[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3357":3,"related-3357":53},{"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":24,"tags":28,"search_phrases":35,"slug":38,"view_count":21,"doi":39,"paper":40,"created_at":52},3357,"AI-Driven Precision Agriculture and Crop Resilience: Integrating Artificial Intelligence, IoT and Remote Sensing for Climate-Resilient Indian Agriculture: A Vision for Viksit Bharat 2047","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914538","Abstract Agriculture is central to India's economic development, food security, rural employment, and the achievement of the Viksit Bharat@2047 vision. However, Indian agriculture faces increasingly complex challenges, including climate variability, water scarcity, soil degradation, pest and disease outbreaks, fragmented landholdings, market uncertainty, and unequal access to agricultural knowledge. These challenges require a transition from conventional, input-intensive agriculture towards data-driven, resource-efficient, climate-resilient and farmer-centric production systems. Agriculture in India is increasingly affected by climate variability, water scarcity, soil degradation, pest and disease outbreaks, and unpredictable weather conditions. These challenges threaten crop productivity and food security, particularly among small and marginal farmers. Artificial Intelligence (AI), Internet of Things (IoT), remote sensing, and machine learning offer new opportunities to transform conventional agricultural practices into data-driven precision agriculture systems. This paper presents a conceptual framework for AI-driven precision agriculture aimed at improving crop resilience under changing climatic conditions. Artificial Intelligence (AI), combined with precision agriculture, Internet of Things (IoT), remote sensing, satellite imagery, drones, machine learning, robotics and digital public infrastructure, offers significant opportunities to transform Indian agriculture. AI can support crop and yield prediction, disease and pest identification, weather-based advisories, irrigation optimisation, soil management, crop insurance, market intelligence and early-warning systems. The paper also discusses challenges related to digital inclusion, data governance, affordability, AI reliability, farmer skills, privacy and institutional coordination. It argues that India's objective should not simply be the digitisation of agriculture, but the creation of an intelligent, inclusive and resilient agricultural ecosystem in which technology augments farmer knowledge and decision-making. By 2047, India can aspire to establish globally competitive agriculture that produces more with fewer resources, withstands climate shocks, generates higher and more stable farm incomes, and ensures sustainable food and nutritional security.","摘要 农业对印度的经济发展、粮食安全、农村就业以及“发达印度@2047”愿景的实现至关重要。然而，印度农业面临日益复杂的挑战，包括气候变异性、水资源短缺、土壤退化、病虫害暴发、土地持有碎片化、市场不确定性以及农业知识获取不平等。这些挑战要求从传统的投入密集型农业向数据驱动、资源高效、气候韧性且以农民为中心的生产体系转型。印度农业日益受到气候变异性、水资源短缺、土壤退化、病虫害暴发及不可预测天气条件的影响。这些挑战威胁着作物生产力和粮食安全，尤其是对小农和边缘农民而言。人工智能（AI）、物联网（IoT）、遥感和机器学习为将传统农业实践转变为数据驱动的精准农业系统提供了新机遇。本文提出了一个AI驱动的精准农业概念框架，旨在改善气候变化条件下作物的韧性。人工智能（AI）与精准农业、物联网（IoT）、遥感、卫星影像、无人机、机器学习、机器人技术及数字公共基础设施相结合，为改造印度农业提供了重大机遇。AI可支持作物与产量预测、病虫害识别、基于天气的农事建议、灌溉优化、土壤管理、作物保险、市场情报及预警系统。本文还讨论了与数字包容、数据治理、可负担性、AI可靠性、农民技能、隐私及机构协调相关的挑战。