[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2261":3,"related-2261":76},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":36,"classification":37,"search_phrases":70,"slug":73,"view_count":74,"doi":8,"paper":8,"created_at":75},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。",null,"农民日报","2026-09-06T16:00:00Z","报道",10,false,85,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},26,23,18,12,6,1,"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。",[24],{"name":9,"url":6},[26,27,28,29,30],"数字乡村","智慧农业","农业物联网","数字孪生","遥感监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":8},"I","digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"partial","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":46,"reason":69},[43,50,57,63],{"code":44,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},"I1",21,true,[27,29],[27],[29,27],{"code":51,"score":52,"supported":46,"title":53,"abstract":54,"tags":56},"I3",4,[],[55],"遥感",[30],{"code":58,"score":52,"supported":46,"title":59,"abstract":60,"tags":62},"I5",[],[61],"物联网",[28],{"code":64,"score":52,"supported":46,"title":65,"abstract":66,"tags":68},"I6",[],[67],"农业大数据",[26],"一级领域明确，二级方向需要细分",[71,72],"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",2,"2026-09-13T00:04:03.310614Z",{"total":20,"page":21,"page_size":20,"items":77},[78,193,255,327,404,470],{"id":79,"title":80,"url":81,"summary":82,"summary_zh":83,"content":8,"source_name":84,"source_url":81,"published_at":85,"category":86,"cover_url":8,"hotness":87,"is_selected":13,"score":88,"score_detail":89,"sources":95,"tags":99,"primary_topic":102,"secondary_topics":103,"classification":104,"search_phrases":174,"slug":177,"view_count":178,"doi":179,"paper":180,"created_at":192},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z","论文",25,69,{"impact":90,"substance":18,"depth":91,"authority":92,"freshness":93,"relevant":21,"comment":94},22,16,13,0,"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。",[96,97],{"name":84,"url":81},{"name":84,"url":98},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914539",[26,27,100,28,101,30],"农业人工智能","气候韧性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":8},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":105},{"candidates":106,"agricultural_evidence":46,"reason":69},[107,116,122,129,134,143,150,157,163,170],{"code":108,"score":18,"supported":46,"title":109,"abstract":111,"tags":115},"I2",[110],"artificial intelligence",[112,113,114,110],"机器学习","病虫害识别","machine learning",[100],{"code":58,"score":18,"supported":46,"title":117,"abstract":119,"tags":121},[118],"IoT",[61,120,118],"internet of things",[28],{"code":44,"score":123,"supported":46,"title":124,"abstract":126,"tags":128},15,[125],"precision agriculture",[127,125],"精准农业",[27],{"code":51,"score":123,"supported":46,"title":130,"abstract":132,"tags":133},[131],"remote sensing",[55,131],[30],{"code":135,"score":136,"supported":46,"title":137,"abstract":138,"tags":142},"H3",9,[],[139,140,141],"灌溉","水资源","irrigation",[],{"code":144,"score":20,"supported":46,"title":145,"abstract":146,"tags":149},"A8",[],[147,148],"粮食安全","food