[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1595":3,"related-1595":94},{"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":23,"tags":25,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":68,"slug":71,"view_count":21,"doi":72,"paper":73,"created_at":93},1595,"Site-specific mechanical weeding robot with automatic self-adjustable individually controlled cultivator","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10445-3","Abstract Context Weeds significantly reduce crop productivity and profitability, particularly in sugar beet production where losses exceed $1.25 billion annually in the United States. Limitations of herbicide-based control and drawbacks of conventional tillage highlight the need for efficient, sustainable alternatives. Aims To develop and evaluate an autonomous site-specific mechanical weeding (SSMW) system with adaptive control for precise inter-row weed removal under field conditions. Methods An SSMW system was designed by integrating an inter-row tillage mechanism with a robotic platform using RTK-GPS for navigation and site-specific actuation. Site-specific weeding was performed in the field test and a strain-based feedback control system was implemented in computer simulation to enable ASIC tillage under uneven terrain. Simulation studies based on field data were conducted to validate control performance, followed by field experiments to assess system effectiveness. Key Results The SSMW system’s efficacy was found to be 96.5% by tillage length. Conclusion The SSMW system achieved precise site-specific weeding using GPS-based control of individual sweeps, with a weeding efficacy of 96.5% based on tillage length. The ASIC framework with sensor feedback simulation showed the feasibility of improved adaptability to uneven terrain, with future work focusing on enhanced sensing and faster actuation for improved field performance. Implications and Impacts This study contributes to precision agriculture by enabling targeted weed management through intelligent sensing and control, reducing energy use and soil disruption. The system offers a sustainable alternative for organic farming, improving operational efficiency and environmental stewardship.","摘要 背景 杂草显著降低作物产量和经济效益，尤其是在甜菜生产中，美国每年因此造成的损失超过12.5亿美元。基于除草剂的防治方法存在局限性，而传统耕作方式也有弊端，因此亟需高效、可持续的替代方案。目的 开发并评估一种具有自适应控制能力的自主位点特异性机械除草（SSMW）系统，用于田间条件下精确的行间杂草清除。方法 设计了一套SSMW系统，将行间耕作机构与机器人平台集成，利用RTK-GPS实现导航和位点特异性驱动。田间试验中实施了位点特异性除草，并在计算机仿真中引入基于应变的反馈控制系统，以实现不平整地形下的自适应智能位点特异性耕作（ASIC）。基于田间数据的仿真研究用于验证控制性能，随后开展田间试验评估系统有效性。关键结果 SSMW系统按耕作长度计算的除草效率为96.5%。结论 SSMW系统通过基于GPS的单个锄铲控制实现了精确的位点特异性除草，按耕作长度计算的除草效率为96.5%。带有传感器反馈仿真的ASIC框架展示了在不平整地形下提高适应性的可行性，未来工作将聚焦于增强感知能力和加快驱动响应，以提升田间表现。影响与意义 本研究通过智能感知与控制实现靶向杂草管理，减少能源消耗和土壤扰动，为精准农业做出贡献。该系统为有机农业提供了可持续的替代方案，提高了作业效率并促进了环境可持续性。",null,"Precision Agriculture","2026-09-04T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"论文提出基于GPS与自适应控制的间作除草机器人，田间除草效率达96.5%，为精准除草提供可持续方案，具有较高专业价值。",[24],{"name":10,"url":6},[26,27,28,29],"智慧农业","农业机器人","精准农业","除草技术",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,50,56,63],{"code":44,"score":20,"supported":45,"title":46,"abstract":47,"tags":49},"I1",true,[],[28,48],"precision agriculture",[26,28],{"code":31,"score":51,"supported":45,"title":52,"abstract":53,"tags":55},6,[],[54,32],"土壤",[],{"code":57,"score":58,"supported":45,"title":59,"abstract":60,"tags":62},"F4",3,[],[61],"除草",[],{"code":64,"score":21,"supported":14,"title":65,"abstract":66,"tags":67},"F6",[],[],[27],[69,70],"农业机器人 