[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4832":3,"related-4832":166},{"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":26,"primary_topic":33,"secondary_topics":39,"classification":40,"search_phrases":128,"slug":131,"view_count":132,"doi":133,"paper":134,"created_at":165},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。",null,"International Journal of Molecular Sciences","2026-10-12T00:00:00Z","论文",10,true,83,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},22,21,18,14,8,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"智慧农业","农业人工智能","种业振兴","精准农业","土壤健康","多组学",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":41,"locked":42,"version":43,"method":44,"evidence":45},"classified",false,"2026-10-22.2","rules",{"candidates":46,"agricultural_evidence":14},[47,55,61,69,76,83,90,97,104,111,118,123],{"code":48,"score":49,"supported":14,"title":50,"abstract":52,"tags":54},"I1",19,[51],"precision agriculture",[30,53,51],"数字孪生",[27,30],{"code":34,"score":56,"supported":14,"title":57,"abstract":58,"tags":60},7,[],[59,35],"土壤",[31],{"code":62,"score":63,"supported":14,"title":64,"abstract":65,"tags":68},"A1",6,[],[66,67],"水稻","rice",[],{"code":70,"score":63,"supported":14,"title":71,"abstract":72,"tags":75},"A2",[],[73,74],"小麦","wheat",[],{"code":77,"score":63,"supported":14,"title":78,"abstract":79,"tags":82},"A3",[],[80,81],"玉米","maize",[],{"code":84,"score":63,"supported":14,"title":85,"abstract":86,"tags":89},"A8",[],[87,88],"粮食安全","food security",[],{"code":91,"score":63,"supported":14,"title":92,"abstract":93,"tags":96},"H7",[],[94,95],"气候变化","climate change",[],{"code":98,"score":63,"supported":14,"title":99,"abstract":100,"tags":103},"H8",[],[101,102],"可持续农业","sustainable agriculture",[],{"code":105,"score":106,"supported":14,"title":107,"abstract":108,"tags":110},"I2",4,[],[109],"artificial intelligence",[28],{"code":112,"score":113,"supported":14,"title":114,"abstract":115,"tags":117},"F3",3,[],[116],"肥料",[],{"code":119,"score":22,"supported":42,"title":120,"abstract":121,"tags":122},"F1",[],[],[29],{"code":124,"score":22,"supported":42,"title":125,"abstract":126,"tags":127},"F2",[],[],[32],[129,130],"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",0,"10.3390\u002Fijms27209034",{"doi":133,"openalex_id":135,"authors":136,"venue":10,"cited_by_count":132,"oa_url":6,"card":157,"direction":163,"ingested_from":164},"W7221567543",[137,140,142,145,147,149,152,155],{"name":138,"orcid":139},"Tao Tong","https:\u002F\u002Forcid.org\u002F0000-0002-9003-9667",{"name":141,"orcid":9},"Wang Qingxia",{"name":143,"orcid":144},"Fanrong Zeng","https:\u002F\u002Forcid.org\u002F0000-0001-7742-5195",{"name":146,"orcid":9},"Shuzhen Ye",{"name":148,"orcid":9},"Zhijuan Ji",{"name":150,"orcid":151},"Zhong‐Hua Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7531-320X",{"name":153,"orcid":154},"Jiaoyu Wang","https:\u002F\u002Forcid.org\u002F0000-0002-0249-808X",{"name":156,"orcid":9},"Younan Ouyang",{"tldr":158,"method":159,"finding":160,"direction":161,"opportunity":162},"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。","农业人工智能与决策模型","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","openalex","2026-10-13T23:30:29.504594Z",{"total":63,"page":22,"page_size":63,"items":167},[168,254,319,407,476,564],{"id":169,"title":170,"url":171,"summary":172,"summary_zh":173,"content":9,"source_name":174,"source_url":171,"published_at":175,"category":12,"cover_url":9,"hotness":13,"is_selected":42,"score":176,"score_detail":177,"sources":183,"tags":185,"primary_topic":188,"secondary_topics":189,"classification":196,"search_phrases":235,"slug":238,"view_count":22,"doi":239,"paper":240,"created_at":253},4896,"Site-Specific