[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3243":3,"related-3243":46},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":8,"paper":36,"created_at":45},3243,"Integrating Climate Resilience and Digital Traceability in Regenerative Agri-Food Systems: A Scenario-Based Framework for Verifiable Sustainability Claims（再生农业食品系统整合气候韧性与数字溯源：可验证可持续性声明的情景框架）","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F18\u002F2024","MDPI Agriculture 16(18) 2024 发表研究：面对再生农业食品系统的可持续性声明常缺乏可验证、系统性证据的问题，构建整合情景分析、SWOT评估和案例研究的三方法框架。三种前瞻性情景——乐观、常规、悲观——基于现有气候韧性和政策文献构建；SWOT分析综合区块链、物联网和AI赋能的溯源机制影响因素；案例研究以先前记录的再生农业食品计划为基础，分析气候韧性叙事、数字验证工具和消费者信任线索的交互作用。结果表明，再生声明常与可验证数据流脱节，数字溯源采用面临结构性、成本相关障碍，消费者信任更依赖于情感和本土化线索而非正式认证；该框架为政策制定者、认证机构和农产品生产者将气候韧性战略与数字可验证可持续性声明相结合提供可复制、低资源方法。（罗马尼亚布加勒斯特国家科学技术大学Politehnica）",null,"MDPI Agriculture","2026-09-20T00:00:00Z","论文",10,false,70,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},12,20,17,13,8,1,"论文提出情景分析、SWOT与案例研究三方法框架，指出再生农业可持续性声明与可验证数据流脱节，对农业数字化溯源有参考价值，但属学术探讨、产业落地影响有限。",[24],{"name":9,"url":6},[26,27,28,29,30],"农业人工智能","区块链","气候韧性","数字溯源","再生农业",[32,33],"布加勒斯特 Politehnica 再生农业","MDPI Agriculture 数字溯源","布加勒斯特Politehnica再生农业-3243",0,{"doi":8,"openalex_id":8,"authors":37,"venue":8,"cited_by_count":35,"oa_url":8,"card":38,"direction":42,"ingested_from":44},[],{"tldr":39,"method":40,"finding":41,"direction":42,"opportunity":43},"构建情景分析、SWOT与案例研究框架，整合再生农业的气候韧性与数字溯源以验证可持续性声明。","情景分析、SWOT评估与案例研究三方法框架，结合区块链、物联网和AI溯源文献。","再生声明常与可验证数据脱节，数字溯源有成本障碍，消费者信任更依赖情感与本土线索。","数字乡村与农业信息化","可探索低成本数字溯源与本土化信任线索结合的可验证声明机制，尤其在小农户场景。","agent","2026-09-23T00:04:32.924354Z",{"total":47,"page":21,"page_size":47,"items":48},6,[49,80,123,162,204,244],{"id":50,"title":51,"url":52,"summary":53,"summary_zh":8,"content":8,"source_name":54,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":55,"score_detail":56,"sources":61,"tags":63,"search_phrases":67,"slug":70,"view_count":35,"doi":8,"paper":71,"created_at":79},3050,"面向再生农业与气候韧性食物系统的可解释数字孪生集成框架","https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F18\u002F9645","研究提出集成全球农业基础表征模型（GAFRM）、自演化可解释数字孪生（SEDT）、自演化进化基础优化器（SEEFO）与增强型绿色再生农业可持续性指数（GRASI）的统一计算框架，显式将作物微量营养素密度纳入农业决策架构并建模其与覆盖作物、生物炭施用、免耕等再生实践的关系。框架使用多源全球数据集评估全球约60个代表性国家6大洲15个气候带2000-2026年数据，SEDT达到RMSE 3.18\u002FMAE 2.29\u002FR² 0.972\u002FNSE 0.968；SEEFO获得最高Hypervolume 0.956与最低GD 0.028；通过XAI特征归因建立全透明、可解释决策支持环境。","MDPI Sustainability 18(18):9645",80,{"impact":57,"substance":58,"depth":57,"authority":19,"freshness":59,"relevant":21,"comment":60},18,22,9,"方法新颖、数据规模大且指标可靠，属农业人工智能与气候智慧农业前沿成果，值得进入每日精选。",[62],{"name":54,"url":52},[64,65,66,28,30],"智慧农业","可解释AI","数字孪生",[68,69],"GAFRM 数字孪生 再生农业","GRASI 气候韧性","GAFRM数字孪生再生农业-3050",{"doi":8,"openalex_id":8,"authors":72,"venue":8,"cited_by_count":35,"oa_url":8,"card":73,"direction":77,"ingested_from":44},[],{"tldr":74,"method":75,"finding":76,"direction":77,"opportunity":78},"提出集成数字孪生与进化优化的可解释框架，将微量营养素密度纳入再生农业决策。","全球60国2000-2026多源数据，GAFRM+SEDT+SEEFO+GRAS","SEDT预测精度高（R²0.972），SEEFO优化性能最优，实现透明可解释决策支持。","农业人工智能与决策模型","可探索将微量营养素密度与再生实践耦合的实时数字孪生，并验证跨气候带可迁移性。","2026-09-21T00:04:39.490071Z",{"id":81,"title":82,"url":83,"summary":84,"summary_zh":85,"content":8,"source_name":86,"source_url":83,"published_at":87,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":88,"score_detail":89,"sources":92,"tags":94,"search_phrases":98,"slug":101,"view_count":35,"doi":102,"paper":103,"created_at":122},2945,"Assessing