智慧农业 精准农业 除草技术","农业机器人 智慧农业","农业机器人智慧农业精准农业除草技术-1595","10.1007\u002Fs11119-026-10445-3",{"doi":72,"openalex_id":74,"authors":75,"venue":10,"cited_by_count":85,"oa_url":6,"card":86,"direction":90,"ingested_from":92},"W7207873251",[76,78,80,82],{"name":77,"orcid":9},"Shafi Md. Istiak",{"name":79,"orcid":9},"Mohammad Aftabi Talami",{"name":81,"orcid":9},"James Y. Kim",{"name":83,"orcid":84},"Sulaymon Eshkabilov","https:\u002F\u002Forcid.org\u002F0000-0002-7266-2201",0,{"tldr":87,"method":88,"finding":89,"direction":90,"opportunity":91},"开发并评估了基于RTK-GPS的自主位点特异性机械除草系统，除草效率达96.5%。","集成行间耕作机构与机器人平台，采用RTK-GPS导航和应变反馈控制模拟。","系统除草效率96.5%，自适应控制模拟显示适应不平地形的可行性。","智慧农业 \u002F 农业物联网","未来可研究增强传感和更快执行器，以提升田间性能，并探索在有机农业中的实际应用。","openalex","2026-09-04T23:30:03.245281Z",{"total":51,"page":21,"page_size":51,"items":95},[96,198,277,406,496,568],{"id":97,"title":98,"url":99,"summary":100,"summary_zh":101,"content":9,"source_name":102,"source_url":99,"published_at":103,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":104,"score_detail":105,"sources":111,"tags":113,"primary_topic":117,"secondary_topics":123,"classification":130,"search_phrases":181,"slug":184,"view_count":185,"doi":9,"paper":186,"created_at":197},5013,"REMOTE SENSING AND GIS-BASED ASSESSMENT OF SOIL SALINITY IN IRRIGATED LANDS: METHODS, CHALLENGES AND PROSPECTS FOR PRECISION AGRICULTURE","http:\u002F\u002Fwww.arabianjournalofscience.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F1223","Soil salinization is a major land degradation process affecting the sustainability of irrigated agriculture, particularly in arid and semi-arid regions. Conventional soil sampling provides essential information about salinity levels but may be insufficient for continuous monitoring of large agricultural territories because of spatial variability, sampling costs, and temporal limitations. Remote sensing (RS) and geographic information systems (GIS) offer complementary approaches for identifying spatial patterns of salinity, monitoring land conditions, and supporting agricultural management. This article examines the scientific and methodological foundations of integrating multispectral satellite imagery, spectral indices, geospatial analysis, and machine learning for soil salinity assessment in irrigated lands. Particular attention is given to Landsat and Sentinel-2 imagery, the interpretation of vegetation and soil reflectance indicators, the role of electrical conductivity measurements in model calibration, and the limitations associated with vegetation cover, soil moisture, and seasonal variability. The analysis indicates that reliable salinity assessment requires the integration of satellite observations with field measurements and environmental variables rather than the isolated application of spectral indices. The article proposes a methodological framework for developing spatially explicit salinity assessment systems applicable to irrigated agricultural landscapes, including those of Central Asia. The framework provides a basis for improving monitoring strategies and supporting evidence-based land management without assuming unverified local measurement results.","土壤盐渍化是影响灌溉农业可持续性的主要土地退化过程，在干旱和半干旱地区尤为突出。传统土壤采样可提供盐分水平的基本信息，但由于空间变异性、采样成本和时间限制，可能不足以对大面积农业区域进行持续监测。