Precision Cover Cropping for Soil Health and Environmental Sustainability Using Remote Sensing and Multi-Criteria Analysis","https:\u002F\u002Fdoi.org\u002F10.21663\u002Feeg-d-26-00041","ABSTRACT Cover crops, which are plants or mixtures of plants grown primarily to enhance and protect soil health, are a key strategy in sustainable agriculture. Traditionally, they have been uniformly planted in a field typically during off-season when the main crops are not in competition. However, advancements in precision agriculture, particularly the integration of remote-sensing technology, demand more efficient seeding strategies based on field variability. The aims of this study were to assess the specific needs of a field in Aurora County, SD, and determine the type and optimal combinations of the cover crop mix. The cover crop species considered in this study included legumes, grasses, and brassicas. The remote-sensing data were extracted from Sentinel-2 satellite imagery. Soil samples were collected from 0 to 30 cm depth, and soil properties were measured. Additionally, topographic maps such as slope and aspect were extracted from light detection and ranging (LiDAR) data. The results of precision cover crop analyses showed that 6.5 percent (7.8 ha), 20.6 percent (24.7 ha), 38.1 percent (45.7 ha), 29.6 percent (35.5 ha), and 5.3 percent (6.3 ha) of the studied field should planted in the following mixtures of crops: rye, radish, and vetch; triticale, turnip, and vetch; triticale, radish, and clover; triticale, rapeseed, and pea; and barley, flax, and clover, respectively. The mixtures significantly improved soil properties by reducing compaction, increasing residue C\u002FN ratio, and enhancing weed suppression compared with the control. The resulting map should be interpreted as a decision-support recommendation rather than a fully validated field prescription.","摘要 覆盖作物（cover crops）是指主要为了提升和保护土壤健康而种植的植物或植物混合体，是可持续农业的一项关键策略。传统上，覆盖作物通常在非主作物竞争的季节以均匀方式播种于田间。然而，精准农业的进步，尤其是遥感技术的整合，要求基于田块变异性制定更高效的播种策略。本研究旨在评估南达科他州奥罗拉县某田块的具体需求，并确定覆盖作物混合物的类型及最优组合。本研究考虑的覆盖作物种类包括豆科、禾本科和十字花科植物。遥感数据提取自Sentinel-2卫星影像。土壤样品采集深度为0至30 cm，并测定了土壤性质。此外，从激光雷达（LiDAR）数据中提取了坡度和坡向等地形图。精准覆盖作物分析结果表明，研究田块中分别有6.5%（7.8 ha）、20.6%（24.7 ha）、38.1%（45.7 ha）、29.6%（35.5 ha）和5.3%（6.3 ha）的面积应种植以下作物混合物：黑麦、萝卜和野豌豆；小黑麦、芜菁和野豌豆；小黑麦、萝卜和三叶草；小黑麦、油菜和豌豆；以及大麦、亚麻和三叶草。与对照相比，这些混合物通过降低紧实度、提高残体碳氮比和增强杂草抑制效果，显著改善了土壤性质。所得图谱应被解读为决策支持建议，而非经过充分验证的田间处方。","Environmental and Engineering Geoscience","2026-10-13T00:00:00Z",72,{"impact":178,"substance":18,"depth":179,"authority":180,"freshness":181,"relevant":22,"comment":182},12,17,13,9,"该研究将遥感与多准则分析结合用于精准覆盖作物制图，方法新颖、数据具体，对农业信息化与可持续农业有参考价值，但属单点田间案例，影响范围有限。",[184],{"name":174,"url":171},[27,30,186,31,187],"遥感","覆盖作物",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[190],{"code":191,"slug":192,"name":193,"path":194,"parent_code":195},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":41,"locked":42,"version":43,"method":44,"evidence":197},{"candidates":198,"agricultural_evidence":14},[199,204,209,213,220,224,230],{"code":34,"score":200,"supported":14,"title":201,"abstract":202,"tags":203},15,[35],[59,35],[31],{"code":191,"score":178,"supported":14,"title":205,"abstract":207,"tags":208},[206],"remote