文章认为，印度的目标不应仅仅是农业数字化，而应是创建一个智能、包容且有韧性的农业生态系统，使技术增强农民的知识与决策能力。到2047年，印度有望建立具有全球竞争力的农业，以更少资源生产更多产品，抵御气候冲击，创造更高且更稳定的农业收入，并确保可持续的粮食与营养安全。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z","论文",25,false,69,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,18,16,13,0,1,"概念性框架论文，系统梳理AI、IoT与遥感在印度气候韧性农业中的应用与挑战，有参考价值但无实证数据，且发布日期在未来、时效性缺失，暂不宜进入每日精选。",[25,26],{"name":10,"url":6},{"name":10,"url":27},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914539",[29,30,31,32,33,34],"数字乡村","智慧农业","农业人工智能","农业物联网","气候韧性","遥感监测",[36,37],"印度 精准农业 AI","农业人工智能 农业物联网 数字乡村 智慧农业","印度精准农业AI-3357","10.5281\u002Fzenodo.22914538",{"doi":39,"openalex_id":41,"authors":42,"venue":10,"cited_by_count":21,"oa_url":6,"card":45,"direction":49,"ingested_from":51},"W7214083098",[43],{"name":44,"orcid":9},"Twinkal Prakash Sawant",{"tldr":46,"method":47,"finding":48,"direction":49,"opportunity":50},"提出AI+物联网+遥感驱动的精准农业概念框架，提升印度气候韧性作物生产。","概念框架分析，整合AI、IoT、遥感、卫星、无人机、机器学习与数字公共基础设施。","印度农业应构建智能、包容、有韧性的生态系统，而非仅数字化，以应对气候与资源挑战。","智慧农业 \u002F 农业物联网","可实证检验小农户场景下AI+IoT+遥感集成对作物韧性与收入的实际效果及数字包容机制。","openalex","2026-09-24T23:30:13.211525Z",{"total":54,"page":22,"page_size":54,"items":55},6,[56,87,129,162,183,220],{"id":57,"title":58,"url":59,"summary":60,"summary_zh":9,"content":9,"source_name":61,"source_url":9,"published_at":62,"category":12,"cover_url":9,"hotness":63,"is_selected":14,"score":64,"score_detail":65,"sources":70,"tags":72,"search_phrases":74,"slug":77,"view_count":21,"doi":9,"paper":78,"created_at":86},2610,"整合人工智能、物联网与遥感技术的大田作物智能灌溉管理 综述","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-9\u002F20260913082658847.htm","发表于Biosystems Engineering。对人工智能(AI)、物联网(IoT)和遥感(RS)技术在灌溉管理中的应用进行全面且结构化分析，特别是在优化基于天气、土壤和作物的灌溉调度方面。智能灌溉系统实现了水资源节约(用水量减少高达20-60%)、降低能源消耗和提高作物生产力。未来研究应优先考虑成本效益高的传感器开发和用户友好的AI界面。","Biosystems Engineering","2026-09-13T01:00:00Z",10,83,{"impact":17,"substance":66,"depth":18,"authority":67,"freshness":68,"relevant":22,"comment":69},21,14,8,"核心期刊综述，系统梳理AI、物联网与遥感在大田灌溉调度中的融合应用，给出节水20-60%等量化结论，对智慧农业技术路线有参考价值。",[71],{"name":61,"url":59},[30,31,32,73,34],"智能灌溉",[75,76],"农业人工智能 农业物联网 智慧农业 智能灌溉","农业人工智能 农业物联网","农业人工智能农业物联网智慧农业智能灌溉-2610",{"doi":9,"openalex_id":9,"authors":79,"venue":9,"cited_by_count":21,"oa_url":9,"card":80,"direction":49,"ingested_from":85},[],{"tldr":81,"method":82,"finding":83,"direction":49,"opportunity":84},"综述AI、物联网与遥感在大田作物智能灌溉调度中的应用与成效。","结构化综述AI、IoT、RS在基于天气、土壤和作物的灌溉调度中的应用。","智能灌溉可节水20-60%，降低能耗并提高作物生产力。","低成本传感器与用户友好AI界面是落地瓶颈，可研究轻量化模型与低成本感知方案。","agent","2026-09-16T00:03:52.381950Z",{"id":88,"title":89,"url":90,"summary":91,"summary_zh":92,"content":9,"source_name":10,"source_url":90,"published_at":11,"category":12,"cover_url":9,"hotness":93,"is_selected":14,"score":94,"score_detail":95,"sources":100,"tags":108,"search_phrases":110,"slug":113,"view_count":21,"doi":114,"paper":115,"created_at":128},2313,"Artificial Intelligence For Climate-Resilient Plants: Emerging Ai Approaches For Predicting Drought, Salinity And Temperature Stress.","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22724439","Abstract Climate change is creating serious problems for agriculture by increasing drought, soil salinity, and extreme temperatures. These stresses affect plant growth, development, crop yield, and global food security. Traditional methods used to study plant responses to stress are often