[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5462":3,"related-5462":81},{"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":37,"classification":38,"search_phrases":66,"slug":69,"view_count":70,"doi":8,"paper":71,"created_at":80},5462,"Architecture diverse TabNet ensemble for crop type classification using harmonized multisource soil data（基于多源土壤数据融合的架构多样性TabNet集成作物类型分类）","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75366-7","对18,237条多源土壤记录、7类作物构建TabNet集成框架。TPE优化使对数损失由0.3325降至0.2566，集成后准确率0.9053、宏F1 0.9025，优于XGBoost的0.8724。作者称尚未做外部验证。",null,"Scientific Reports 2026-10-18","2026-10-18T00:00:00Z","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,"方法新颖、数据规模可观且指标有实质提升，但缺乏外部验证、属细分方法研究，公共影响有限，可作候选而非必选。",[24],{"name":9,"url":6},[26,27,28,29,30],"农业人工智能","机器学习","精准农业","土壤数据","作物分类",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":39,"locked":13,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":45},[44,50,56,61],{"code":32,"score":17,"supported":45,"title":46,"abstract":48,"tags":49},true,[47,33],"土壤",[47],[29],{"code":51,"score":52,"supported":13,"title":53,"abstract":54,"tags":55},"I2",2,[],[],[26,27],{"code":57,"score":21,"supported":13,"title":58,"abstract":59,"tags":60},"I1",[],[],[28],{"code":62,"score":21,"supported":13,"title":63,"abstract":64,"tags":65},"I3",[],[],[30],[67,68],"TabNet 作物类型分类","多源土壤数据 作物分类","TabNet作物类型分类-5462",0,{"doi":8,"openalex_id":8,"authors":72,"venue":8,"cited_by_count":70,"oa_url":8,"card":73,"direction":77,"ingested_from":79},[],{"tldr":74,"method":75,"finding":76,"direction":77,"opportunity":78},"构建架构多样性TabNet集成，用多源土壤数据分类7类作物。","18,237条多源土壤记录，TPE调参的TabNet集成，对比XGBoost。","集成后准确率0.9053、宏F1 0.9025，优于XGBoost的0.8724。","农业人工智能与决策模型","尚未外部验证，可跨区域迁移验证并融合遥感时序特征提升泛化。","agent","2026-10-21T00:10:08.428584Z",{"total":82,"page":21,"page_size":82,"items":83},6,[84,165,301,360,410,493],{"id":85,"title":86,"url":87,"summary":88,"summary_zh":89,"content":8,"source_name":90,"source_url":87,"published_at":91,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":92,"score_detail":93,"sources":98,"tags":100,"primary_topic":103,"secondary_topics":108,"classification":115,"search_phrases":144,"slug":147,"view_count":52,"doi":148,"paper":149,"created_at":164},2509,"Artificial Intelligence-Based Crop Recommendation Using Soil and Climate Data: A Comprehensive Review of Machine Learning, Deep Learning, and Smart Agriculture Approaches","https:\u002F\u002Fdoi.org\u002F10.64388\u002Firev10i3-1722922","Crop recommendation systems that integrate soil and climate data with artificial intelligence (AI) have expanded rapidly since 2020, spanning classical machine learning (ML), ensemble methods, deep learning, explainable AI (XAI), and Internet of Things (IoT)-enabled sensing. This review critically synthesizes 35 sources — 33 peer-reviewed journal articles and conference papers plus 2 preprints retained only for background context — comprising 12 studies verified in full against their primary text and 23 studies verified at the bibliographic level, to examine what has been attempted, which data and algorithms have been used, and how reliable the reported results are. The reviewed literature shows convergent use of a narrow feature set (nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall) and recurring near-ceiling accuracy, including cases at or above 98% [1], [5], [6] and, in one case, a reported 1.00 across accuracy, precision, recall, and F1-score following class-balancing [7]. Cross-examination of dataset descriptions across studies reveals inconsistent provenance and documentation: structurally similar seven-feature datasets are described with different national contexts and reported sample sizes