sensing",[186],[186],{"code":48,"score":21,"supported":14,"title":210,"abstract":211,"tags":212},[],[30,51],[27,30],{"code":214,"score":63,"supported":14,"title":215,"abstract":216,"tags":219},"A4",[],[217,218],"油菜","rapeseed",[],{"code":98,"score":63,"supported":14,"title":221,"abstract":222,"tags":223},[],[101,102],[],{"code":225,"score":113,"supported":14,"title":226,"abstract":227,"tags":229},"B1",[],[228],"萝卜",[],{"code":231,"score":22,"supported":42,"title":232,"abstract":233,"tags":234},"A7",[],[],[187],[236,237],"Sentinel-2 遥感 覆盖作物","精准覆盖作物 土壤健康","Sentinel-2遥感覆盖作物-4896","10.21663\u002Feeg-d-26-00041",{"doi":239,"openalex_id":241,"authors":242,"venue":174,"cited_by_count":132,"oa_url":9,"card":247,"direction":251,"ingested_from":164},"W7221669942",[243,245],{"name":244,"orcid":9},"Ali Mirzakhani Nafchi",{"name":246,"orcid":9},"Salman Mirzaee",{"tldr":248,"method":249,"finding":250,"direction":251,"opportunity":252},"利用遥感与多准则分析为南达科他州田块制定精准覆盖作物混播方案。","Sentinel-2影像、LiDAR地形与土壤采样，结合多准则分析。","田块被划分为5个管理区，各推荐不同覆盖作物混播，显著改善土壤紧实度、残体C\u002FN比和抑草效果。","农业遥感与作物表型","可验证该决策支持地图的田间效果，并融合实时物联网数据优化动态推荐。","2026-10-14T23:30:27.077312Z",{"id":255,"title":256,"url":257,"summary":258,"summary_zh":259,"content":9,"source_name":260,"source_url":257,"published_at":261,"category":12,"cover_url":9,"hotness":13,"is_selected":42,"score":262,"score_detail":263,"sources":265,"tags":267,"primary_topic":270,"secondary_topics":271,"classification":276,"search_phrases":299,"slug":302,"view_count":113,"doi":303,"paper":304,"created_at":318},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":21,"substance":20,"depth":180,"authority":63,"freshness":181,"relevant":22,"comment":264},"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。",[266],{"name":260,"url":257},[27,28,30,268,269],"作物推荐","土壤肥力",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[272],{"code":105,"slug":273,"name":274,"path":275,"parent_code":195},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":41,"locked":42,"version":43,"method":44,"evidence":277},{"candidates":278,"agricultural_evidence":14},[279,283,290,294],{"code":34,"score":200,"supported":14,"title":280,"abstract":281,"tags":282},[35],[59,35],[269],{"code":105,"score":13,"supported":14,"title":284,"abstract":285,"tags":289},[],[286,287,288],"机器学习","machine learning","decision support",[28],{"code":48,"score":21,"supported":14,"title":291,"abstract":292,"tags":293},[],[30,51],[27,30],{"code":112,"score":63,"supported":14,"title":295,"abstract":296,"tags":298},[],[116,297],"fertilizer",[],[300,301],"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":303,"openalex_id":305,"authors":306,"venue":260,"cited_by_count":132,"oa_url":257,"card":313,"direction":161,"ingested_from":164},"W7220664944",[307,309,311],{"name":308,"orcid":9},"Gunashekar B.",{"name":310,"orcid":9},"Saiteja G.",{"name":312,"orcid":9},"Maniteja