遥感（RS）和地理信息系统（GIS）为识别盐渍化空间格局、监测土地状况和支持农业管理提供了互补性方法。本文探讨了整合多光谱卫星影像、光谱指数、地理空间分析和机器学习进行灌溉土地土壤盐渍化评估的科学与方法学基础。文章特别关注Landsat和Sentinel-2影像、植被与土壤反射率指标的解译、电导率测量在模型校准中的作用，以及与植被覆盖、土壤湿度和季节变异性相关的局限性。分析表明，可靠的盐渍化评估需要将卫星观测与野外测量和环境变量相结合，而非孤立地应用光谱指数。本文提出了一个方法学框架，用于开发适用于灌溉农业景观（包括中亚地区）的空间显式盐渍化评估系统。该框架为改进监测策略和支持基于证据的土地管理提供了基础，且不以未经核实的当地测量结果为假设前提。","Arabian journal of science.","2026-10-13T00:00:00Z",74,{"impact":106,"substance":107,"depth":108,"authority":109,"freshness":20,"relevant":21,"comment":110},16,20,17,13,"系统综述遥感与GIS在灌溉农田盐渍化评估中的方法框架与挑战，对盐碱地治理和精准农业有参考价值，但属综述性论文，非突破性成果。",[112],{"name":102,"url":99},[26,28,114,115,116],"遥感","盐碱地治理","土壤监测",{"code":118,"slug":119,"name":120,"path":121,"parent_code":122},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[124,129],{"code":125,"slug":126,"name":127,"path":128,"parent_code":122},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":131},{"candidates":132,"agricultural_evidence":45},[133,139,147,152,156,163,169,175],{"code":118,"score":17,"supported":45,"title":134,"abstract":136,"tags":138},[135],"remote sensing",[114,135,137],"multispectral",[114],{"code":125,"score":108,"supported":45,"title":140,"abstract":142,"tags":146},[141],"GIS",[143,144,141,145],"地理信息","空间分析","geographic information",[],{"code":31,"score":148,"supported":45,"title":149,"abstract":150,"tags":151},15,[32],[54,32],[115],{"code":44,"score":13,"supported":45,"title":153,"abstract":154,"tags":155},[48],[],[26,28],{"code":157,"score":51,"supported":45,"title":158,"abstract":159,"tags":162},"I2",[],[160,161],"机器学习","machine learning",[],{"code":164,"score":58,"supported":45,"title":165,"abstract":166,"tags":168},"H3",[],[167],"灌溉",[],{"code":170,"score":58,"supported":45,"title":171,"abstract":172,"tags":174},"H7",[],[173],"干旱",[],{"code":176,"score":58,"supported":45,"title":177,"abstract":178,"tags":180},"I7",[],[179],"methodological framework",[],[182,183],"遥感 GIS 土壤盐渍化","Landsat Sentinel-2 盐分监测","遥感GIS土壤盐渍化-5013",2,{"doi":9,"openalex_id":187,"authors":188,"venue":102,"cited_by_count":85,"oa_url":99,"card":191,"direction":195,"ingested_from":92},"W7221843179",[189],{"name":190,"orcid":9},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":192,"method":193,"finding":194,"direction":195,"opportunity":196},"综述遥感与GIS评估灌溉地土壤盐渍化的方法、挑战与前景，并提出空间显式评估框架。","整合多光谱卫星影像、光谱指数、GIS空间分析与机器学习，结合电导率校准。","可靠盐渍化评估需卫星观测与实地测量及环境变量融合，而非孤立使用光谱指数。","农业遥感与作物表型","可探索多源遥感与机器学习融合的盐渍化动态监测，并验证于中亚等干旱灌区。","2026-10-15T23:30:32.401626Z",{"id":199,"title":200,"url":201,"summary":202,"summary_zh":203,"content":9,"source_name":204,"source_url":201,"published_at":103,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":205,"score_detail":206,"sources":211,"tags":213,"primary_topic":216,"secondary_topics":217,"classification":219,"search_phrases":259,"slug":262,"view_count":21,"doi":263,"paper":264,"created_at":276},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",72,{"impact":207,"substance":208,"depth":108,"authority":109,"freshness":209,"relevant":21,"comment":210},12,21,9,"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。",[212],{"name":204,"url":201},[26,28,114,214,215],"土壤健康","覆盖作物",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[218],{"code":118,"slug":119,"name":120,"path":121,"parent_code":122},{"status":38,"locked":14,"version":39,"method":40,"evidence":220},{"candidates":221,"agricultural_evidence":45},[222,226,230,234,241