spatial suitability and climate resilience of agroforestry systems in the Indian Himalaya of Uttarakhand using remote sensing and artificial neural networks","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44415-026-00113-9","Agroforestry systems are widely recognized for their capacity to enhance ecological sustainability, livelihood security, and climate resilience in mountain environments. However, their spatial suitability and long-term resilience under changing climatic conditions remain poorly quantified in the Indian Himalaya. This study assesses the spatial suitability and climate resilience of agroforestry systems in Uttarakhand using an integrated framework of remote sensing, geographic information systems (GIS), and artificial neural networks (ANNs). Landsat 8 OLI imagery (30 m resolution) was classified using a supervised Gaussian Maximum Likelihood Classifier (MLC), while land-use categories were defined based on a modified Anderson Level I\u002FII classification scheme to represent the heterogeneous Himalayan landscape. Field surveys conducted across representative agroecological zones documented the composition of tree and crop species within prevailing agroforestry systems. A multi-criteria land suitability analysis, guided by FAO (Food and Agriculture Organization of the United Nations) principles, was implemented using key biophysical variables including altitude, slope, aspect, Normalized Difference Vegetation Index (NDVI), soil properties, temperature, and precipitation. Future agroforestry patterns were simulated using an ANN model under RCP 4.5 climate scenarios. The model demonstrated strong predictive performance, indicating reliable simulation of agroforestry distribution. Results show that mid-altitudinal zones offer the highest suitability for agroforestry expansion, whereas small and fragmented systems exhibit reduced climate resilience under future projections. The findings provide a spatially explicit framework to support climate-resilient agroforestry planning and policy formulation in the Himalayan region.","农林复合系统因其在山区环境中提升生态可持续性、生计安全及气候适应能力方面的作用而受到广泛认可。然而，在印度喜马拉雅地区，其空间适宜性及气候变化条件下的长期适应能力仍缺乏充分的量化评估。本研究采用遥感、地理信息系统（GIS）与人工神经网络（ANN）相结合的综合框架，评估了北阿坎德邦农林复合系统的空间适宜性与气候适应能力。利用监督式高斯最大似然分类器（MLC）对Landsat 8 OLI影像（30 m分辨率）进行分类，同时基于改进的Anderson一级\u002F二级分类方案界定土地利用类别，以表征喜马拉雅地区异质性景观。在代表性农业生态区开展实地调查，记录了现有农林复合系统中树种和作物物种的组成。依据联合国粮食及农业组织（FAO）原则，采用多准则土地适宜性分析，纳入海拔、坡度、坡向、归一化植被指数（NDVI）、土壤属性、温度和降水等关键生物物理变量。利用ANN模型在RCP 4.5气候情景下模拟未来农林复合系统格局。模型表现出较强的预测性能，表明对农林复合系统分布的模拟可靠。结果表明，中海拔区域为农林复合系统扩展提供了最高的适宜性，而小型和破碎化系统在未来预测情景下表现出较低的气候适应能力。研究结果为喜马拉雅地区气候适应型农林复合系统规划与政策制定提供了空间明确的框架支持。","Discover Forests","2026-09-18T00:00:00Z",72,{"impact":16,"substance":90,"depth":18,"authority":19,"freshness":59,"relevant":21,"comment":91},21,"遥感与人工神经网络结合评估喜马拉雅地区农林复合系统空间适宜性与气候韧性，方法新颖、结论有区域政策参考价值，但属境外区域研究，公共影响有限。",[93],{"name":86,"url":83},[26,95,28,96,97],"遥感","土地适宜性","农林复合经营",[99,100],"Uttarakhand 农林复合系统 遥感","印度喜马拉雅 人工神经网络 