K.",{"tldr":314,"method":315,"finding":316,"direction":161,"opportunity":317},"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":320,"title":321,"url":322,"summary":323,"summary_zh":324,"content":9,"source_name":325,"source_url":322,"published_at":326,"category":12,"cover_url":9,"hotness":13,"is_selected":42,"score":327,"score_detail":328,"sources":330,"tags":332,"primary_topic":335,"secondary_topics":340,"classification":346,"search_phrases":388,"slug":391,"view_count":22,"doi":392,"paper":393,"created_at":406},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":19,"substance":17,"depth":49,"authority":180,"freshness":21,"relevant":22,"comment":329},"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。",[331],{"name":325,"url":322},[27,28,30,333,334],"无线传感器网络","土壤监测",{"code":336,"slug":337,"name":338,"path":339,"parent_code":195},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",[341,342],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"code":98,"slug":343,"name":344,"path":345,"parent_code":38},"sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture",{"status":41,"locked":42,"version":43,"method":44,"evidence":347},{"candidates":348,"agricultural_evidence":14},[349,355,359,363,367,372,378,384],{"code":336,"score":200,"supported":14,"title":350,"abstract":352,"tags":354},[351],"wireless sensor",[353,351],"无线传感",[333],{"code":34,"score":20,"supported":14,"title":356,"abstract":357,"tags":358},[35],[59,35],[],{"code":98,"score":21,"supported":14,"title":360,"abstract":361,"tags":362},[102],[],[],{"code":48,"score":21,"supported":14,"title":364,"abstract":365,"tags":366},[],[30,51],[27,30],{"code":77,"score":63,"supported":14,"title":368,"abstract":369,"tags":371},[],[80,370],"corn",[],{"code":214,"score":63,"supported":14,"title":373,"abstract":374,"tags":377},[],[375,376],"大豆","soybean",[],{"code":379,"score":113,"supported":14,"title":380,"abstract":381,"tags":383},"F5",[],[382],"harvester",[],{"code":105,"score":22,"supported":42,"title":385,"abstract":386,"tags":387},[],[],[28],[389,390],"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":392,"openalex_id":394,"authors":395,"venue":325,"cited_by_count":132,"oa_url":399,"card":400,"direction":404,"ingested_from":164},"W7214927134",[396],{"name":397,"orcid":398},"Muhammad Ovais Akhter","https:\u002F\u002Forcid.org\u002F0000-0002-8928-4489","https:\u002F\u002Fph.pollub.pl\u002Findex.php\u002Fiapgos\u002Farticle\u002Fdownload\u002F9467\u002F5631",{"tldr":401,"method":402,"finding":403,"direction":404,"opportunity":405},"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。","智慧农业 \u002F 农业物联网","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":408,"title":409,"url":410,"summary":411,"summary_zh":412,"content":9,"source_name":413,"source_url":410,"published_at":414,"category":12,"cover_url":9,"hotness":13,"is_selected":42,"score":415,"score_detail":416,"sources":418,"tags":420,"primary_topic":423,"secondary_topics":424,"classification":430,"search_phrases":457,"slug":460,"view_count":461,"doi":462,"paper":463,"created_at":475},3680,"AI-Driven Nutrient Optimization Systems for Precision Agriculture: A Data-Integrated Approach to Reducing Nutrient Runoff and Enhancing Soil Health in Agricultural Systems","https:\u002F\u002Fdoi.org\u002F10.36948\u002Fijfmr.2026.v08i05.85286","Nutrients play a fundamental role in plant growth, soil fertility, and overall agricultural productivity. However, inefficient fertilizer application in conventional farming systems often leads to nutrient imbalance, reduced soil quality, and increased environmental degradation. The emergence of precision agriculture and artificial intelligence (AI) has introduced new opportunities for improving nutrient management through data-driven decision-making. This study investigates an AI-driven nutrient optimization system designed to enhance fertilizer efficiency while reducing nutrient runoff and improving soil