security",[],{"code":151,"score":20,"supported":46,"title":152,"abstract":153,"tags":156},"H2",[],[154,155],"土壤","soil",[],{"code":158,"score":52,"supported":46,"title":159,"abstract":160,"tags":162},"H7",[],[161],"气候变化",[101],{"code":164,"score":165,"supported":46,"title":166,"abstract":167,"tags":169},"H6",3,[],[168],"农业生态",[],{"code":64,"score":21,"supported":13,"title":171,"abstract":172,"tags":173},[],[],[26],[175,176],"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",5,"10.5281\u002Fzenodo.22914538",{"doi":179,"openalex_id":181,"authors":182,"venue":84,"cited_by_count":93,"oa_url":81,"card":185,"direction":189,"ingested_from":191},"W7214083098",[183],{"name":184,"orcid":8},"Twinkal Prakash Sawant",{"tldr":186,"method":187,"finding":188,"direction":189,"opportunity":190},"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",{"id":194,"title":195,"url":196,"summary":197,"summary_zh":198,"content":8,"source_name":84,"source_url":196,"published_at":199,"category":86,"cover_url":8,"hotness":12,"is_selected":13,"score":200,"score_detail":201,"sources":203,"tags":205,"primary_topic":208,"secondary_topics":209,"classification":210,"search_phrases":238,"slug":241,"view_count":93,"doi":242,"paper":243,"created_at":254},5563,"AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531525","Abstract: Crop diseases and pest infestations remain persistent threats to plantation productivity, with pests estimated to destroy up to 40% of global crop production and plant diseases and invasive insects imposing substantial economic losses worldwide (Food and Agriculture Organization of the United Nations [FAO], 2019). Climate change is intensifying these risks by altering the geographic range, seasonality, and population dynamics of pathogens and pests (Gullino et al., 2021; Skendžić et al., 2021). This paper examines how artificial intelligence (AI) — including convolutional neural networks (CNNs), vision transformers (ViTs), unmanned aerial vehicle (UAV)-based multispectral and hyperspectral sensing, and Internet of Things (IoT)-enabled monitoring — can strengthen the early detection of biotic stress in plantation crops and thereby support plantation risk management. Drawing on recent peer-reviewed and industry literature, the paper synthesises evidence on detection performance, deployment modalities, and pathways for integration into enterprise risk-management systems. It proposes a conceptual framework in which AI-enabled early-warning systems operate as a continuous, spatially explicit risk-sensing layer linking agronomic observations with operational, financial, and insurance decision-making. AI can improve the timeliness, spatial precision, and consistency of surveillance; however, its risk-management value depends on field validation, representative training data, reliable infrastructure, affordable deployment, and appropriate governance. AI should therefore currently be positioned as decision support that augments, rather than replaces, agronomic expertise and ground-truthing. Keywords: artificial intelligence; plant disease detection; pest infestation; precision