土地适宜性","Uttarakhand农林复合系统遥感-2945","10.1007\u002Fs44415-026-00113-9",{"doi":102,"openalex_id":104,"authors":105,"venue":86,"cited_by_count":35,"oa_url":83,"card":115,"direction":119,"ingested_from":121},"W7213553408",[106,108,110,113],{"name":107,"orcid":8},"Deepak Kumar Mishra",{"name":109,"orcid":8},"Ujjwal Kumar",{"name":111,"orcid":112},"A. Arunachalam","https:\u002F\u002Forcid.org\u002F0000-0001-6590-4113",{"name":114,"orcid":8},"Ayyanadar Arunachalam",{"tldr":116,"method":117,"finding":118,"direction":119,"opportunity":120},"结合遥感、GIS与人工神经网络评估印度喜马拉雅地区农林复合系统的空间适宜性与气候韧性。","Landsat 8影像监督分类、FAO多准则适宜性分析、ANN在RCP4.5情景","中海拔区最适宜农林复合扩展，小而破碎系统在未来气候下韧性较低。","农业遥感与作物表型","可将该遥感-ANN框架迁移至中国山区，耦合多情景气候与农户数据优化农林复合布局。","openalex","2026-09-19T23:30:33.082917Z",{"id":124,"title":125,"url":126,"summary":127,"summary_zh":128,"content":8,"source_name":129,"source_url":126,"published_at":130,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":131,"score_detail":132,"sources":136,"tags":138,"search_phrases":143,"slug":146,"view_count":35,"doi":147,"paper":148,"created_at":161},2625,"Multi-omics and artificial intelligence for climate-resilient and nutrient-enriched food crops: advances, applications and future perspectives","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1931230","Climate change has intensified both abiotic and biotic stresses, threatening global crop productivity, nutritional quality, and agricultural sustainability. These challenges have accelerated the adoption of advanced multi-omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, together with artificial intelligence (AI) to accelerate the development of climate-resilient and nutrient-enriched crops. This review provides an integrated view of the recent advances in AI-enabled multi-omics, genome editing and precision agriculture for climate-resilient and nutrient-rich crops along with emerging trends, current challenges, and future research priorities. Machine learning-enhanced genomic selection approaches such as convolutional neural network (CNN)-based models and transformer models enhance the accuracy of predictions for complex stress-tolerant and nutritional traits. AI-assisted unmanned aerial vehicle (UAV) remote sensing allows measurement of traits at an unprecedented scale and with high-throughput and quantitative phenotyping. AI-driven multi-omics integration approaches, such as pan-omics pipelines, decipher abiotic stress tolerance regulatory networks. The AI-assisted CRISPR-Cas9 genome editing systems have accelerated crop biofortification outcomes by improving target prioritization and guide RNA design. Genome-editing studies have reported enhanced iron and zinc bioavailability in experimental wheat lines, reduced phytic acid content in soybean, and enhanced vitamin and amino acid content in cassava and maize, highlighting the potential of AI-assisted genome-editing approaches for crop biofortification. AI-enabled precision agriculture platforms use AI together with satellite data and Internet of Things (IoT) sensors and predictive modeling to enhance crop management while decreasing environmental resource usage. This review examines data standardization and model interpretability together with access and regulations while it presents a future roadmap for achieving 2050 food security targets. By integrating multi-omics, artificial intelligence, genome engineering, and precision agriculture into a unified framework, this review identifies future research directions and key knowledge gaps for the development of climate-resilient and nutrient-rich crops.","气候变化加剧了非生物与生物胁迫，对全球作物生产力、营养品质和农业可持续性构成威胁。