报告格式，而非实测结果。该架构和协议以已发表的最先进成果为依据并与之交叉验证，这些成果报告联邦作物产量预测的准确率范围为82–97%，传感器控制灌溉可节水20–30%。","American Journal of Environmental Science and Engineering","2026-10-09T00:00:00Z",66,{"impact":416,"substance":668,"depth":669,"authority":584,"freshness":173,"relevant":455,"comment":670},16,17,"提出边缘AI+IoT+联邦学习的抗气候风险农业监测架构与评估协议，方法设计完整但尚无田间实测数据，属有参考价值的预研性论文。",[672],{"name":664,"url":661},[590,674,675,593,676],"农业物联网","边缘计算","联邦学习",{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},[679],{"code":152,"slug":153,"name":154,"path":155,"parent_code":128},{"status":598,"locked":581,"version":4,"method":599,"evidence":681},{"candidates":682,"agricultural_evidence":603},[683,690,695,700,704,710],{"code":16,"score":684,"supported":603,"title":685,"abstract":687,"tags":689},19,[686],"smart agriculture",[590,593,686,688],"digital twin",[593,590],{"code":48,"score":684,"supported":603,"title":691,"abstract":692,"tags":694},[619],[618,693,619],"internet of things",[674,675],{"code":152,"score":585,"supported":603,"title":696,"abstract":697,"tags":699},[],[606,698,153],"水资源",[],{"code":144,"score":610,"supported":603,"title":701,"abstract":702,"tags":703},[],[613,145],[],{"code":324,"score":705,"supported":603,"title":706,"abstract":707,"tags":709},3,[],[708],"温室",[],{"code":24,"score":455,"supported":581,"title":711,"abstract":712,"tags":713},[],[],[676],[715,716],"LoRaWAN NB-IoT 农业监测","联邦学习 作物产量预测","LoRaWANNB-IoT农业监测-4561","10.11648\u002Fj.ajese.20261004.11",{"doi":718,"openalex_id":720,"authors":721,"venue":664,"cited_by_count":424,"oa_url":661,"card":728,"direction":654,"ingested_from":656},"W7221105407",[722,725],{"name":723,"orcid":724},"Zarin Progga","https:\u002F\u002Forcid.org\u002F0009-0004-2311-1210",{"name":726,"orcid":727},"Mohammad Ali","https:\u002F\u002Forcid.org\u002F0009-0009-3725-8535",{"tldr":729,"method":730,"finding":731,"direction":654,"opportunity":732},"提出边缘AI、物联网与联邦学习集成的智慧农业环境监测架构与评估协议，未开展实地试验。","LoRaWAN\u002FNB-IoT多参数传感、边缘轻量推理、跨农场联邦学习、数字孪生仿","架构基于已发表结果交叉验证，联邦产量预测精度82-97%，传感器灌溉节水20-30%。","该框架仅停留在设计阶段，未来可开展真实农田部署，验证边缘联邦学习在异构农场环境下的精度与通信开销。","2026-10-10T23:30:07.789940Z",{"technologies":735,"keywords":770},[736,738,741,744,746,748,749,751,754,756,757,759,760,761,763,765,767,769],{"name":591,"count":737},38,{"name":739,"count":740},"遥感",24,{"name":742,"count":743},"精准农业",21,{"name":745,"count":361},"遥感监测",{"name":618,"count":747},15,{"name":674,"count":584},{"name":750,"count":173},"农业遥感",{"name":752,"count":753},"机器学习",7,{"name":755,"count":408},"GIS",{"name":675,"count":408},{"name":758,"count":705},"传感器",{"name":592,"count":69},{"name":593,"count":69},{"name":762,"count":69},"无人机遥感",{"name":764,"count":455},"农业传感器",{"name":766,"count":455},"多源遥感",{"name":768,"count":455},"大模型",{"name":676,"count":455},[771,773,776,777,779,781,783,785,787,789,791,793],{"name":590,"count":772},80,{"name":774,"count":775},"智能灌溉",23,{"name":594,"count":743},{"name":778,"count":668},"水资源管理",{"name":780,"count":747},"节水灌溉",{"name":782,"count":584},"粮食安全",{"name":784,"count":584},"节水农业",{"name":786,"count":524},"土壤墒情",{"name":788,"count":385},"智慧灌溉",{"name":790,"count":585},"农业水资源",{"name":792,"count":173},"灌溉管理",{"name":794,"count":753},"数字农业"]