ranging from 2,100 to 3,000 records [5], [6], [9]. Validation practice is dominated by random hold-out or k-fold cross-validation; among the studies examined in full, only one tested spatial cross-validation, reporting a substantial performance decline (AUC 0.89 to 0.55–0.62) relative to random-split results [4]. Explainable AI and uncertainty quantification remain minority practices in the reviewed literature. A prior conference proceedings paper self-described as a","自2020年以来，将土壤和气候数据与人工智能（AI）相结合的作物推荐系统迅速扩展，涵盖经典机器学习（ML）、集成方法、深度学习、可解释人工智能（XAI）以及物联网（IoT）赋能的传感技术。本综述批判性地综合了35个来源——33篇同行评审期刊论文和会议论文，以及2篇仅用于背景参考的预印本——其中包括12项经全文核实的研究和23项经书目层面核实的研究，以考察已尝试的研究方向、所使用的数据和算法，以及所报告结果的可靠性。所综述的文献显示，特征集使用趋同且范围狭窄（氮、磷、钾、温度、湿度、pH和降雨量），准确率反复接近上限，包括达到或超过98%的案例[1], [5], [6]，以及一例在类别平衡后准确率、精确率、召回率和F1分数均报告为1.00的研究[7]。对各项研究中数据集描述的交叉审查揭示了来源和文档记录的不一致：结构相似的七特征数据集被描述为不同的国家背景，报告的样本量从2,100到3,000条记录不等[5], [6], [9]。验证实践以随机留出法或k折交叉验证为主；在经全文审查的研究中，仅有一项测试了空间交叉验证，报告称相对于随机划分结果，性能显著下降（AUC从0.89降至0.55–0.62）[4]。可解释人工智能和不确定性量化在所综述文献中仍属少数实践。一篇先前会议论文集中自述为","Iconic Research and Engineering Journals","2026-09-14T00:00:00Z",61,{"impact":16,"substance":94,"depth":95,"authority":82,"freshness":96,"relevant":21,"comment":97},18,16,9,"系统综述揭示作物推荐模型普遍存在数据集来源混乱与验证方法单一（仅一项空间交叉验证即大幅掉点）的问题，对智慧农业AI落地有实质警示价值，但期刊影响力有限。",[99],{"name":90,"url":87},[101,26,28,102,29],"智慧农业","作物推荐",{"code":51,"slug":104,"name":105,"path":106,"parent_code":107},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[109,110],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":111,"slug":112,"name":113,"path":114,"parent_code":107},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":39,"locked":13,"version":40,"method":41,"evidence":116},{"candidates":117,"agricultural_evidence":45},[118,127,132,137],{"code":51,"score":119,"supported":45,"title":120,"abstract":124,"tags":126},34,[121,122,123],"machine learning","deep learning","artificial intelligence",[125,27,121,122,123],"深度学习",[26],{"code":32,"score":128,"supported":45,"title":129,"abstract":130,"tags":131},15,[33],[47,33],[29],{"code":57,"score":12,"supported":45,"title":133,"abstract":135,"tags":136},[134],"smart agriculture",[],[101,28],{"code":111,"score":96,"supported":45,"title":138,"abstract":139,"tags":143},[],[140,141,142],"物联网","internet of things","IoT",[],[145,146],"农业人工智能 作物推荐 土壤数据 智慧农业","农业人工智能 作物推荐","农业人工智能作物推荐土壤数据智慧农业-2509","10.64388\u002Firev10i3-1722922",{"doi":148,"openalex_id":150,"authors":151,"venue":90,"cited_by_count":70,"oa_url":156,"card":157,"direction":162,"ingested_from":163},"W7212594050",[152,154],{"name":153,"orcid":8},"Snehal Sanjay Raut",{"name":155,"orcid":8},"Yogesh V. Chimate","https:\u002F\u002Fwww.irejournals.com\u002Fformatedpaper\u002F1722922.pdf",{"tldr":158,"method":159,"finding":160,"direction":77,"opportunity":161},"综述AI作物推荐研究，指出高准确率多源于数据与验证缺陷。","系统综述35篇文献，对比ML、DL、XAI与IoT方法及验证方式。","常用7特征数据集来源不一，随机验证致准确率虚高，空间验证性能骤降。","需建立标准化数据集并推广空间交叉验证与不确定性量化，提升模型真实泛化能力。","智慧农业 \u002F 农业物联网","openalex","2026-09-15T23:30:08.397298Z",{"id":166,"title":167,"url":168,"summary":169,"summary_zh":170,"content":8,"source_name":171,"source_url":168,"published_at":172,"category":11,"cover_url":8,"hotness":12,"is_selected":45,"score":173,"score_detail":174,"sources":179,"tags":181,"primary_topic":185,"secondary_topics":186,"classification":187,"search_phrases":266,"slug":269,"view_count":70,"doi":270,"paper":271,"created_at":300},4832,"Applications and Future Prospects of Plant Multi-Omics Technologies in Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fijms27209034","Against the backdrop of global population growth, intensifying climate change, and limited arable land, ensuring food security and promoting sustainable agriculture have become