agriculture; plantation risk management; machine learning; remote sensing. Title: AI-Powered Early Detection of Crop Diseases and Pest Infestations: A New Paradigm for Plantation Risk Management Author: Dr. Geetha Govindan K Gopalakrishnan International Journal of Management and Commerce Innovations ISSN 2348-7585 (Online) Vol. 14, Issue 2, October 2026 - March 2027 Page No: 54-59 Research Publish Journals Website: www.researchpublish.com Published Date: 21-October-2026 DOI: https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526 Paper Download Link (Source) https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-management","摘要：作物病害与虫害侵染始终是种植园生产力的持续威胁，据估计，害虫每年可摧毁全球高达40%的作物产量，植物病害与入侵昆虫在全球范围内造成重大经济损失（联合国粮食及农业组织[FAO]，2019）。气候变化通过改变病原体与害虫的地理分布范围、季节性规律及种群动态，正在加剧这些风险（Gullino等，2021；Skendžić等，2021）。本文探讨人工智能（AI）——包括卷积神经网络（CNN）、视觉变换器（ViT）、基于无人机（UAV）的多光谱与高光谱遥感，以及物联网（IoT）赋能的监测技术——如何强化种植园作物生物胁迫的早期检测，从而支撑种植园风险管理。本文基于近期同行评审文献与行业文献，综合梳理了检测性能、部署模式及其融入企业风险管理系统的路径等方面的证据。文章提出一个概念框架，其中AI赋能的预警系统作为持续运行、空间显性的风险感知层，将农艺观测与运营、财务及保险决策相衔接。AI能够提升监测的及时性、空间精度与一致性；然而，其风险管理价值取决于田间验证、具有代表性的训练数据、可靠的基础设施、可负担的部署成本以及适当的治理。因此，当前应将AI定位为决策支持工具，用以增强而非替代农艺专业知识与实地核验。关键词：人工智能；植物病害检测；虫害侵染；精准农业；种植园风险管理；机器学习；遥感。题目：AI驱动的作物病害与虫害侵染早期检测：种植园风险管理的新范式。作者：Geetha Govindan K Gopalakrishnan博士。《国际管理与商业创新期刊》ISSN 2348-7585（在线）第14卷第2期，2026年10月—2027年3月，第54-59页。研究出版期刊网站：www.researchpublish.com。发布日期：2026年10月21日。DOI：https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531526。论文下载链接（来源）：https:\u002F\u002Fwww.researchpublish.com\u002Fpapers\u002Fai-powered-early-detection-of-crop-diseases-and-pest-infestations-a-new-paradigm-for-plantation-risk-ma","2026-10-21T00:00:00Z",56,{"impact":19,"substance":91,"depth":92,"authority":20,"freshness":136,"relevant":21,"comment":202},"综述性论文，系统梳理AI与遥感在作物病虫害早期预警中的应用并提出风险管理框架，有参考价值但方法创新与数据规模有限，信源为普通出版平台。",[204],{"name":84,"url":196},[27,100,30,206,207],"风险管理","病虫害监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":8},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":211},{"candidates":212,"agricultural_evidence":46,"reason":69},[213,218,224,228,233],{"code":108,"score":12,"supported":46,"title":214,"abstract":215,"tags":217},[],[112,114,110,216],"decision support",[100],{"code":51,"score":12,"supported":46,"title":219,"abstract":220,"tags":223},[],[55,131,221,222],"multispectral","hyperspectral",[30],{"code":58,"score":136,"supported":46,"title":225,"abstract":226,"tags":227},[],[61,120,118],[],{"code":44,"score":229,"supported":46,"title":230,"abstract":231,"tags":232},7,[],[127,125],[27],{"code":158,"score":20,"supported":46,"title":234,"abstract":235,"tags":237},[],[161,236],"climate change",[],[239,240],"AI 作物病害 早期检测","无人机 多光谱 病虫害监测","AI作物病害早期检测-5563","10.5281\u002Fzenodo.23531525",{"doi":242,"openalex_id":244,"authors":245,"venue":84,"cited_by_count":93,"oa_url":196,"card":248,"direction":189,"ingested_from":191},"W7223085400",[246],{"name":247,"orcid":8},"Dr. Geetha Govindan K