,248,254],{"code":31,"score":148,"supported":45,"title":223,"abstract":224,"tags":225},[32],[54,32],[214],{"code":118,"score":207,"supported":45,"title":227,"abstract":228,"tags":229},[135],[114],[114],{"code":44,"score":20,"supported":45,"title":231,"abstract":232,"tags":233},[],[28,48],[26,28],{"code":235,"score":51,"supported":45,"title":236,"abstract":237,"tags":240},"A4",[],[238,239],"油菜","rapeseed",[],{"code":242,"score":51,"supported":45,"title":243,"abstract":244,"tags":247},"H8",[],[245,246],"可持续农业","sustainable agriculture",[],{"code":249,"score":58,"supported":45,"title":250,"abstract":251,"tags":253},"B1",[],[252],"萝卜",[],{"code":255,"score":21,"supported":14,"title":256,"abstract":257,"tags":258},"A7",[],[],[215],[260,261],"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":263,"openalex_id":265,"authors":266,"venue":204,"cited_by_count":85,"oa_url":9,"card":271,"direction":195,"ingested_from":92},"W7221669942",[267,269],{"name":268,"orcid":9},"Ali Mirzakhani Nafchi",{"name":270,"orcid":9},"Salman Mirzaee",{"tldr":272,"method":273,"finding":274,"direction":195,"opportunity":275},"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。","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":278,"title":279,"url":280,"summary":281,"summary_zh":282,"content":9,"source_name":283,"source_url":280,"published_at":284,"category":12,"cover_url":9,"hotness":13,"is_selected":45,"score":285,"score_detail":286,"sources":288,"tags":290,"primary_topic":294,"secondary_topics":295,"classification":296,"search_phrases":370,"slug":373,"view_count":85,"doi":374,"paper":375,"created_at":405},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":18,"substance":208,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":287},"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。",[289],{"name":283,"url":280},[26,291,292,28,214,293],"农业人工智能","种业振兴","多组学",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":297},{"candidates":298,"agricultural_evidence":45},[299,305,310,317,324,331,338,344,348,354,360,365],{"code":44,"score":300,"supported":45,"title":301,"abstract":302,"tags":304},19,[48],[28,303,48],"数字孪生",[26,28],{"code":31,"score":306,"supported":45,"title":307,"abstract":308,"tags":309},7,[],[54,32],[214],{"code":311,"score":51,"supported":45,"title":312,"abstract":313,"tags":316},"A1",[],[314,315],"水稻","rice",[],{"code":318,"score":51,"supported":45,"title":319,"abstract":320,"tags":323},"A2",[],[321,322],"小麦","wheat",[],{"code":325,"score":51,"supported":45,"title":326,"abstract":327,"tags":330},"A3",[],[328,329],"玉米","maize",[],{"code":332,"score":51,"supported":45,"title":333,"abstract":334,"tags":337},"A8",[],[335,336],"粮食安全","food security",[],{"code":170,"score":51,"supported":45,"title":339,"abstract":340,"tags":343},[],[341,342],"气候变化","climate change",[],{"code":242,"score":51,"supported":45,"title":345,"abstract":346,"tags":347},[],[245,246],[],{"code":157,"score":349,"supported":45,"title":350,"abstract":351,"tags":353},4,[],[352],"artificial intelligence",[291],{"code":355,"score":58,"supported":45,"title":356,"abstract":357,"tags":359},"F3",[],[358],"肥料",[],{"code":361,"score":21,"supported":14,"title":362,"abstract":363,"tags":364},"F1",[],[],[292],{"code":366,"score":21,"supported":14,"title":367,"abstract":368,"tags":369},"F2",[],[],[293],[371,372],"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":374,"openalex_id":376,"authors":377,"venue":283,"cited_by_count":85,"oa_url":280,"card":398,"direction":404,"ingested_from":92},"W7221567543",[378,381,383,386,388,390,393,396],{"name":379,"orcid":380},"Tao