这些挑战加速了先进多组学技术（包括基因组学、转录组学、蛋白质组学和代谢组学）以及人工智能（AI）的应用，以加快气候韧性及营养强化作物的开发。本综述综合阐述了AI赋能的多组学、基因组编辑和精准农业在气候韧性及营养丰富作物方面的最新进展，以及新兴趋势、当前挑战和未来研究重点。机器学习增强的基因组选择方法，如基于卷积神经网络（CNN）的模型和Transformer模型，提高了对复杂耐逆性和营养性状预测的准确性。AI辅助的无人机（UAV）遥感能够以前所未有的规模进行高通量定量表型测量。AI驱动的多组学整合方法，如泛组学流程，可解析非生物胁迫耐受调控网络。AI辅助的CRISPR-Cas9基因组编辑系统通过改进靶标优先级排序和引导RNA设计，加速了作物生物强化成果。基因组编辑研究报告了实验小麦品系中铁和锌生物利用度的提高、大豆中植酸含量的降低，以及木薯和玉米中维生素和氨基酸含量的增强，凸显了AI辅助基因组编辑方法在作物生物强化方面的潜力。AI赋能的精准农业平台利用AI结合卫星数据、物联网（IoT）传感器和预测建模，在减少环境资源使用的同时增强作物管理。本综述审视了数据标准化和模型可解释性以及可及性和监管问题，同时提出了实现2050年粮食安全目标的未来路线图。通过将多组学、人工智能、基因组工程和精准农业整合为统一框架，本综述确定了开发气候韧性及营养丰富作物的未来研究方向和关键知识空白。","Frontiers in Sustainable Food Systems","2026-09-16T00:00:00Z",86,{"impact":58,"substance":58,"depth":133,"authority":134,"freshness":59,"relevant":21,"comment":135},19,14,"核心期刊综述，系统梳理AI驱动多组学、基因编辑与精准农业在气候韧性及营养强化作物上的进展，方法新颖、结论可靠，对智慧育种与农业AI方向有较高参考价值。",[137],{"name":129,"url":126},[26,139,140,28,141,142],"精准农业","基因编辑","智慧育种","多组学",[144,145],"农业人工智能 基因编辑 智慧育种 气候韧性","农业人工智能 基因编辑","农业人工智能基因编辑智慧育种气候韧性-2625","10.3389\u002Ffsufs.2026.1931230",{"doi":147,"openalex_id":149,"authors":150,"venue":129,"cited_by_count":35,"oa_url":126,"card":155,"direction":160,"ingested_from":121},"W7213241547",[151,153],{"name":152,"orcid":8},"Surasreeta Paul",{"name":154,"orcid":8},"Sandeep Singh Rana",{"tldr":156,"method":157,"finding":158,"direction":77,"opportunity":159},"综述AI赋能多组学、基因编辑与精准农业，培育气候韧性与营养强化作物。","整合基因组、转录组、蛋白与代谢组学，结合CNN、Transformer、UAV遥","AI辅助多组学与基因编辑可提升耐逆与营养性状预测及生物强化效率。","多组学数据标准化与模型可解释性不足，可研究跨物种可迁移的AI决策框架。","智慧农业 \u002F 农业物联网","2026-09-16T23:30:07.496425Z",{"id":163,"title":164,"url":165,"summary":166,"summary_zh":167,"content":8,"source_name":168,"source_url":165,"published_at":169,"category":11,"cover_url":8,"hotness":170,"is_selected":13,"score":171,"score_detail":172,"sources":175,"tags":183,"search_phrases":186,"slug":189,"view_count":35,"doi":190,"paper":191,"created_at":203},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-30T00:00:00Z",55,71,{"impact":57,"substance":17,"depth":18,"authority":19,"freshness":173,"relevant":21,"comment":174},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。",[176,177,179,181],{"name":168,"url":165},{"name":168,"url":178},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22724438",{"name":168,"url":180},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22767935",{"name":168,"url":182},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22767934",[64,26,28,184,185],"遥感监测","作物育种",[187,188],"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":190,"openalex_id":192,"authors":193,"venue":168,"cited_by_count":35,"oa_url":165,"card":198,"direction":119,"ingested_from":121},"W7212377743",[194,196],{"name":195,"orcid":8},"Aruna Nangare",{"name":197,"orcid":8},"Vaishali