Gopalakrishnan",{"tldr":249,"method":250,"finding":251,"direction":252,"opportunity":253},"综述AI（CNN、ViT、无人机遥感、物联网）用于作物病虫害早期检测并融入种植园风险管理。","文献综述，整合CNN、ViT、无人机多\u002F高光谱与IoT监测的检测性能与部署方式。","AI可提升监测及时性与空间精度，但价值取决于田间验证、代表性数据与治理，应作决策支持而非替代专家。","农业人工智能与决策模型","可将AI预警层与保险、金融风控系统对接，研究跨域数据融合与可解释风险定价的空白。","2026-10-22T11:20:24.428080Z",{"id":256,"title":257,"url":258,"summary":259,"summary_zh":260,"content":8,"source_name":261,"source_url":258,"published_at":262,"category":86,"cover_url":8,"hotness":12,"is_selected":13,"score":263,"score_detail":264,"sources":267,"tags":269,"primary_topic":273,"secondary_topics":274,"classification":275,"search_phrases":301,"slug":304,"view_count":74,"doi":305,"paper":306,"created_at":326},4435,"Land Use Change, Flood Exposure, and Vegetation Stress in the Chi River Basin, Northeast Thailand: A Multi-Temporal Remote Sensing and Machine Learning Assessment","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu181910188","Climate change has intensified hydrological extremes in Southeast Asia, yet the relationships between land use change, flood exposure, and vegetation stress remain poorly understood in tropical floodplain environments. This study assessed land use dynamics, flood exposure, and vegetation stress in the Upper Chi River Basin, Northeast Thailand, using multi-temporal remote sensing datasets spanning 2015–2024, official land use data from the Land Development Department (LDD), and machine learning approaches, including Random Forest classification of binary flood occurrence, SHAP analysis, and spatial statistics. Agricultural land dominated the landscape (84.2% in 2023), with 11.0% land use change occurring between 2015 and 2023, primarily agricultural conversion from forest (38.6% of Forest Area converted) and water body (30.0% of Water Body converted). Flood frequency was highest in areas classified as Water Bodies (0.38 events) and Agricultural Areas (0.55 events), while Forest Areas showed the greatest resilience (0.05 events). Random Forest classification of flood occurrence achieved an overall accuracy of 0.920 and AUC-ROC of 0.905, with DEM emerging as the dominant predictor in both Random Forest importance (0.435) and mean absolute SHAP (0.308). Vegetation stress affected 21.1% of the basin, with 59.2% of points showing an NDVI decline greater than 0.02 and 33.3% falling below the VCI \u003C 0.4 drought threshold. Four sensitivity classes were identified: Low (66.4%), Moderate (19.2%), High (12.4%), and Very High (2.0%), with Very High Sensitivity concentrated along the Chi River corridor in low-lying areas (mean DEM = 146.0 m). Moran’s I confirmed strong spatial clustering of flood frequency (I = 0.787) and sensitivity class (I = 0.534), highlighting the need for spatial statistical approaches. The findings support elevation-based land use zoning, forest conservation, and climate-resilient agricultural practices to enhance flood resilience and contribute to Sustainable Development Goals 2 (Zero Hunger), 11 (Sustainable Cities), 13 (Climate Action), and 15 (Life on Land).","气候变化加剧了东南亚的水文极端事件，但在热带洪泛平原环境中，土地利用变化、洪水暴露与植被胁迫之间的关系仍缺乏深入认识。本研究利用2015—2024年多时相遥感数据集、泰国土地发展厅（LDD）的官方土地利用数据以及机器学习方法（包括对二元洪水发生事件的随机森林分类、SHAP分析和空间统计），评估了泰国东北部奇河上游流域的土地利用动态、洪水暴露和植被胁迫。