Tong","https:\u002F\u002Forcid.org\u002F0000-0002-9003-9667",{"name":382,"orcid":9},"Wang Qingxia",{"name":384,"orcid":385},"Fanrong Zeng","https:\u002F\u002Forcid.org\u002F0000-0001-7742-5195",{"name":387,"orcid":9},"Shuzhen Ye",{"name":389,"orcid":9},"Zhijuan Ji",{"name":391,"orcid":392},"Zhong‐Hua Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7531-320X",{"name":394,"orcid":395},"Jiaoyu Wang","https:\u002F\u002Forcid.org\u002F0000-0002-0249-808X",{"name":397,"orcid":9},"Younan Ouyang",{"tldr":399,"method":400,"finding":401,"direction":402,"opportunity":403},"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","2026-10-13T23:30:29.504594Z",{"id":407,"title":408,"url":409,"summary":410,"summary_zh":411,"content":9,"source_name":412,"source_url":409,"published_at":413,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":414,"score_detail":415,"sources":417,"tags":419,"primary_topic":423,"secondary_topics":428,"classification":430,"search_phrases":453,"slug":456,"view_count":21,"doi":457,"paper":458,"created_at":495},4445,"Soil intrinsic penetration (SIP) index: decoupling environmental noise from mechanical resistance to improve soybean yield prediction","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-75118-7","Soil compaction is a critical global agricultural concern, acting as a severe physical barrier that restricts root system expansion and reduces crop productivity. However, conventional Soil Penetration Resistance (PR) is a transient metric dictated by instantaneous hydraulic states rather than a stable structural property, leading to misleading interpretations in precision agriculture. This study aimed to develop and validate the Moisture-Normalized Soil Intrinsic Penetration (SIP) Index to isolate persistent anthropogenic structural degradation from environmental noise. Data were compiled from five field campaigns (2020–2025) conducted on a Typic Hapludox in Chapadão do Sul, Brazil. Three distinct sensing technologies: electronic (reference); hydraulic; and impact penetrometers, were evaluated across four depth intervals (0.0–0.4 m). The SIP Index was derived using a power-law normalization approach (SIP = PR x SW α x Clay β ), with empirical coefficients optimized via a Differential Evolution algorithm to maximize Spearman correlations with soybean yield. Results consistently identified the 0.10–0.20 m layer as the critical functional compaction barrier (traffic pan) across all sensing systems. While raw PR from continuous-insertion sensors (electronic and hydraulic) showed negligible or counter-intuitive yield correlations (ρ ≈ -0.01 to 0.08), the SIP Index drastically enhanced predictive capacity, reaching ρ = -0.39 for the electronic sensor, representing a substantial shift from the negligible associations observed with raw data, and ρ = -0.41 for the hydraulic system. Impact penetrometers exhibited higher inherent stability against moisture variations, rendering the SIP correction less significant for that device. Ultimately, the SIP framework effectively filters out pedogenic and hydraulic noise, providing a robust anthropogenic signature for delineating management zones and identifying true mechanical constraints to crop performance.","土壤压实是全球农业面临的严峻问题，作为一种严重的物理障碍，限制根系扩展并降低作物生产力。