critical issues. The limitations of conventional agricultural systems are becoming increasingly apparent, creating an urgent need for innovation driven by science and technology. In this context, plant multi-omics technologies serve as the foundational data and technical cornerstone of systems biology and precision agriculture. Integrating multi-level data from genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics offers substantial advantages for systematically understanding and improving plants. Unlike previous reviews that mainly catalog omics technologies or focus on individual crops, this review adopts a unified discovery-to-translation framework and critically evaluates the strength of evidence across applications. This review focuses on cutting-edge applications of multi-omics in three key areas: improving the traits of cereal crops (rice, wheat and maize), remediating soil health, and innovating products such as biofertilizers and biostimulants for agriculture. We systematically explore the roles of multi-omics in accelerating breeding processes, deciphering complex agronomic traits, restoring soil ecosystems, and constructing intelligent decision-support systems. The review also examines current challenges, including the complexity of data integration and the difficulties of predicting genotype-by-environment interactions. Furthermore, it outlines future directions involving deeper convergence with artificial intelligence, high-throughput phenomics, and digital twins. In summary, plant multi-omics technology is a vital tool for unravelling the mysteries of plant life and is also a key enabler of intelligent, precise and sustainable agricultural transformation.","在全球人口增长、气候变化加剧和耕地有限的背景下，保障粮食安全和促进可持续农业已成为关键议题。传统农业系统的局限性日益显现，迫切需要由科学和技术驱动的创新。在此背景下，植物多组学技术作为系统生物学和精准农业的基础数据与技术基石，整合基因组学、转录组学、蛋白质组学、代谢组学、表观基因组学和微生物组学等多层次数据，为系统理解和改良植物提供了显著优势。与以往主要罗列组学技术或聚焦单一作物的综述不同，本综述采用统一的“从发现到转化”框架，并批判性评估各应用中的证据强度。本综述聚焦多组学在三个关键领域的前沿应用：改良谷类作物（水稻、小麦和玉米）的性状、修复土壤健康，以及创新生物肥料和生物刺激素等农业产品。我们系统探讨了多组学在加速育种进程、解析复杂农艺性状、恢复土壤生态系统和构建智能决策支持系统中的作用。本综述还审视了当前挑战，包括数据整合的复杂性和预测基因型与环境互作的困难。此外，文章展望了未来方向，涉及与人工智能、高通量表型组学和数字孪生的更深层次融合。总之，植物多组学技术是揭示植物生命奥秘的重要工具，也是智能、精准和可持续农业转型的关键使能技术。","International Journal of Molecular Sciences","2026-10-12T00:00:00Z",83,{"impact":175,"substance":176,"depth":94,"authority":177,"freshness":20,"relevant":21,"comment":178},22,21,14,"核心期刊综述，系统梳理植物多组学在精准农业中的应用与AI、数字孪生等未来方向，专业深度与信源权威度俱佳，值得进入每日精选。",[180],{"name":171,"url":168},[101,26,182,28,183,184],"种业振兴","土壤健康","多组学",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":188},{"candidates":189,"agricultural_evidence":45},[190,197,202,209,216,223,230,237,244,249,256,261],{"code":57,"score":191,"supported":45,"title":192,"abstract":194,"tags":196},19,[193],"precision agriculture",[28,195,193],"数字孪生",[101,28],{"code":32,"score":198,"supported":45,"title":199,"abstract":200,"tags":201},7,[],[47,33],[183],{"code":203,"score":82,"supported":45,"title":204,"abstract":205,"tags":208},"A1",[],[206,207],"水稻","rice",[],{"code":210,"score":82,"supported":45,"title":211,"abstract":212,"tags":215},"A2",[],[213,214],"小麦","wheat",[],{"code":217,"score":82,"supported":45,"title":218,"abstract":219,"tags":222},"A3",[],[220,221],"玉米","maize",[],{"code":224,"score":82,"supported":45,"title":225,"abstract":226,"tags":229},"A8",[],[227,228],"粮食安全","food security",[],{"code":231,"score":82,"supported":45,"title":232,"abstract":233,"tags":236},"H7",[],[234,235],"气候变化","climate change",[],{"code":238,"score":82,"supported":45,"title":239,"abstract":240,"tags":243},"H8",[],[241,242],"可持续农业","sustainable