农业用地在景观中占主导地位（2023年为84.2%），2015—2023年间土地利用变化率为11.0%，主要表现为农业用地扩张导致的林地转换（38.6%的林地面积被转换）和水体转换（30.0%的水体面积被转换）。洪水频率在水体分类区域（0.38次）和农业用地分类区域（0.55次）最高，而林地表现出最强的恢复力（0.05次）。洪水发生事件的随机森林分类总体精度达到0.920，AUC-ROC为0.905，其中DEM在随机森林重要性（0.435）和平均绝对SHAP值（0.308）中均为最主要的预测因子。植被胁迫影响了流域21.1%的区域，59.2%的样点NDVI下降幅度超过0.02，33.3%的样点低于VCI \u003C 0.4的干旱阈值。研究识别出四个敏感性等级：低敏感（66.4%）、中等敏感（19.2%）、高敏感（12.4%）和极高敏感（2.0%），其中极高敏感区集中在奇河沿岸低洼地带（平均DEM = 146.0 m）。Moran's I证实了洪水频率（I = 0.787）和敏感性等级（I = 0.534）具有强烈的空间聚集性，凸显了空间统计方法的必要性。研究结果支持基于海拔的土地利用分区、森林保护以及气候适应性农业实践，以增强洪水恢复力，并助力实现可持续发展目标2（零饥饿）、11（可持续城市）、13（气候行动）和15（陆地生物）。","Sustainability","2026-10-07T00:00:00Z",70,{"impact":265,"substance":90,"depth":18,"authority":92,"freshness":136,"relevant":21,"comment":266},8,"基于多时相遥感与机器学习的流域土地利用—洪涝—植被胁迫评估，方法扎实、数据翔实，对农业防灾与气候韧性有参考价值，但属区域性研究，公共影响有限。",[268],{"name":261,"url":258},[26,112,30,270,271,272],"土地利用","洪涝风险","植被胁迫",{"code":32,"slug":33,"name":34,"path":35,"parent_code":8},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":276},{"candidates":277,"agricultural_evidence":46,"reason":69},[278,282,286,293,297],{"code":108,"score":123,"supported":46,"title":279,"abstract":280,"tags":281},[114],[112,114],[112],{"code":51,"score":123,"supported":46,"title":283,"abstract":284,"tags":285},[131],[55,131],[30],{"code":158,"score":136,"supported":46,"title":287,"abstract":288,"tags":292},[],[161,289,290,236,291],"气候适应","干旱","drought",[],{"code":164,"score":21,"supported":13,"title":294,"abstract":295,"tags":296},[],[],[270],{"code":64,"score":21,"supported":13,"title":298,"abstract":299,"tags":300},[],[],[26],[302,303],"泰国 Chi River 流域 遥感","土地利用变化 洪涝暴露 植被胁迫","泰国ChiRiver流域遥感-4435","10.3390\u002Fsu181910188",{"doi":305,"openalex_id":307,"authors":308,"venue":261,"cited_by_count":93,"oa_url":258,"card":320,"direction":324,"ingested_from":191},"W7220850940",[309,311,314,317],{"name":310,"orcid":8},"Jiradech Majandang",{"name":312,"orcid":313},"Patiwat Littidej","https:\u002F\u002Forcid.org\u002F0000-0002-1024-547X",{"name":315,"orcid":316},"Benjamabhorn Pumhirunroj","https:\u002F\u002Forcid.org\u002F0009-0009-6607-5594",{"name":318,"orcid":319},"D. C. Slack","https:\u002F\u002Forcid.org\u002F0000-0003-0324-2163",{"tldr":321,"method":322,"finding":323,"direction":324,"opportunity":325},"用多时相遥感和机器学习评估泰国栖河流域土地利用变化、洪水暴露与植被胁迫的关系。","2015–2024年多时相遥感、LDD土地利用数据、随机森林、SHAP与空间统计","农地占84.2%，洪水频发于农地和水体，21.1%流域植被受胁迫，DEM是洪水主导因子。","农业遥感与作物表型","可结合高分辨率时序遥感与作物模型，量化洪水胁迫对热带平原农业产量的级联影响。","2026-10-08T23:30:29.519607Z",{"id":328,"title":329,"url":330,"summary":331,"summary_zh":332,"content":8,"source_name":333,"source_url":330,"published_at":334,"category":86,"cover_url":8,"hotness":12,"is_selected":13,"score":335,"score_detail":336,"sources":339,"tags":341,"primary_topic":343,"secondary_topics":344,"classification":345,"search_phrases":380,"slug":383,"view_count":74,"doi":384,"paper":385,"created_at":403},4297,"IoT -Based Smart Agriculture Using Machine Learning","https:\u002F\u002Fdoi.org\u002F10.47392\u002Firjaeh.2026.0711","Agriculture plays a crucial role in economic development and food security. Traditional farming methods often face challenges such as inefficient water