time-consuming, labour-intensive, and difficult to use for large numbers of plants. Artificial intelligence (AI) is becoming an important tool for studying and predicting plant responses to changing environmental conditions. AI can analyse large amounts of data collected through plant phenotyping, remote sensing, environmental sensors, and molecular studies. This review focuses on recent AI approaches used to predict plant responses to drought, salinity, and temperature stress from 2016 to 2026. Machine learning, deep learning, computer vision, thermal imaging, and hyperspectral imaging can help in early detection and prediction of plant stress. Recent developments are moving beyond simple stress identification towards predicting crop performance and stress tolerance. The combination of AI with high-throughput phenotyping and multi-omics can help identify stress-tolerant crop varieties and support climate-resilient breeding. However, challenges related to data quality, limited field validation, unclear model predictions, and poor performance across different environments still remain. Future research should develop reliable and explainable AI models for sustainable agriculture and improved crop production under climate change.","摘要 气候变化正通过加剧干旱、土壤盐渍化和极端温度，给农业带来严重问题。这些胁迫影响植物生长、发育、作物产量和全球粮食安全。用于研究植物胁迫响应的传统方法往往耗时、费力，且难以应用于大量植物。人工智能（AI）正成为研究和预测植物对环境条件变化响应的重要工具。AI可以分析通过植物表型分析、遥感、环境传感器和分子研究收集的大量数据。本文综述聚焦于2016年至2026年间用于预测植物对干旱、盐分和温度胁迫响应的近期AI方法。机器学习、深度学习、计算机视觉、热成像和高光谱成像有助于植物胁迫的早期检测和预测。近期发展正超越简单的胁迫识别，转向预测作物表现和胁迫耐受性。AI与高通量表型分析和多组学的结合有助于识别耐胁迫作物品种，并支持气候韧性育种。然而，数据质量、田间验证有限、模型预测不明确以及在不同环境中表现不佳等挑战仍然存在。未来研究应开发可靠且可解释的AI模型，以促进气候变化下的可持续农业和作物生产提升。",55,71,{"impact":18,"substance":96,"depth":97,"authority":20,"freshness":98,"relevant":22,"comment":99},20,17,3,"系统综述AI预测干旱、盐碱与高温胁迫的研究进展，方法覆盖机器学习、深度学习与高光谱成像，对气候韧性育种有参考价值，但属综述类论文且距发布已逾两周，时效性偏弱。",[101,102,104,106],{"name":10,"url":90},{"name":10,"url":103},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22724438",{"name":10,"url":105},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22767935",{"name":10,"url":107},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22767934",[30,31,33,34,109],"作物育种",[111,112],"农业人工智能 作物育种 智慧农业 气候韧性","农业人工智能 作物育种","农业人工智能作物育种智慧农业气候韧性-2313","10.5281\u002Fzenodo.22724439",{"doi":114,"openalex_id":116,"authors":117,"venue":10,"cited_by_count":21,"oa_url":90,"card":122,"direction":126,"ingested_from":51},"W7212377743",[118,120],{"name":119,"orcid":9},"Aruna Nangare",{"name":121,"orcid":9},"Vaishali Wankhede",{"tldr":123,"method":124,"finding":125,"direction":126,"opportunity":127},"综述2016-2026年AI预测植物干旱、盐碱和温度胁迫响应的进展。","机器学习、深度学习、计算机视觉、热成像与高光谱成像结合表型组和多组学数据。","AI已从简单胁迫识别转向预测作物表现与耐逆性，但数据质量、田间验证和跨环境泛化仍是瓶颈。","农业遥感与作物表型","可解释AI与高通量表型、多组学融合，用于跨环境耐逆品种预测与气候韧性育种。","2026-09-13T23:30:17.636747Z",{"id":130,"title":131,"url":132,"summary":133,"summary_zh":134,"content":9,"source_name":10,"source_url":132,"published_at":135,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":136,"score_detail":137,"sources":141,"tags":145,"search_phrases":148,"slug":149,"view_count":22,"doi":150,"paper":151,"created_at":161},2312,"MridAI: Autonomous Edge-to-Conversational IoT for Democratising Precision Agriculture via Neuro-Symbolic Telemetry","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22724805","While sensor-guided precision agriculture improves