agriculture",[],{"code":51,"score":245,"supported":45,"title":246,"abstract":247,"tags":248},4,[],[123],[26],{"code":250,"score":251,"supported":45,"title":252,"abstract":253,"tags":255},"F3",3,[],[254],"肥料",[],{"code":257,"score":21,"supported":13,"title":258,"abstract":259,"tags":260},"F1",[],[],[182],{"code":262,"score":21,"supported":13,"title":263,"abstract":264,"tags":265},"F2",[],[],[184],[267,268],"植物多组学 精准农业","多组学 智能育种","植物多组学精准农业-4832","10.3390\u002Fijms27209034",{"doi":270,"openalex_id":272,"authors":273,"venue":171,"cited_by_count":70,"oa_url":168,"card":294,"direction":299,"ingested_from":163},"W7221567543",[274,277,279,282,284,286,289,292],{"name":275,"orcid":276},"Tao Tong","https:\u002F\u002Forcid.org\u002F0000-0002-9003-9667",{"name":278,"orcid":8},"Wang Qingxia",{"name":280,"orcid":281},"Fanrong Zeng","https:\u002F\u002Forcid.org\u002F0000-0001-7742-5195",{"name":283,"orcid":8},"Shuzhen Ye",{"name":285,"orcid":8},"Zhijuan Ji",{"name":287,"orcid":288},"Zhong‐Hua Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7531-320X",{"name":290,"orcid":291},"Jiaoyu Wang","https:\u002F\u002Forcid.org\u002F0000-0002-0249-808X",{"name":293,"orcid":8},"Younan Ouyang",{"tldr":295,"method":296,"finding":297,"direction":77,"opportunity":298},"综述植物多组学在精准农业中的应用，涵盖作物改良、土壤修复和生物产品创新。","整合基因组、转录组、蛋白组、代谢组等多组学数据，采用发现到转化框架。","多组学可加速育种、解析复杂农艺性状、修复土壤生态并构建智能决策系统。","多组学与AI、高通量表型、数字孪生融合，解决基因型-环境互作预测难题。","数字乡村与农业信息化","2026-10-13T23:30:29.504594Z",{"id":302,"title":303,"url":304,"summary":305,"summary_zh":306,"content":8,"source_name":307,"source_url":304,"published_at":308,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":309,"score_detail":310,"sources":312,"tags":314,"primary_topic":316,"secondary_topics":317,"classification":319,"search_phrases":340,"slug":343,"view_count":251,"doi":344,"paper":345,"created_at":359},4374,"AI-Driven Crop Recommendation and Growth Prediction System Using Soil Fertility and Climate Intelligence","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23179023","Agricultural decisions are increasingly affected by changing climate conditions, soil fertility, and the availability of timely technical guidance. This mini project presents an AI-Driven Agricultural Intelligence System that combines crop recommendation, soil fertility assessment, weather-risk prediction, yield estimation, fertilizer recommendation, and multilingual conversational support in one web platform. The system accepts soil and climate parameters such as nitrogen, phosphorus, potassium, pH, temperature, humidity, rainfall, location, crop, and season. The supplied implementation uses a Random Forest model for crop recommendation, XGBoost for weather-risk prediction, a Random Forest regressor for yield estimation, a Random Forest classifier for fertilizer recommendation, and a deterministic rule-based soilfertility module with model-independent fallback logic. A FastAPI backend performs validation, model loading, chained inference, weather integration, authentication, and response formatting, while a React-based Progressive Web Application provides the user interface. OpenWeatherMap supplies current weather context and Google Gemini AI supports multilingual agricultural explanations with offline FAQ and rulebased fallbacks. Reported module performance is approximately 88-95%, and representative API, fallback, authentication, translation, rate-limiting, and chatbot tests pass in the supplied project material. The project demonstrates how machine learning, web development, external APIs, and multilingual AI can be combined to deliver practical, accessible, and data-driven agricultural decision support. Keywords: Machine Learning, Agriculture, Crop Recommendation, Soil Fertility, Weather Risk, Yield Prediction, Fertilizer Recommendation, FastAPI, React, XGBoost, Google Gemini AI, Precision Agriculture.","农业决策日益受到气候条件变化、土壤肥力以及及时技术指导可得性的影响。