usage, unpredictable environmental conditions, and limited real-time monitoring. This paper proposes an IoT-Based Smart Agriculture system integrated with Machine Learning to enhance farm productivity and resource management. The system employs IoT sensors to collect data related to soil moisture, temperature, humidity, and light intensity. These data are transmitted to a cloud platform where machine learning algorithms analyze environmental conditions, predict crop requirements, and generate intelligent recommendations for irrigation and crop management. The proposed solution enables continuous monitoring, reduces water wastage, improves crop health assessment, and supports data-driven decision-making. By combining IoT and Machine Learning, the system offers a cost-effective and scalable approach to precision farming, helping farmers increase yield, reduce operational costs, and promote sustainable agricultural practices.","农业在经济发展和粮食安全中发挥着至关重要的作用。传统耕作方式往往面临水资源利用效率低、环境条件不可预测以及实时监测能力有限等挑战。本文提出了一种基于物联网（IoT）并与机器学习相结合的智慧农业系统，以提升农业生产率和资源管理水平。该系统采用物联网传感器采集与土壤湿度、温度、湿度和光照强度相关的数据。这些数据被传输至云平台，由机器学习算法分析环境条件、预测作物需求，并生成灌溉和作物管理的智能建议。所提出的解决方案能够实现持续监测、减少水资源浪费、改进作物健康评估，并支持数据驱动的决策。通过将物联网与机器学习相结合，该系统为精准农业提供了一种具有成本效益且可扩展的方法，帮助农民提高产量、降低运营成本并促进可持续农业实践。","International Research Journal on Advanced Engineering Hub (IRJAEH)","2026-10-05T00:00:00Z",46,{"impact":265,"substance":19,"depth":337,"authority":20,"freshness":136,"relevant":21,"comment":338},11,"论文提出IoT与机器学习结合的智慧农业系统，思路常规、缺乏实测数据与规模验证，学术权威性一般，仅具参考价值。",[340],{"name":333,"url":330},[27,100,28,342,127],"智能灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":8},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":346},{"candidates":347,"agricultural_evidence":46,"reason":69},[348,354,358,362,366,370,374],{"code":44,"score":349,"supported":46,"title":350,"abstract":352,"tags":353},19,[351],"smart agriculture",[27,127,351],[27,127],{"code":108,"score":123,"supported":46,"title":355,"abstract":356,"tags":357},[114],[112,114],[100],{"code":58,"score":123,"supported":46,"title":359,"abstract":360,"tags":361},[118],[61,118],[28],{"code":135,"score":12,"supported":46,"title":363,"abstract":364,"tags":365},[],[139,140,141],[342],{"code":144,"score":20,"supported":46,"title":367,"abstract":368,"tags":369},[],[147,148],[],{"code":151,"score":20,"supported":46,"title":371,"abstract":372,"tags":373},[],[154,155],[],{"code":375,"score":165,"supported":46,"title":376,"abstract":377,"tags":379},"H8",[],[378],"可持续农业",[],[381,382],"IoT 智慧农业 机器学习","土壤墒情 智能灌溉 云平台","IoT智慧农业机器学习-4297","10.47392\u002Firjaeh.2026.0711",{"doi":384,"openalex_id":386,"authors":387,"venue":333,"cited_by_count":93,"oa_url":330,"card":398,"direction":189,"ingested_from":191},"W7220440757",[388,390,392,394,396],{"name":389,"orcid":8},"Priyanka Behera",{"name":391,"orcid":8},"Satyabrata Satapathy",{"name":393,"orcid":8},"Nakshytrajeet Kar",{"name":395,"orcid":8},"Sucharita NathSharma",{"name":397,"orcid":8},"Priyanka