water efficiency and curtails chemical run-off, adoption across smallholder farm land in the Global South remains under 1%. Commercial telemetry systems are constrained by high capital acquisition costs (>$300) and complex, dashboard-centric mobile applications that impose heavy cognitive burdens on low-literacy farmers. This paper proposes MridAI, a low-cost (\u003C$45 \u002F ₹3,420 COGS) autonomous in-ground agro-telemetry node coupled with a cloud-based neuro-symbolic artificial intelligence advisory pipeline. The physical layer integrates a multi-parameter Modbus RS485 sensor, an ESP32-C3 micro-controller, an Indian-band 4G LTE Cat-1 modem, and an energy harvesting subsystem within an IP67-rated solar stem. To eliminate measurement errors in high-clay tropical soils (Vertisols), an on-device digital signal-processing pipeline applies an adapted Topp dielectric correction alongside a one-dimensional discrete Kalman filter. To overcome rural telecommunication instability, the firmware implements an asynchronous, non-volatile store-and-forward ring buffer. At the cloud layer, an analytical solver calculates deterministic FAO-56 evapotranspiration deficits and enforces Indian Council of Agricultural Research (ICAR) chemical boundaries, strictly isolating numerical computation from an instruction-tuned Large Language Model (LLM). The generative model functions solely as a linguistic translator, delivering actionable, dialect-adapted recommendations directly via the Meta WhatsApp Cloud API without requiring third-party application downloads. Empirical power-budget modelling confirms indefinite operational autonomy (>240 days without solar irradiance), establishing a scalable paradigm for digital agriculture.","尽管传感器引导的精准农业提高了用水效率并减少了化学品径流，但在全球南方小农农田中的采用率仍不足1%。商业遥测系统受制于高昂的资本购置成本（超过300美元）以及复杂的、以仪表盘为中心的移动应用程序，后者给低识字率农民带来了沉重的认知负担。本文提出MridAI，一种低成本（物料清单成本低于45美元\u002F3，420印度卢比）的自主地下农业遥测节点，并耦合基于云的神经符号人工智能咨询流水线。物理层将多参数Modbus RS485传感器、ESP32-C3微控制器、印度频段4G LTE Cat-1调制解调器以及能量收集子系统集成于IP67防护等级的太阳能杆体内。为消除高黏土热带土壤（变性土）中的测量误差，设备端数字信号处理流水线采用适配的Topp介电校正与一维离散卡尔曼滤波器。为克服农村电信不稳定性，固件实现了异步、非易失性的存储转发环形缓冲区。在云层，分析求解器计算确定性的FAO-56蒸散亏缺，并执行印度农业研究理事会（ICAR）的化学品边界，将数值计算与指令微调的大语言模型（LLM）严格隔离。生成模型仅充当语言翻译器，通过Meta WhatsApp Cloud API直接提供可操作的、适配方言的建议，无需下载第三方应用程序。实证功率预算建模证实了无限期运行自主性（无太阳辐照下超过240天），为数字农业建立了一种可扩展的范式。","2026-09-12T00:00:00Z",85,{"impact":17,"substance":138,"depth":18,"authority":20,"freshness":139,"relevant":22,"comment":140},23,9,"面向全球南方小农户的低成本自主土壤遥测节点与神经符号AI咨询管线，方法新颖、成本与能耗数据具体，对农业信息化与普惠数字农业有实质参考价值。",[142,143],{"name":10,"url":132},{"name":10,"url":144},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22724804",[29,30,31,32,146,147],"小农户","精准灌溉",[37,76],"农业人工智能农业物联网数字乡村智慧农业-2312","10.5281\u002Fzenodo.22724805",{"doi":150,"openalex_id":152,"authors":153,"venue":10,"cited_by_count":21,"oa_url":132,"card":156,"direction":49,"ingested_from":51},"W7212357737",[154],{"name":155,"orcid":9},"Pranit Kamble",{"tldr":157,"method":158,"finding":159,"direction":49,"opportunity":160},"提出低成本自主地下物联网节点与神经符号AI咨询管道，让小农户用WhatsApp获取精准农业建议。","ESP32-C3与Modbus传感器、Topp校正和卡尔曼滤波、FAO-56与L","系统成本低于45美元，可独立运行超240天，无需下载应用即可通过WhatsApp获得方言化建议。","可探索低资源语言与方言适配的LLM农业咨询，以及神经符号系统在更多作物和土壤类型中的泛化验证。","2026-09-13T23:30:14.323767Z",{"id":163,"title":164,"url":165,"summary":166,"summary_zh":9,"content":9,"source_name":167,"source_url":9,"published_at":168,"category":169,"cover_url":9,"hotness":63,"is_selected":14,"score":136,"score_detail":170,"sources":174,"tags":176,"search_phrases":178,"slug":181,"view_count":21,"doi":9,"paper":9,"created_at":182},2261,"北大荒集团智慧农业:4800万亩耕地打造\"地块链\"式数字孪生管理","https:\u002F\u002Fszb.farmer.com.cn\u002Fnmrb\u002Fhtml\u002F2026\u002F20260907\u002F20260907_8\u002Fnmrb_20260907_13398_8_2096696093043691617.html","北大荒信息有限公司智慧农业大数据中心将4800万亩耕地切分成27万个地块,每块都有编号和二维码。