health. Findings indicate that AI-based systems significantly improve nutrient use efficiency by enabling site-specific fertilizer recommendations and adaptive management strategies. The integration of multi-source agricultural data enhances prediction accuracy for soil nutrient status and crop demand, while also supporting early identification of nutrient loss risks such as runoff and leaching. The study concludes that AI-driven nutrient optimization provides a sustainable and scalable approach for modern agriculture, supporting both productivity enhancement and environmental protection through intelligent, data-integrated farming systems.","养分在植物生长、土壤肥力和农业整体生产力中发挥着基础性作用。然而，传统农业系统中低效的施肥方式往往导致养分失衡、土壤质量下降以及环境退化加剧。精准农业与人工智能（AI）的出现，为通过数据驱动决策改善养分管理带来了新的机遇。本研究探讨了一种AI驱动的养分优化系统，旨在提高肥料利用效率，同时减少养分径流并改善土壤健康。研究结果表明，基于AI的系统通过实现特定地块的施肥建议和适应性管理策略，显著提高了养分利用效率。多源农业数据的整合提升了土壤养分状况和作物需求的预测精度，同时也有助于早期识别径流和淋溶等养分流失风险。研究得出结论，AI驱动的养分优化为现代农业提供了一种可持续且可扩展的方法，通过智能化、数据整合的农业系统，同时支持生产力提升和环境保护。","International Journal For Multidisciplinary Research","2026-09-26T00:00:00Z",61,{"impact":178,"substance":19,"depth":200,"authority":21,"freshness":21,"relevant":22,"comment":417},"论文提出AI驱动的养分优化系统，方法有新意但属学术探讨，产业落地影响有限，可作为智慧农业技术动态收录。",[419],{"name":413,"url":410},[27,28,421,31,422],"精准施肥","养分流失",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[425],{"code":112,"slug":426,"name":427,"path":428,"parent_code":429},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",{"status":41,"locked":42,"version":43,"method":44,"evidence":431},{"candidates":432,"agricultural_evidence":14},[433,437,441,447,451],{"code":34,"score":200,"supported":14,"title":434,"abstract":435,"tags":436},[35],[59,35],[31],{"code":48,"score":200,"supported":14,"title":438,"abstract":439,"tags":440},[51],[30,51],[27],{"code":112,"score":13,"supported":14,"title":442,"abstract":443,"tags":446},[],[444,116,445,297],"施肥","养分管理",[421],{"code":105,"score":106,"supported":14,"title":448,"abstract":449,"tags":450},[],[109],[28],{"code":452,"score":113,"supported":14,"title":453,"abstract":454,"tags":456},"I6",[],[455],"agricultural data",[],[458,459],"AI 养分优化 精准农业","养分流失 土壤健康","AI养分优化精准农业-3680",2,"10.36948\u002Fijfmr.2026.v08i05.85286",{"doi":462,"openalex_id":464,"authors":465,"venue":413,"cited_by_count":132,"oa_url":410,"card":470,"direction":161,"ingested_from":164},"W7214553198",[466,468],{"name":467,"orcid":9},"Olufisayo Obebe",{"name":469,"orcid":9},"Ebenezer Akinola",{"tldr":471,"method":472,"finding":473,"direction":161,"opportunity":474},"研究AI驱动的养分优化系统，通过数据集成实现精准施肥，减少养分流失并改善土壤健康。","集成多源农业数据与AI算法，进行站点特异性施肥推荐和自适应管理。","AI系统显著提高养分利用效率，增强土壤养分与作物需求预测精度，并早期识别流失风险。","可探索多源数据融合的实时决策模型，以及在不同土壤气候条件下AI系统的可扩展性与长期环境效应评估。","2026-09-28T23:30:49.535963Z",{"id":477,"title":478,"url":479,"summary":480,"summary_zh":481,"content":9,"source_name":482,"source_url":479,"published_at":483,"category":12,"cover_url":9,"hotness":13,"is_selected":42,"score":484,"score_detail":485,"sources":488,"tags":490,"primary_topic":492,"secondary_topics":493,"classification":496,"search_phrases":527,"slug":530,"view_count":63,"doi":531,"paper":532,"created_at":563},2947,"AI-enabled UAV-based Soil Organic Carbon Mapping in Arid Environments: A Pilot Study Protocol","https:\u002F\u002Fdoi.org\u002F10.2174\u002F0118743315495282260915110324","Introduction Soil organic carbon (SOC) is an important indicator of soil health, agricultural productivity, and carbon sequestration