Shit",{"tldr":399,"method":400,"finding":401,"direction":189,"opportunity":402},"提出物联网结合机器学习的智慧农业系统，实现环境监测与智能灌溉决策。","部署土壤湿度、温湿度、光照等IoT传感器，云端机器学习分析预测作物需求。","系统可连续监测、减少水资源浪费、改善作物健康评估并支持数据驱动决策。","可探索边缘计算与轻量模型在田间实时决策中的应用，降低云端依赖与延迟。","2026-10-06T23:30:08.015155Z",{"id":405,"title":406,"url":407,"summary":408,"summary_zh":409,"content":8,"source_name":410,"source_url":407,"published_at":411,"category":86,"cover_url":8,"hotness":87,"is_selected":13,"score":263,"score_detail":412,"sources":414,"tags":418,"primary_topic":420,"secondary_topics":421,"classification":422,"search_phrases":452,"slug":455,"view_count":74,"doi":456,"paper":457,"created_at":469},3616,"Integrated Machine Learning for Agriculture, Environment and Health-Adjacent Systems: Solar Cell Screening, Reservoir Monitoring and IoT-Driven Advisory Across Interdisciplinary Contexts","https:\u002F\u002Fdoi.org\u002F10.6084\u002Fm9.figshare.34001991","This paper explores the convergence of precision agriculture and environmental monitoring by taking advantage of overlapping physical sensor infrastructure. The authors present three interdisciplinary machine learning studies built upon a shared edge-inference architecture optimized for low-latency execution on a Raspberry Pi 4. First, the framework accelerates agrivoltaic deployments by combining density functional theory with gradient boosting regressors to screen perovskite solar cell efficiencies without physical fabrication cycles. Second, it introduces a hybrid 1D-CNN and random forest model to detect water level anomalies in Bangladesh's Kaptai Reservoir, reducing RMSE by 35% over a 40-year validation window. Third, it details an IoT agricultural platform (\"AgriLM\") that pairs ResNet-50 pest detection with an instruction-tuned LLM layer to deliver natural language, climate-adaptive management advice to smallholder farmers, optimizing cross-system resource constraints.","本文探讨了通过利用重叠的物理传感器基础设施，实现精准农业与环境监测的融合。作者提出了三项跨学科机器学习研究，均构建于一个共享的边缘推理架构之上，该架构针对树莓派4（Raspberry Pi 4）上的低延迟执行进行了优化。首先，该框架将密度泛函理论与梯度提升回归器相结合，无需物理制造周期即可筛选钙钛矿太阳能电池效率，从而加速农业光伏部署。其次，该框架引入了一种混合一维卷积神经网络（1D-CNN）与随机森林模型，用于检测孟加拉国卡普泰水库（Kaptai Reservoir）的水位异常，在40年验证窗口内将均方根误差（RMSE）降低了35%。第三，该框架详细介绍了物联网农业平台（“AgriLM”），该平台将ResNet-50害虫检测与指令微调的大语言模型（LLM）层相结合，为小农户提供自然语言、气候适应性的管理建议，并优化跨系统资源约束。","Figshare","2026-09-26T00:00:00Z",{"impact":19,"substance":90,"depth":18,"authority":136,"freshness":136,"relevant":21,"comment":413},"跨学科机器学习论文，方法新颖、数据扎实，但偏学术预印本，产业落地影响有限。",[415,416],{"name":410,"url":407},{"name":410,"url":417},"https:\u002F\u002Fdoi.org\u002F10.6084\u002Fm9.figshare.34001991.v1",[27,100,61,30,419],"光伏农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":8},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":423},{"candidates":424,"agricultural_evidence":46,"reason":69},[425,429,433,437,444,448],{"code":108,"score":123,"supported":46,"title":426,"abstract":427,"tags":428},[114],[112,114],[100],{"code":58,"score":123,"supported":46,"title":430,"abstract":431,"tags":432},[118],[61,118],[61],{"code":44,"score":229,"supported":46,"title":434,"abstract":435,"tags":436},[],[127,125],[27],{"code":438,"score":20,"supported":46,"title":439,"abstract":440,"tags":443},"G2",[],[441,442],"小农户","smallholder",[],{"code":158,"score":165,"supported":46,"title":445,"abstract":446,"tags":447},[],[289],[],{"code":51,"score":21,"supported":13,"title":449,"abstract":450,"tags":451},[],[],[30],[453,454],"Kaptai