本小型项目提出了一套人工智能驱动的农业智能系统，将作物推荐、土壤肥力评估、天气风险预测、产量估算、肥料推荐和多语言对话支持集成于一个网络平台。该系统接受土壤和气候参数，如氮、磷、钾、pH值、温度、湿度、降雨量、地点、作物和季节。所提供的实现采用随机森林（Random Forest）模型进行作物推荐，采用XGBoost进行天气风险预测，采用随机森林回归器进行产量估算，采用随机森林分类器进行肥料推荐，并采用基于确定性规则的土壤肥力模块，具备与模型无关的回退逻辑。FastAPI后端执行验证、模型加载、链式推理、天气集成、身份认证和响应格式化，而基于React的渐进式网络应用提供用户界面。OpenWeatherMap提供当前天气上下文，Google Gemini AI支持多语言农业解释，并具备离线FAQ和基于规则的回退机制。所报告的模块性能约为88-95%，且在所提供的项目材料中，具有代表性的API、回退、身份认证、翻译、速率限制和聊天机器人测试均通过。该项目展示了如何将机器学习、网络开发、外部API和多语言AI相结合，以提供实用、可及且数据驱动的农业决策支持。关键词：机器学习、农业、作物推荐、土壤肥力、天气风险、产量预测、肥料推荐、FastAPI、React、XGBoost、Google Gemini AI、精准农业。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-06T00:00:00Z",50,{"impact":20,"substance":177,"depth":19,"authority":82,"freshness":96,"relevant":21,"comment":311},"学生级迷你项目论文，方法组合常规、性能数据自报，工程整合有一定参考价值但学术与产业增量有限，可作智慧农业应用案例收录。",[313],{"name":307,"url":304},[101,26,28,102,315],"土壤肥力",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[318],{"code":51,"slug":104,"name":105,"path":106,"parent_code":107},{"status":39,"locked":13,"version":40,"method":41,"evidence":320},{"candidates":321,"agricultural_evidence":45},[322,326,331,335],{"code":32,"score":128,"supported":45,"title":323,"abstract":324,"tags":325},[33],[47,33],[315],{"code":51,"score":12,"supported":45,"title":327,"abstract":328,"tags":330},[],[27,121,329],"decision support",[26],{"code":57,"score":20,"supported":45,"title":332,"abstract":333,"tags":334},[],[28,193],[101,28],{"code":250,"score":82,"supported":45,"title":336,"abstract":337,"tags":339},[],[254,338],"fertilizer",[],[341,342],"AI 作物推荐 土壤肥力","XGBoost 天气风险 产量预测","AI作物推荐土壤肥力-4374","10.5281\u002Fzenodo.23179023",{"doi":344,"openalex_id":346,"authors":347,"venue":307,"cited_by_count":70,"oa_url":304,"card":354,"direction":77,"ingested_from":163},"W7220664944",[348,350,352],{"name":349,"orcid":8},"Gunashekar B.",{"name":351,"orcid":8},"Saiteja G.",{"name":353,"orcid":8},"Maniteja K.",{"tldr":355,"method":356,"finding":357,"direction":77,"opportunity":358},"构建集成作物推荐、产量预测、肥料建议与多语言问答的AI农业决策Web平台。","随机森林、XGBoost、FastAPI、React PWA、OpenWeath","各模块准确率约88-95%，API、认证、翻译与聊天机器人测试均通过。","可引入真实田间时序数据与可解释性，验证模型跨区域跨季节泛化能力。","2026-10-07T23:30:47.248380Z",{"id":361,"title":362,"url":363,"summary":364,"summary_zh":8,"content":8,"source_name":365,"source_url":8,"published_at":366,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":367,"score_detail":368,"sources":370,"tags":372,"primary_topic":375,"secondary_topics":376,"classification":378,"search_phrases":398,"slug":401,"view_count":245,"doi":8,"paper":402,"created_at":409},4286,"Applications of Artificial Intelligence in Soil Characterization and Agriculture: A Systematic Review","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37386408","Navarro Rubio等系统综述2018-2026年196项AI土壤特性与农业应用研究，遵循PRISMA 2020框架。综述发现研究明显向数据驱动方法转变，机器学习和深度学习模型主导近期研究；随机森林、支持向量机、梯度提升方法、人工神经网络、卷积神经网络、长短期记忆架构最常被报告；主要数据源包括原位传感、实验室测量、遥感影像和环境协变量，常通过多源数据融合框架集成；树集成模型在不同土壤特性上提供稳健性能。","Agronomy 2026 16(13):1241 DOI:10.3390\u002Fagronomy16131241","2026-10-02T00:00:00Z",78,{"impact":94,"substance":175,"depth":94,"authority":177,"freshness":82,"relevant":21,"comment":369},"PRISMA框架下196项研究的系统综述，方法学扎实、结论可靠，对农业AI土壤应用具较高参考价值。",[371],{"name":365,"url":363},[101,26,27,373,374],"遥感","土壤监测",{"code":51,"slug":104,"name":105,"path":106,"parent_code":107},[377],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":13,"version":40,"method":41,"evidence":379},{"candidates":380,"agricultural_evidence":45},[381,385,390,394],{"code":51,"score":95,"supported":45,"title":382,"abstract":383,"tags":384},[123],[125,27],[26,27],{"code":32,"score":386,"supported":45,"title":387,"abstract":388,"tags":389},11,[33],[47],[],{"code":62,"score":245,"supported":45,"title":391,"abstract":392,"tags":393},[],[373],[373],{"code":57,"score":21,"supported":13,"title":395,"abstract":396,"tags":397},[],[],[101],[399,400],"AI 