potential. However, accurate and scalable SOC mapping in arid environments is constrained by high spatial heterogeneity and the limitations of conventional soil sampling. This study aims to develop a standardized UAV-enabled framework for high-resolution SOC mapping in arid agricultural environments. Methods A pilot-study protocol integrating UAV-based hyperspectral remote sensing with artificial intelligence and machine learning was developed. The workflow encompasses study-site selection, ground-reference sampling, UAV hyperspectral data acquisition, radiometric and geometric preprocessing, spectral feature extraction and selection, machine-learning model development, validation, uncertainty assessment, and performance evaluation using R 2 , RMSE, and MAE. The protocol also incorporates assessment of environmental confounders, including soil moisture, surface roughness, and crop residues. Results The resulting framework provides a systematic and reproducible workflow for UAV-based SOC estimation, integrating field observations, hyperspectral features, predictive modelling, and uncertainty assessment. It establishes defined procedures for evaluating model robustness and transferability across varying field conditions. Discussion The framework addresses an important methodological gap in UAV-enabled SOC mapping by integrating remote sensing and AI within a standardized pilot-study design. Its emphasis on environmental confounders and uncertainty assessment can improve the reliability and comparability of SOC mapping studies. However, field validation across diverse arid environments remains necessary. Conclusion The proposed protocol provides a practical foundation for reproducible SOC mapping and subsequent field validation, supporting precision agriculture, sustainable soil management, and carbon monitoring, reporting, and verification (MRV) in arid regions.","引言 土壤有机碳（SOC）是衡量土壤健康、农业生产力及碳固存潜力的重要指标。然而，干旱环境中高空间异质性和传统土壤采样的局限性制约了准确且可扩展的SOC制图。本研究旨在开发一个标准化的无人机（UAV）框架，用于干旱农业环境中的高分辨率SOC制图。方法 开发了一套整合无人机高光谱遥感与人工智能及机器学习的试点研究方案。该工作流程涵盖研究地点选择、地面参考采样、无人机高光谱数据采集、辐射与几何预处理、光谱特征提取与选择、机器学习模型开发、验证、不确定性评估，以及使用R²、RMSE和MAE进行的性能评价。该方案还包括对环境混杂因素的评估，包括土壤水分、地表粗糙度和作物残茬。结果 所构建的框架为基于无人机的SOC估算提供了系统且可重复的工作流程，整合了野外观测、高光谱特征、预测建模和不确定性评估。它建立了明确的程序，用于评估模型在不同田间条件下的稳健性和可迁移性。讨论 该框架通过将遥感与人工智能整合于标准化的试点研究设计中，填补了无人机SOC制图领域的重要方法学空白。其对环境混杂因素和不确定性评估的重视，可提高SOC制图研究的可靠性和可比性。然而，仍需在不同干旱环境中进行田间验证。结论 所提出的方案为可重复的SOC制图及后续田间验证提供了实用基础，支持干旱地区的精准农业、可持续土壤管理以及碳监测、报告与核查（MRV）。","The Open Agriculture Journal","2026-09-18T00:00:00Z",67,{"impact":178,"substance":19,"depth":486,"authority":180,"freshness":21,"relevant":22,"comment":487},16,"提出无人机高光谱结合AI的干旱区土壤有机碳制图标准化方案，方法框架清晰但尚属试点协议、缺乏实地验证，具备一定参考价值。",[489],{"name":482,"url":479},[27,28,30,186,491],"土壤碳汇",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[494,495],{"code":105,"slug":273,"name":274,"path":275,"parent_code":195},{"code":191,"slug":192,"name":193,"path":194,"parent_code":195},{"status":41,"locked":42,"version":43,"method":44,"evidence":497},{"candidates":498,"agricultural_evidence":14},[499,503,507,512,516,522],{"code":34,"score":20,"supported":14,"title":500,"abstract":501,"tags":502},[35],[59,35],[],{"code":105,"score":13,"supported":14,"title":504,"abstract":505,"tags":506},[],[286,287,109],[28],{"code":191,"score":13,"supported":14,"title":508,"abstract":509,"tags":511},[],[186,206,510],"hyperspectral",[186],{"code":48,"score":21,"supported":14,"title":513,"abstract":514,"tags":515},[],[30,51],[27,30],{"code":517,"score":106,"supported":14,"title":518,"abstract":519,"tags":521},"H4",[],[520],"carbon