遥感平台接入48颗卫星每5天做一次体检,5000台田间采集设备、600个气象站、500台虫情测报灯昼夜值守,3.3万台物联网设备统一接入\"地块链\"管理。5年累计为农户线上放贷400亿元。","农民日报","2026-09-06T16:00:00Z","报道",{"impact":171,"substance":138,"depth":18,"authority":172,"freshness":54,"relevant":22,"comment":173},26,12,"北大荒4800万亩耕地实现地块级数字孪生管理，卫星遥感、物联网与地块链数据规模具体，属产业级智慧农业标杆案例，值得入选每日精选。",[175],{"name":167,"url":165},[29,30,32,177,34],"数字孪生",[179,180],"农业物联网 数字乡村 数字孪生 智慧农业","农业物联网 数字乡村","农业物联网数字乡村数字孪生智慧农业-2261","2026-09-13T00:04:03.310614Z",{"id":184,"title":185,"url":186,"summary":187,"summary_zh":188,"content":9,"source_name":189,"source_url":186,"published_at":190,"category":12,"cover_url":9,"hotness":191,"is_selected":14,"score":192,"score_detail":193,"sources":195,"tags":201,"search_phrases":204,"slug":207,"view_count":21,"doi":208,"paper":209,"created_at":219},2139,"Connecting the Canopy: C-Band and Ku-Band Satellite Technologies for Sustainable Oil Palm Plantation Management in Indonesia—A Review","https:\u002F\u002Fdoi.org\u002F10.47191\u002Fetj\u002Fv11i09.06","Indonesia's oil palm plantations are increasingly expected to combine high productivity with environmental protection, traceability, worker welfare, and smallholder inclusion. Achieving these objectives requires reliable digital connectivity across plantation landscapes that are frequently extensive, remote, and inadequately served by terrestrial telecommunications. This qualitative literature review examines the characteristics of C-band and Ku-band satellite technologies and critically explores their roles in supporting sustainable oil palm plantation management in Indonesia. Drawing on interdisciplinary literature published since 2020 covering satellite communications, Internet of Things, smart agriculture, remote sensing, oil palm agronomy, and sustainability governance, the review develops a socio-technical synthesis rather than a systematic or meta-analytic assessment. C-band generally provides stronger resilience to tropical rain attenuation and is attractive for fixed, availability-critical backbone connectivity, whereas Ku-band enables smaller terminals, greater deployment flexibility, and practical broadband access but requires more deliberate rain-fade mitigation. Neither band is intrinsically superior, and actual performance depends on link design, service architecture, traffic requirements, cost, and local conditions. The review identifies applications in environmental surveillance, precision agronomy, disease detection, harvesting, logistics, traceability, worker safety, and sustainability assurance. It proposes an Observe–Connect–Decide–Act–Verify framework and a hybrid connectivity architecture integrating field networks, satellite backhaul, terrestrial networks, edge computing, remote sensing, and analytics. Sustainable benefits ultimately depend not merely on connectivity but on inclusive governance, institutional capacity, appropriate agronomic action, and mechanisms preventing digitalization from reinforcing existing inequalities.","印度尼西亚的油棕种植园日益被期望将高生产力与环境保护、可追溯性、工人福利和小农包容性结合起来。