土壤特性 系统综述","机器学习 土壤 遥感","AI土壤特性系统综述-4286",{"doi":8,"openalex_id":8,"authors":403,"venue":8,"cited_by_count":70,"oa_url":8,"card":404,"direction":77,"ingested_from":79},[],{"tldr":405,"method":406,"finding":407,"direction":77,"opportunity":408},"系统综述2018-2026年196项AI用于土壤特性与农业的研究。","PRISMA 2020框架，分析机器学习与深度学习模型及多源数据融合。","研究转向数据驱动，树集成模型在不同土壤特性上表现稳健。","可探索可解释AI与多源数据融合在土壤特性预测中的泛化能力。","2026-10-06T00:05:04.976398Z",{"id":411,"title":412,"url":413,"summary":414,"summary_zh":415,"content":8,"source_name":416,"source_url":413,"published_at":417,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":418,"score_detail":419,"sources":421,"tags":423,"primary_topic":425,"secondary_topics":426,"classification":432,"search_phrases":475,"slug":478,"view_count":21,"doi":479,"paper":480,"created_at":492},4073,"Intelligent soil monitoring system for sustainable agriculture using TinyML and ultra-low power wireless sensor networks","https:\u002F\u002Fdoi.org\u002F10.35784\u002Fiapgos.9467","Precision agriculture demands continuous, energy-autonomous soil monitoring capable of detecting critical conditions such as rapid moisture loss or nutrient depletion approaching crop-threatening thresholds. Conventional wireless sensor networks face a fundamental trade-off between multi-year battery life and sub-second response latency, as duty-cycled polling wastes energy on idle queries while always-on communication remains infeasible for battery-powered field deployments spanning hundreds of acres. This work presents a novel integration of TinyML-based edge inference with a custom-designed 40 nm CMOS ultra-low power wake-up receiver (WuRX), enabling event-driven, asynchronous soil condition monitoring without reliance on periodic polling schedules. The proposed three-tier edge-fog-cloud architecture employs sensor nodes measuring moisture, temperature, pH, nitrogen, phosphorus, and potassium, with quantized neural networks trained and validated on 64,800 labelled samples collected from instrumented corn and soybean field deployments over 180 days, achieving a classification accuracy of 91.3% and F1-score of 93.0% at an inference energy cost of 0.87 mJ per cycle. Circuit-level simulations of the 40 nm CMOS WuRX confirm an input return loss of -15.8 dB, transmission gain of +18.2 dB, and power-added efficiency of 45.3% at -60 dBm sensitivity, with continuous standby consumption of 45–53 µW. System-level power budget analysis validates battery lifetimes of 200–300 days using 240 mAh lithium cells augmented by a 5 cm² monocrystalline solar harvester. Key limitations include the restriction of field validation to two crop types and dependence on adequate solar irradiance for energy-neutral operation. The proposed architecture reduces communication overhead by 80–90% relative to threshold-based systems, providing a practical framework for scalable, resource-constrained precision agriculture deployments.","精准农业需要能够持续、能源自主地监测土壤，以检测诸如水分快速流失或养分耗竭接近威胁作物阈值等关键状况。传统无线传感器网络面临多年电池寿命与亚秒级响应延迟之间的根本性权衡，因为占空比轮询会在空闲查询上浪费能量，而始终在线的通信对于覆盖数百英亩的电池供电田间部署而言仍不可行。本研究提出了一种新颖的集成方案，将基于TinyML的边缘推理与定制设计的40 nm CMOS超低功耗唤醒接收器（WuRX）相结合，实现事件驱动的异步土壤状况监测，而无需依赖周期性轮询调度。所提出的三层边缘-雾-云架构采用测量水分、温度、pH、氮、磷和钾的传感器节点，其量化神经网络在180天内从仪器化玉米和大豆田间部署中采集的64,800个标记样本上进行训练和验证，在每次推理周期能耗为0.87 mJ的情况下，实现了91.3%的分类准确率和93.0%的F1分数。对40 nm CMOS WuRX的电路级仿真证实，在-60 dBm灵敏度下，输入回波损耗为-15.8 dB，传输增益为+18.2 dB，功率附加效率为45.3%，连续待机功耗为45–53 µW。系统级功率预算分析验证了使用240 mAh锂电池并辅以5 cm²单晶硅太阳能收集器时，电池寿命可达200–300天。主要局限性包括田间验证仅限于两种作物类型，以及能量中性运行依赖充足的太阳辐照度。