sequestration",[491],{"code":91,"score":113,"supported":14,"title":523,"abstract":524,"tags":526},[],[525],"干旱",[],[528,529],"无人机 土壤有机碳 制图","AI 高光谱 干旱农业","无人机土壤有机碳制图-2947","10.2174\u002F0118743315495282260915110324",{"doi":531,"openalex_id":533,"authors":534,"venue":482,"cited_by_count":132,"oa_url":479,"card":558,"direction":251,"ingested_from":164},"W7213561504",[535,538,541,544,547,550,552,554,556],{"name":536,"orcid":537},"Moath Awawdeh","https:\u002F\u002Forcid.org\u002F0000-0003-1404-6782",{"name":539,"orcid":540},"Irfan Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2172-4177",{"name":542,"orcid":543},"Anees Bashir","https:\u002F\u002Forcid.org\u002F0000-0002-4668-6592",{"name":545,"orcid":546},"Tarig Faisal","https:\u002F\u002Forcid.org\u002F0000-0001-6451-7576",{"name":548,"orcid":549},"Nicky Rahmana Putra","https:\u002F\u002Forcid.org\u002F0000-0003-4886-496X",{"name":551,"orcid":9},"Almaha Jamal",{"name":553,"orcid":9},"Afra Rashed",{"name":555,"orcid":9},"Hamda Yousif",{"name":557,"orcid":9},"Sarah Sadeq",{"tldr":559,"method":560,"finding":561,"direction":251,"opportunity":562},"提出一套无人机高光谱结合AI的干旱区土壤有机碳制图标准化试点方案。","无人机高光谱遥感、地面采样、光谱特征选择与机器学习建模，用R²、RMSE、MAE","构建了可复现的SOC估算流程，并纳入环境混杂因素与不确定性评估。","可在多干旱区开展跨区域验证，探索模型迁移性与不确定性量化方法。","2026-09-19T23:30:33.273156Z",{"id":565,"title":566,"url":567,"summary":568,"summary_zh":569,"content":9,"source_name":570,"source_url":567,"published_at":571,"category":12,"cover_url":9,"hotness":13,"is_selected":42,"score":415,"score_detail":572,"sources":574,"tags":576,"primary_topic":578,"secondary_topics":579,"classification":582,"search_phrases":608,"slug":611,"view_count":461,"doi":612,"paper":613,"created_at":626},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",{"impact":178,"substance":19,"depth":486,"authority":63,"freshness":181,"relevant":22,"comment":573},"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。",[575],{"name":570,"url":567},[27,28,30,268,577],"土壤数据",{"code":105,"slug":273,"name":274,"path":275,"parent_code":195},[580,581],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"code":336,"slug":337,"name":338,"path":339,"parent_code":195},{"status":41,"locked":42,"version":43,"method":44,"evidence":583},{"candidates":584,"agricultural_evidence":14},[585,592,596,601],{"code":105,"score":586,"supported":14,"title":587,"abstract":589,"tags":591},34,[287,588,109],"deep learning",[590,286,287,588,109],"深度学习",[28],{"code":34,"score":200,"supported":14,"title":593,"abstract":594,"tags":595},[35],[59,35],[577],{"code":48,"score":13,"supported":14,"title":597,"abstract":599,"tags":600},[598],"smart agriculture",[],[27,30],{"code":336,"score":181,"supported":14,"title":602,"abstract":603,"tags":607},[],[604,605,606],"物联网","internet of things","IoT",[],[609,610],"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":612,"openalex_id":614,"authors":615,"venue":570,"cited_by_count":132,"oa_url":620,"card":621,"direction":404,"ingested_from":164},"W7212594050",[616,618],{"name":617,"orcid":9},"Snehal Sanjay Raut",{"name":619,"orcid":9},"Yogesh V. Chimate","https:\u002F\u002Fwww.irejournals.com\u002Fformatedpaper\u002F1722922.pdf",{"tldr":622,"method":623,"finding":624,"direction":161,"opportunity":625},"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。","2026-09-15T23:30:08.397298Z"]