然而，传统的土壤穿透阻力（PR）是一种瞬时指标，受即时水分状态支配，而非稳定的结构属性，这导致在精准农业中产生误导性解读。本研究旨在开发并验证水分归一化土壤固有穿透（SIP）指数，以将持久性人为结构退化与环境噪声分离。数据来源于2020至2025年间在巴西沙帕当杜苏尔一处典型强淋溶老成土上开展的五次田间试验。评估了三种不同的传感技术：电子（参考）、液压和冲击式穿透仪，涵盖四个深度区间（0.0–0.4 m）。SIP指数采用幂律归一化方法（SIP = PR × SW^α × Clay^β）推导，经验系数通过差分进化算法优化，以最大化与大豆产量的Spearman相关性。结果一致表明，在所有传感系统中，0.10–0.20 m土层均为关键功能性压实障碍（机械压实层）。连续插入式传感器（电子和液压）的原始PR与产量相关性可忽略或呈反直觉关系（ρ ≈ -0.01至0.08），而SIP指数大幅提升了预测能力，电子传感器达到ρ = -0.39，相较于原始数据的可忽略关联实现了显著转变，液压系统达到ρ = -0.41。冲击式穿透仪对水分变化表现出更高的固有稳定性，因此SIP校正对该设备意义较小。最终，SIP框架有效滤除了成土和水分噪声，为划定管理分区和识别作物生长的真实机械限制提供了稳健的人为信号。","Scientific Reports","2026-10-06T00:00:00Z",76,{"impact":148,"substance":18,"depth":17,"authority":109,"freshness":20,"relevant":21,"comment":416},"提出水分归一化土壤内在穿透指数，用三种传感技术跨五年田间数据验证，显著提升大豆产量预测能力，方法新颖且对精准农业管理分区有实用价值。",[418],{"name":412,"url":409},[26,28,420,421,422],"土壤传感","土壤压实","大豆产量",{"code":235,"slug":424,"name":425,"path":426,"parent_code":427},"soybean-oilseeds","大豆与油料","\u002Ftopic\u002Ffield-crops\u002Fsoybean-oilseeds","A",[429],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":431},{"candidates":432,"agricultural_evidence":45},[433,439,443,447],{"code":235,"score":19,"supported":45,"title":434,"abstract":436,"tags":438},[435],"soybean",[437,435],"大豆",[],{"code":31,"score":19,"supported":45,"title":440,"abstract":441,"tags":442},[32],[54,32],[],{"code":44,"score":20,"supported":45,"title":444,"abstract":445,"tags":446},[],[28,48],[26,28],{"code":448,"score":58,"supported":45,"title":449,"abstract":450,"tags":452},"J3",[],[451],"全球农业",[],[454,455],"土壤穿透阻力 大豆产量","SIP 指数 土壤压实","土壤穿透阻力大豆产量-4445","10.1038\u002Fs41598-026-75118-7",{"doi":457,"openalex_id":459,"authors":460,"venue":412,"cited_by_count":85,"oa_url":489,"card":490,"direction":404,"ingested_from":92},"W7220671604",[461,464,466,469,472,475,478,481,483,486],{"name":462,"orcid":463},"Fábio Henrique Rojo Baio","https:\u002F\u002Forcid.org\u002F0000-0002-9522-0342",{"name":465,"orcid":9},"Job Teixeira de Oliveira",{"name":467,"orcid":468},"Cassiano Garcia Roque","https:\u002F\u002Forcid.org\u002F0000-0001-6872-0424",{"name":470,"orcid":471},"Ricardo Gava","https:\u002F\u002Forcid.org\u002F0000-0001-6268-5728",{"name":473,"orcid":474},"Larissa Pereira Ribeiro Teodoro","https:\u002F\u002Forcid.org\u002F0000-0002-8121-0119",{"name":476,"orcid":477},"Cid Naudi Silva Campos","https:\u002F\u002Forcid.org\u002F0000-0001-6810-885X",{"name":479,"orcid":480},"Rafael Felippe Ratke","https:\u002F\u002Forcid.org\u002F0000-0001-6930-3913",{"name":482,"orcid":9},"Octávio Babosa Plaster",{"name":484,"orcid":485},"Dthenifer Cordeiro Santana","https:\u002F\u002Forcid.org\u002F0000-0001-7611-6040",{"name":487,"orcid":488},"Paulo Eduardo Teodoro","https:\u002F\u002Forcid.org\u002F0000-0002-8236-542X","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75118-7_reference.pdf",{"tldr":491,"method":492,"finding":493,"direction":90,"opportunity":494},"提出土壤固有穿透指数SIP，剔除水分等环境噪声，提升大豆产量预测精度。","基于2020-2025年巴西五季田间数据，用幂律归一化与差分进化算法优化系数。","0.10-0.20米为关键压实层，SIP使电子与液压传感器产量相关性提升至-0.39和-0.41。","可探索SIP指数在不同土壤类型与作物上的普适性，并集成到实时传感与变量作业决策中。","2026-10-08T23:30:38.497161Z",{"id":497,"title":498,"url":499,"summary":500,"summary_zh":501,"content":9,"source_name":502,"source_url":499,"published_at":413,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":503,"score_detail":504,"sources":506,"tags":508,"primary_topic":511,"secondary_topics":513,"classification":520,"search_phrases":542,"slug":545,"view_count":185,"doi":546,"paper":547,"created_at":567},4427,"Development