与基于阈值的系统相比，所提出的架构将通信开销降低了80–90%，为可扩展、资源受限的精准农业部署提供了实用框架。","Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska","2026-09-30T00:00:00Z",80,{"impact":94,"substance":175,"depth":191,"authority":19,"freshness":20,"relevant":21,"comment":420},"将TinyML边缘推理与40nm CMOS超低功耗唤醒接收机结合，实现事件驱动型土壤监测，实测数据扎实、能耗指标明确，对精准农业物联网部署有参考价值。",[422],{"name":416,"url":413},[101,26,28,424,374],"无线传感器网络",{"code":111,"slug":112,"name":113,"path":114,"parent_code":107},[427,428],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":238,"slug":429,"name":430,"path":431,"parent_code":36},"sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture",{"status":39,"locked":13,"version":40,"method":41,"evidence":433},{"candidates":434,"agricultural_evidence":45},[435,441,445,449,453,458,465,471],{"code":111,"score":128,"supported":45,"title":436,"abstract":438,"tags":440},[437],"wireless sensor",[439,437],"无线传感",[424],{"code":32,"score":177,"supported":45,"title":442,"abstract":443,"tags":444},[33],[47,33],[],{"code":238,"score":20,"supported":45,"title":446,"abstract":447,"tags":448},[242],[],[],{"code":57,"score":20,"supported":45,"title":450,"abstract":451,"tags":452},[],[28,193],[101,28],{"code":217,"score":82,"supported":45,"title":454,"abstract":455,"tags":457},[],[220,456],"corn",[],{"code":459,"score":82,"supported":45,"title":460,"abstract":461,"tags":464},"A4",[],[462,463],"大豆","soybean",[],{"code":466,"score":251,"supported":45,"title":467,"abstract":468,"tags":470},"F5",[],[469],"harvester",[],{"code":51,"score":21,"supported":13,"title":472,"abstract":473,"tags":474},[],[],[26],[476,477],"TinyML 土壤监测","超低功耗 唤醒接收机 精准农业","TinyML土壤监测-4073","10.35784\u002Fiapgos.9467",{"doi":479,"openalex_id":481,"authors":482,"venue":416,"cited_by_count":70,"oa_url":486,"card":487,"direction":162,"ingested_from":163},"W7214927134",[483],{"name":484,"orcid":485},"Muhammad Ovais Akhter","https:\u002F\u002Forcid.org\u002F0000-0002-8928-4489","https:\u002F\u002Fph.pollub.pl\u002Findex.php\u002Fiapgos\u002Farticle\u002Fdownload\u002F9467\u002F5631",{"tldr":488,"method":489,"finding":490,"direction":162,"opportunity":491},"提出TinyML边缘推理与超低功耗唤醒接收机集成的土壤监测系统，实现事件驱动异步监测。","TinyML量化神经网络、40nm CMOS唤醒接收机、三层边缘-雾-云架构，6","分类准确率91.3%，通信开销降低80-90%，电池寿命200-300天。","可探索多作物多土壤类型泛化、无光照或弱光下能量收集与事件触发协同优化。","2026-10-03T23:30:17.793958Z",{"id":494,"title":495,"url":496,"summary":497,"summary_zh":498,"content":8,"source_name":499,"source_url":496,"published_at":500,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":501,"score_detail":502,"sources":504,"tags":506,"primary_topic":509,"secondary_topics":510,"classification":512,"search_phrases":542,"slug":545,"view_count":245,"doi":546,"paper":547,"created_at":570},3516,"Approaches to forecast soil nutrient dynamics for precision agriculture and sustainable fertiliser management: A review","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.16160","Predictive modelling of soil nutrient dynamics is an essential tool for promoting sustainable agricultural practices and environmentally responsible farming methods. The statistical and machine learning techniques used to forecast the availability and dynamics of soil nutrients are summarised in this review. The core frameworks for measuring spatio-temporal nutritional variability are established by traditional statistical approaches such as time-series models autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), multivariate techniques (Principal component analysis (PCA) and factor analysis) and geostatistical tools (kriging). By capturing intricate nonlinear interactions within heterogeneous agroecosystems, machine learning techniques like random forest, support vector machines and ensemble approaches (XGBoost, LightGBM and