Wankhede",{"tldr":199,"method":200,"finding":201,"direction":119,"opportunity":202},"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。","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":205,"title":206,"url":207,"summary":208,"summary_zh":209,"content":8,"source_name":210,"source_url":207,"published_at":211,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":212,"score_detail":213,"sources":217,"tags":219,"search_phrases":222,"slug":225,"view_count":35,"doi":226,"paper":227,"created_at":243},1723,"Bridging challenges with technology: A scientometric and systematic study on sustainability in smart agriculture","https:\u002F\u002Fdoi.org\u002F10.59953\u002Fpaperasia.v42i4b.1489","The rapid evolution of agricultural practices driven by population growth, climate change, and technological advancements has necessitated the integration of smart technologies to enhance productivity and sustainability. This study aims to achieve two research objectives: (1) to provide a comprehensive overview of research patterns and key contributors in the domain of technology integration in smart agriculture, and (2) to evaluate the advantages of integrating technology in smart agriculture to address existing challenges. A scientometric analysis and a systematic literature review (SLR) were employed to examine the research landscape and synthesize current evidence. Following the PRISMA screening process, 18 articles were retained for the final systematic review. The findings reveal the transformative role of the Internet of Things (IoT), artificial intelligence (AI), blockchain, and unmanned aerial vehicles (UAVs) in improving resource management, reducing labour costs, enhancing real-time monitoring, and strengthening farm safety. The scientometric analysis further highlights global research collaborations, influential contributors, and emerging technological trends shaping the field of smart agriculture. These findings demonstrate the significant potential of advanced technologies to address contemporary agricultural challenges while promoting sustainable and resilient farming practices worldwide.","人口增长、气候变化和技术进步推动农业实践快速演变，这要求整合智能技术以提升生产力和可持续性。本研究旨在实现两个研究目标：（1）全面概述智能农业技术整合领域的研究模式及主要贡献者；（2）评估智能农业中技术整合的优势，以应对现有挑战。采用科学计量分析和系统文献综述（SLR）来考察研究格局并综合现有证据。经过PRISMA筛选流程，最终纳入18篇文章进行系统综述。研究结果揭示了物联网（IoT）、人工智能（AI）、区块链和无人机（UAVs）在改善资源管理、降低劳动力成本、增强实时监测及强化农场安全方面的变革性作用。科学计量分析进一步凸显了全球研究合作、有影响力的贡献者以及塑造智能农业领域的新兴技术趋势。这些发现表明，先进技术在应对当代农业挑战、同时促进全球可持续和韧性农业实践方面具有巨大潜力。","PaperAsia","2026-09-04T00:00:00Z",56,{"impact":16,"substance":57,"depth":214,"authority":20,"freshness":215,"relevant":21,"comment":216},16,2,"系统综述与科学计量分析，梳理智慧农业技术应用与挑战，但时效性较低。",[218],{"name":210,"url":207},[64,220,26,221,27],"无人机","物联网",[223,224],"农业人工智能 智慧农业 区块链 无人机","农业人工智能 智慧农业","农业人工智能智慧农业区块链无人机-1723","10.59953\u002Fpaperasia.v42i4b.1489",{"doi":226,"openalex_id":228,"authors":229,"venue":210,"cited_by_count":35,"oa_url":207,"card":238,"direction":160,"ingested_from":121},"W7208757733",[230,232,234,236],{"name":231,"orcid":8},"Nor Suzylah Sohaimi",{"name":233,"orcid":8},"Nor Syahidah Ishak",{"name":235,"orcid":8},"Rozaimi Majid",{"name":237,"orcid":8},"Siti Noor Zilawati