of Internet of Things-Based Soil Sensors for pH and Phosphorus Monitoring in Paddy Fields: Development, Accuracy, and Implementation Challenges","https:\u002F\u002Fdoi.org\u002F10.56581\u002Fijrerd.11.05.01-08","The development of Internet of Things (IoT) technology provides opportunities for faster and continuous soil monitoring to support precision agriculture.Monitoring soil pH and phosphorus is important in rice cultivation because both parameters are associated with soil chemical conditions and nutrient availability.This review aimed to examine the development of IoT-based soil sensors for monitoring soil pH and phosphorus, including sensor performance, factors affecting measurement results, and potential applications.A literature review was conducted on research and review articles published between 2019 and 2026.The literature was descriptively analyzed and synthesized based on technological development, sensor performance, and environmental and soil characteristics.The results indicate that pH and phosphorus sensors have been developed using various sensing approaches and have demonstrated promising performance under certain conditions; however, measurement results vary across parameters and soil conditions.Sensor performance is influenced by the sensing principle, soil characteristics, environmental conditions, calibration, and validation.IoT-based sensors have the potential to support real-time soil monitoring, but their application as quantitative measurement tools still requires validation and calibration under diverse soil conditions.","物联网（IoT）技术的发展为实现更快、连续性的土壤监测以支持精准农业提供了机遇。监测土壤pH和磷在水稻种植中具有重要意义，因为这两个参数均与土壤化学状况和养分有效性相关。本综述旨在考察用于监测土壤pH和磷的基于物联网的土壤传感器的发展现状，包括传感器性能、影响测量结果的因素以及潜在应用。本文对2019年至2026年间发表的研究论文和综述文章进行了文献综述。基于技术发展、传感器性能以及环境和土壤特征，对文献进行了描述性分析和综合。结果表明，pH和磷传感器已采用多种传感方法进行开发，并在特定条件下展现出良好性能；然而，测量结果因参数和土壤条件的不同而存在差异。传感器性能受传感原理、土壤特性、环境条件、校准和验证的影响。基于物联网的传感器具有支持实时土壤监测的潜力，但其作为定量测量工具的应用仍需在不同土壤条件下进行验证和校准。","International journal of recent engineering research and development.",62,{"impact":207,"substance":17,"depth":148,"authority":209,"freshness":20,"relevant":21,"comment":505},"综述性论文，系统梳理IoT土壤pH与磷传感器的发展、精度与落地挑战，对智慧农业土壤监测方向有参考价值，但非突破性成果。",[507],{"name":502,"url":499},[26,509,28,116,510],"物联网","水稻种植",{"code":311,"slug":315,"name":314,"path":512,"parent_code":427},"\u002Ftopic\u002Ffield-crops\u002Frice",[514,519],{"code":515,"slug":516,"name":517,"path":518,"parent_code":122},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":14,"version":39,"method":40,"evidence":521},{"candidates":522,"agricultural_evidence":45},[523,529,534,538],{"code":515,"score":17,"supported":45,"title":524,"abstract":526,"tags":528},[525],"internet of things",[509,525,527],"IoT",[509],{"code":311,"score":148,"supported":45,"title":530,"abstract":532,"tags":533},[531],"paddy",[314,315],[510],{"code":31,"score":19,"supported":45,"title":535,"abstract":536,"tags":537},[32],[54,32],[],{"code":44,"score":20,"supported":45,"title":539,"abstract":540,"tags":541},[],[28,48],[26,28],[543,544],"物联网 土壤传感器 稻田","土壤pH 