AdaBoost) provide higher prediction accuracy. Forecasting capabilities are further enhanced by hybrid frameworks [Autoregressive integrated moving average with exogenous variables–artificial neural network. (ARIMAX-ANN)] and deep learning architectures (Convolutional neural network (CNN), long short-term memory (LSTM), ANN). With R2 values above 0.93 and notable decreases in prediction errors, ensemble approaches routinely perform better than traditional linear models. Nevertheless, persistent challenges include data quality limitations, spatial sampling constraints, insufficient environmental covariates and reduced model transferability across diverse pedoclimatic regions. Integrating high-resolution soil properties, climatic variables, terrain attributes and spectral information with advanced modelling architectures remains crucial for enhancing predictive reliability, ultimately supporting precision nutrient management, improved fertiliser efficiency and environmentally responsible agricultural systems.","土壤养分动态的预测建模是推动可持续农业实践和环境友好型耕作方法的重要工具。本综述总结了用于预测土壤养分有效性及其动态变化的统计与机器学习技术。传统统计方法，如时间序列模型自回归积分滑动平均模型（ARIMA）、季节性自回归积分滑动平均模型（SARIMA）、多变量技术（主成分分析（PCA）和因子分析）以及地统计工具（克里金法），为量化养分的时空变异性奠定了核心框架。通过捕捉异质性农业生态系统中复杂的非线性相互作用，随机森林、支持向量机和集成方法（XGBoost、LightGBM和AdaBoost）等机器学习技术可实现更高的预测精度。混合框架[含外生变量的自回归积分滑动平均模型–人工神经网络（ARIMAX-ANN）]和深度学习架构（卷积神经网络（CNN）、长短期记忆网络（LSTM）、人工神经网络（ANN））进一步增强了预测能力。集成方法的R²值超过0.93，且预测误差显著降低，其表现通常优于传统线性模型。然而，持续存在的挑战包括数据质量限制、空间采样约束、环境协变量不足以及模型在不同土壤气候区域间可迁移性降低等问题。将高分辨率土壤属性、气候变量、地形属性和光谱信息与先进建模架构相结合，对于提高预测可靠性仍然至关重要，最终可为精准养分管理、提高肥料利用效率以及环境友好型农业系统提供支撑。","Plant Science Today","2026-09-24T00:00:00Z",79,{"impact":94,"substance":175,"depth":94,"authority":19,"freshness":20,"relevant":21,"comment":503},"系统综述土壤养分动态预测的统计与机器学习方法，方法体系完整、结论有量化支撑，对精准施肥与农业信息化有参考价值，但属综述类论文，产业级影响有限。",[505],{"name":499,"url":496},[101,507,27,28,508],"变量施肥","土壤养分",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[511],{"code":51,"slug":104,"name":105,"path":106,"parent_code":107},{"status":39,"locked":13,"version":40,"method":41,"evidence":513},{"candidates":514,"agricultural_evidence":45},[515,519,523,527,532,538],{"code":32,"score":128,"supported":45,"title":516,"abstract":517,"tags":518},[33],[47,33],[508],{"code":57,"score":12,"supported":45,"title":520,"abstract":521,"tags":522},[193],[],[101,28],{"code":51,"score":12,"supported":45,"title":524,"abstract":525,"tags":526},[],[125,27,121,122],[27],{"code":250,"score":198,"supported":45,"title":528,"abstract":529,"tags":531},[],[254,530],"养分管理",[507],{"code":533,"score":251,"supported":45,"title":534,"abstract":535,"tags":537},"H6",[],[536],"农业生态",[],{"code":238,"score":251,"supported":45,"title":539,"abstract":540,"tags":541},[],[241],[],[543,544],"土壤养分 预测模型 精准农业","机器学习 施肥管理 可持续农业","土壤养分预测模型精准农业-3516","10.14719\u002Fpst.16160",{"doi":546,"openalex_id":548,"authors":549,"venue":499,"cited_by_count":70,"oa_url":496,"card":565,"direction":77,"ingested_from":163},"W7214167059",[550,553,556,559,562],{"name":551,"orcid":552},"R Rathna","https:\u002F\u002Forcid.org\u002F0009-0004-7797-2673",{"name":554,"orcid":555},"B Sivasankari","https:\u002F\u002Forcid.org\u002F0000-0001-9921-8170",{"name":557,"orcid":558},"R. Gangai Selvi","https:\u002F\u002Forcid.org\u002F0000-0002-4475-2293",{"name":560,"orcid":561},"J Prabhakaran","https:\u002F\u002Forcid.org\u002F0000-0001-7339-175X",{"name":563,"orcid":564},"K. G. Sabarinathan","https:\u002F\u002Forcid.org\u002F0000-0002-8659-6479",{"tldr":566,"method":567,"finding":568,"direction":77,"opportunity":569},"综述土壤养分动态预测的统计与机器学习方法，比较精度与局限。","综述ARIMA、地统计、随机森林、XGBoost、CNN\u002FLSTM及混合模型。","集成与深度学习模型精度更高（R²>0.93），但数据质量与跨区迁移性仍是瓶颈。","可研究多源遥感与气候数据融合的迁移学习模型，提升跨区域养分预测泛化能力。","2026-09-25T23:30:54.950445Z"]