Mingat@Minhad",{"tldr":239,"method":240,"finding":241,"direction":160,"opportunity":242},"通过科学计量和系统综述，评估智能农业中技术整合的研究格局与优势。","科学计量分析结合PRISMA系统文献综述，筛选18篇文章。","IoT、AI、区块链和无人机提升资源管理、降低成本、增强监测与安全。","可深入探究区块链与AI在农业中的协同应用，或针对特定作物\u002F区域的实证研究。","2026-09-05T23:30:09.377361Z",{"id":245,"title":246,"url":247,"summary":248,"summary_zh":249,"content":8,"source_name":250,"source_url":247,"published_at":251,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":252,"score_detail":253,"sources":255,"tags":257,"search_phrases":258,"slug":260,"view_count":35,"doi":261,"paper":262,"created_at":273},1419,"Smart Agriculture with Quantum Intelligence and Blockchain Security","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.84736","Selecting energy-efficient, trustworthy cluster heads (CHs) remains a key bottleneck in agricultural wireless sensor and Internet of Things (IoT) networks, where heuristic protocols such as LEACH often converge to unstable configurations and cannot verify whether a selected node is trustworthy. This paper proposes a framework that combines a Quantum-Inspired Genetic Algorithm (QIGA) for cluster head selection with a permissioned blockchain trust-verification layer. Unlike prior work that applies quantum classifiers to sensor-data classification, the proposed method encodes candidate cluster head assignments as qubit chromosomes and evolves them through quantum rotation-gate updates, treating cluster formation as a combinatorial optimization problem rather than a classification task. Verified assignments and node reputation scores are recorded as immutable blockchain transactions validated through a lightweight Byzantine fault-tolerant consensus, enabling automatic exclusion of low-trust nodes without a central authority. A discrete-event simulation comparing the proposed framework against LEACH and a classical genetic algorithm baseline shows comparable or improved energy retention and reliable detection of malicious nodes across a range of attack ratios. The results indicate that combining quantum-inspired combinatorial optimization with blockchain-verified trust offers a practical, hardware-independent pathway toward energy-aware, tamperresistant clustering for precision agriculture","在农业无线传感器与物联网（IoT）网络中，选择节能且可信的簇头（CH）仍是关键瓶颈，诸如LEACH等启发式协议往往收敛于不稳定配置，且无法验证所选节点是否可信。本文提出一个框架，将量子启发遗传算法（QIGA）用于簇头选择，并融合许可型区块链信任验证层。与以往将量子分类器应用于传感器数据分类的研究不同，本方法将候选簇头分配编码为量子比特染色体，并通过量子旋转门更新进行演化，将簇形成视为组合优化问题而非分类任务。验证后的分配结果与节点信誉评分被记录为不可篡改的区块链交易，并通过轻量级拜占庭容错共识机制加以验证，从而在无中心权威的情况下自动排除低信任节点。将所提框架与LEACH及经典遗传算法基线进行离散事件仿真对比，结果表明，在不同攻击比率下，该框架在能量保持方面表现相当或更优，并能可靠检测恶意节点。研究结果表明，将量子启发组合优化与区块链验证信任相结合，为实现精准农业中能量感知、防篡改的分簇提供了一条实用且不依赖硬件的路径。","International Journal for Research in Applied Science and Engineering Technology","2026-08-31T00:00:00Z",59,{"impact":16,"substance":57,"depth":214,"authority":12,"freshness":173,"relevant":21,"comment":254},"提出量子启发遗传算法与区块链结合的簇头选择框架，方法新颖，但尚处仿真阶段，影响有限。",[256],{"name":250,"url":247},[64,26,221,27],[259,224],"农业人工智能 智慧农业 区块链 物联网","农业人工智能智慧农业区块链物联网-1419","10.22214\u002Fijraset.2026.84736",{"doi":261,"openalex_id":263,"authors":264,"venue":250,"cited_by_count":35,"oa_url":247,"card":267,"direction":160,"ingested_from":121},"W7204875790",[265],{"name":266,"orcid":8},"Munta Padmavathi",{"tldr":268,"method":269,"finding":270,"direction":271,"opportunity":272},"提出量子启发遗传算法与区块链结合，用于农业物联网中可信且节能的簇头选择。","量子启发遗传算法（QIGA）优化簇头选择，结合许可区块链和拜占庭容错共识验证信任","相比LEACH和经典遗传算法，QIGA+区块链在能量保持和恶意节点检测上相当或更优。","其他","可探索将量子启发优化与区块链用于动态作物监测场景，或扩展到边缘计算与真实硬件部署验证。","2026-09-02T23:30:17.512521Z"]