Reservoir 水位监测","AgriLM 物联网农业平台","KaptaiReservoir水位监测-3616","10.6084\u002Fm9.figshare.34001991",{"doi":456,"openalex_id":458,"authors":459,"venue":410,"cited_by_count":93,"oa_url":407,"card":464,"direction":189,"ingested_from":191},"W7214485392",[460,462],{"name":461,"orcid":8},"Alepera Rasaed",{"name":463,"orcid":8},"Minaya Aleguja",{"tldr":465,"method":466,"finding":467,"direction":189,"opportunity":468},"用共享边缘推理架构整合机器学习，实现太阳能电池筛选、水库监测与IoT农业咨询。","Raspberry Pi 4边缘推理，DFT+梯度提升、1D-CNN+随机森林、","混合模型将水库水位异常检测RMSE降低35%，AgriLM可提供自然语言气候适应建议。","可探索多任务边缘模型在农业-环境-健康跨域场景下的能效与泛化性权衡。","2026-09-27T23:30:24.718444Z",{"id":471,"title":472,"url":473,"summary":474,"summary_zh":8,"content":475,"source_name":476,"source_url":8,"published_at":477,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":478,"score_detail":479,"sources":483,"tags":485,"primary_topic":489,"secondary_topics":490,"classification":491,"search_phrases":533,"slug":536,"view_count":178,"doi":8,"paper":8,"created_at":537},1363,"北大荒信息有限公司：寒地作物大模型与离朱·智能遥感平台接入 49 颗卫星，垦区每 5 天更新作物长势监测","https:\u002F\u002Fso.html5.qq.com\u002Fpage\u002Freal\u002Fsearch_news?docid=70000021_1256a96d87c79652","北大荒信息有限公司已为 4800 余万亩耕地建立数字化档案，研发各类数字化系统 60 余个。寒地作物大模型综合土壤、气象、作物长势信息辅助生成种植方案和变量施肥处方。自主研发的离朱·智能遥感平台接入 49 颗卫星，每 5 天即可完成一轮作物长势监测；垦区田间布设近 5000 台数据采集设备。","新华社哈尔滨9月1日电**题：“黑土粮仓”焕新记**\n\n新华社记者沈易瑾、王优玲、黄腾\n\n从“看天吃饭”到“看屏种地”，从“经验育种”到“精准选种”，从“机械作业”到“智能操控”……新华社记者随“活力中国调研行”采访团在黑龙江走访发现，数字技术、种业创新、智能装备加速走进黑土地，农业生产方式正不断焕新。\n\n走进北大荒信息有限公司，覆盖千万亩耕地的“数字地图”铺满大屏。地块信息、土壤墒情、作物长势、农机轨迹等数据不断更新，卫星遥感、物联网、人工智能等技术逐步融入耕、种、管、收各环节。\n\n北大荒信息有限公司市场运营中心副总经理王浩介绍，目前，公司已为4800余万亩耕地建立数字化档案，研发各类数字化系统60余个。依托长期积累的农业数据，企业研发的寒地作物大模型能够综合土壤、气象、作物长势等信息，辅助生成种植方案和变量施肥处方。\n\n“过去人工巡田，一个人一天大约只能查看200亩地。现在借助无人机、遥感等技术，可以开展大范围巡田，基本每5天就能更新一次垦区作物长势监测结果。”王浩说，数据正成为巡田、施肥、植保等农事决策的重要依据。\n\n从“靠经验”到“看数据”，田间管理越来越精准；从种源端发力，粮食增产潜力也在进一步释放。黑龙江省绥化市北林区的盛昌种子繁育有限责任公司水稻种植基地里，稻穗飘香，稻浪滚滚。技术员卢国臣俯身查看秧苗，边记录边说：“今年新引进的品种长势不错，现在已经进入蜡熟期。”\n\n基地对面，崖州湾国家实验室（绥化）粮油作物创新平台实验室正加紧建设；示范田内，一处新建的抗寒实验设备已经投入使用。\n\n“新引进的种子试种前都要进行抗寒测试。”公司总经理王会说，种子需在恒温15摄氏度的水中持续浸泡，耐寒性达到当地种植要求后，才能进入下一步研发和繁育。\n\n王会介绍，今年5月，绥化市与崖州湾国家实验室签约共建绥化粮油作物创新平台，盛昌种业流转500亩耕地作为试验田，承接南繁北育科研任务，分子育种、基因编辑等前沿技术将应用于寒地作物培育，为新品种选育提供技术支撑。\n\n“好种子”还要“种得好”，新品种不断迭代，农机装备也在向“智”升级。\n\n在哈尔滨市双城区，黑龙江德沃科技开发有限公司，一台台电驱气力式精密播种机整齐排列。风压是否稳定、有没有漏播重播、株距设置多少……这些过去更多依靠机械结构和人工调节的环节，如今可以通过智能终端实时监测和设置。\n\n“电驱系统让作业参数调整更加便捷，排种方式也由机械夹种改为气力吸种，减少种子损伤。”黑龙江德沃科技开发有限公司总工程师杜木军介绍，这款播种机作业速度可达每小时8至12公里，相比传统机械式播种机提高约50%，还可同步完成侧深施肥、覆土镇压等工序。\n\n播得快，还要播得准。企业电驱排种试验室里，每天要开展上百次排种试验。研发人员逐次分析测试数据，再根据试验数据反复调整排种器、变速箱等关键部件。\n\n![Image 1](http:\u002F\u002Fqqpublic.qpic.cn\u002Fqq_public\u002F0\u002F28-3647446424-C803E5C542109517A24A455AD56CA02F\u002F0?fmt=jpg&size=176&h=768&w=1024&ppv=1)\n\n德沃科技研发的电驱气力式精密播种机（9月1日摄）。新华社记者沈易瑾 摄\n\n“目前，我们的设备在国内高端电驱播种机销量中占比超过60%。”杜木军说，近年来，企业围绕马铃薯全程机械化装备、秸秆处理装备等领域持续研发，更好适应规模化、精准化农业生产需求。\n\n作为我国产粮第一大省，黑龙江粮食总产量已连续16年位居全国首位，全省农作物耕种收综合机械化率达到99.28%。科技创新加快融入粮食生产各环节，持续挖掘粮食稳产增产潜力，为“黑土粮仓”注入新的发展动能。","新华社 · 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