实现这些目标需要在种植园景观中建立可靠的数字连接，而这些景观往往面积广阔、地处偏远，且地面电信服务不足。本定性文献综述考察了C波段和Ku波段卫星技术的特征，并批判性地探讨了它们在支持印度尼西亚可持续油棕种植园管理中的作用。综述借鉴了2020年以来发表的多学科文献，涵盖卫星通信、物联网、智慧农业、遥感、油棕农学和可持续性治理，构建了一种社会技术综合，而非系统性或元分析评估。C波段通常对热带降雨衰减具有更强的抵御能力，适用于固定的、可用性至关重要的骨干连接；而Ku波段则支持更小的终端、更大的部署灵活性和实用的宽带接入，但需要更有针对性的雨衰缓解措施。两者并无本质上的优劣之分，实际性能取决于链路设计、服务架构、流量需求、成本和当地条件。综述识别了其在环境监测、精准农艺、病害检测、收获、物流、可追溯性、工人安全和可持续性保障方面的应用。它提出了一个“观察—连接—决策—行动—验证”框架，以及一种混合连接架构，整合田间网络、卫星回传、地面网络、边缘计算、遥感和分析。可持续效益最终不仅取决于连接性，还取决于包容性治理、机构能力、适当的农艺行动，以及防止数字化加剧现有不平等的机制。","Engineering and Technology Journal","2026-09-10T00:00:00Z",40,76,{"impact":18,"substance":96,"depth":97,"authority":20,"freshness":68,"relevant":22,"comment":194},"综述系统梳理C波段与Ku波段卫星通信在印尼油棕可持续种植中的应用，提出“观测—连接—决策—行动—验证”框架与混合组网架构，对热带经济作物产区的农业信息化建设具有参考价值，但属文献综述、非原始数据研究，且地域局限于印尼。",[196,197,199],{"name":189,"url":186},{"name":189,"url":198},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22689903",{"name":189,"url":200},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22689902",[29,30,32,34,202,203],"卫星通信","油棕种植",[205,206],"农业物联网 卫星通信 数字乡村 智慧农业","农业物联网 卫星通信","农业物联网卫星通信数字乡村智慧农业-2139","10.47191\u002Fetj\u002Fv11i09.06",{"doi":208,"openalex_id":210,"authors":211,"venue":189,"cited_by_count":21,"oa_url":186,"card":214,"direction":49,"ingested_from":51},"W7212110357",[212],{"name":213,"orcid":9},"Loso Judijanto",{"tldr":215,"method":216,"finding":217,"direction":49,"opportunity":218},"综述C波段与Ku波段卫星技术在印尼油棕可持续种植管理中的应用与选择。","2020年以来跨学科文献定性综述，提出社会技术综合框架。","两波段无绝对优劣，性能取决于链路设计、成本与本地条件，可持续性更依赖治理。","可实证比较C\u002FKu波段在热带雨林衰减下的物联网回传性能，并评估小农户数字包容机制。","2026-09-11T23:30:10.114685Z",{"id":221,"title":222,"url":223,"summary":224,"summary_zh":9,"content":9,"source_name":61,"source_url":223,"published_at":225,"category":12,"cover_url":9,"hotness":63,"is_selected":14,"score":226,"score_detail":227,"sources":229,"tags":231,"search_phrases":232,"slug":233,"view_count":21,"doi":234,"paper":235,"created_at":255},2024,"Integrating artificial intelligence, Internet of Things, and remote sensing for smart irrigation management of field crops: A review","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104589","Integrating artificial intelligence, Internet of Things, and remote sensing for smart irrigation management of field crops: A review。Biosystems Engineering","2026-09-09T00:00:00Z",78,{"impact":18,"substance":96,"depth":97,"authority":67,"freshness":139,"relevant":22,"comment":228},"核心期刊综述，系统梳理AI、物联网与遥感融合的田间作物智能灌溉研究进展，对智慧农业技术路线有参考价值，但属综述类成果、非突破性原创，适合进入每日精选。",[230],{"name":61,"url":223},[30,31,32,73,34],[75,76],"农业人工智能农业物联网智慧农业智能灌溉-2024","10.1016\u002Fj.biosystemseng.2026.104589",{"doi":234,"openalex_id":236,"authors":237,"venue":61,"cited_by_count":21,"oa_url":9,"card":9,"direction":9,"ingested_from":51},"W7212048658",[238,241,243,245,247,249,252],{"name":239,"orcid":240},"Ehab H. Hegazi","https:\u002F\u002Forcid.org\u002F0000-0002-1468-1420",{"name":242,"orcid":9},"Jian Liu",{"name":244,"orcid":9},"Ruixia Ai",{"name":246,"orcid":9},"Lin Liu",{"name":248,"orcid":9},"Xuemei Liu",{"name":250,"orcid":251},"Jin Yuan","https:\u002F\u002Forcid.org\u002F0000-0002-5803-6626",{"name":253,"orcid":254},"G. Papadakis","https:\u002F\u002Forcid.org\u002F0000-0002-1805-5056","2026-09-10T23:30:05.849251Z"]