监测","物联网土壤传感器稻田-4427","10.56581\u002Fijrerd.11.05.01-08",{"doi":546,"openalex_id":548,"authors":549,"venue":502,"cited_by_count":85,"oa_url":499,"card":562,"direction":90,"ingested_from":92},"W7220841659",[550,552,555,557,560],{"name":551,"orcid":9},"Fariha Laqania",{"name":553,"orcid":554},"Oviyanti Mulyani","https:\u002F\u002Forcid.org\u002F0000-0003-3941-8896",{"name":556,"orcid":9},"Ade Setiawan",{"name":558,"orcid":559},"Pujawati Suryatmana","https:\u002F\u002Forcid.org\u002F0000-0002-4012-8645",{"name":561,"orcid":9},"Wahyu Daradjat",{"tldr":563,"method":564,"finding":565,"direction":90,"opportunity":566},"综述2019-2026年稻田pH与磷的物联网土壤传感器发展、精度及实施挑战。","文献综述，按技术发展、传感器性能与土壤环境特征归纳分析。","传感器在特定条件下表现良好，但精度受原理、土壤、环境与校准影响，定量应用仍需验证。","稻田复杂土壤条件下pH\u002F磷传感器的原位校准与多源数据融合验证是待突破的研究空白。","2026-10-08T23:30:13.316611Z",{"id":569,"title":570,"url":571,"summary":572,"summary_zh":573,"content":9,"source_name":574,"source_url":571,"published_at":575,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":576,"sources":578,"tags":580,"primary_topic":583,"secondary_topics":584,"classification":585,"search_phrases":602,"slug":605,"view_count":349,"doi":606,"paper":607,"created_at":645},4419,"Advances in plant electrostimulation: from cellular and molecular mechanisms to agricultural applications","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15592324.2026.2740676","Electrical stimulation is an emerging physical strategy for agricultural regulation that applies electric fields or currents with defined parameters to modulate plant growth, development, and rhizosphere processes. Owing to its residue-free nature, environmental compatibility, and high controllability, electrical stimulation has considerable potential to improve crop yield and quality while enhancing tolerance to abiotic and biotic stresses, making it increasingly relevant to sustainable agriculture. This review critically synthesizes the mechanisms and applications of electrical stimulation across cellular and molecular, physiological and metabolic, whole-plant, and field scales. Particular emphasis is placed on distinguishing continuous or quasi-continuous stimulation from high-voltage pulsed treatments, which differ in membrane-level mechanisms, reversibility, and safety profiles. Three interrelated factors are identified as central to its effectiveness: regulation of membrane potential and ion transport, root-targeted modulation of plant–soil interactions, and context-dependent matching of stimulation parameters with specific biological responses. We further highlight that translating laboratory-scale benefits into reliable field applications will require intelligent delivery systems capable of stable, scalable, energy-efficient, and feedback-controlled operation under variable environmental conditions. Future research should prioritize multiscale investigation of molecular mechanisms, standardized evaluation of stimulation parameters, development of smart and synergistic regulation platforms, and validation across diverse crops, soils, and production systems. These advances will facilitate the practical deployment of electrical stimulation technologies and support their integration into sustainable and precision agriculture.","电刺激是一种新兴的农业调控物理策略，即施加具有确定参数的电場或电流以调控植物生长、发育及根际过程。由于其无残留、环境兼容性强和高度可控等特点，电刺激在提高作物产量和品质、增强对非生物和生物胁迫的耐受性方面具有巨大潜力，因而与可持续农业的关系日益密切。本文综述了电刺激在细胞与分子、生理与代谢、整株植物及田间尺度上的机制与应用。特别强调区分连续或准连续刺激与高压脉冲处理，二者在膜水平机制、可逆性和安全性方面存在差异。本文识别出三个相互关联的核心因素对其有效性至关重要：膜电位与离子运输的调控、以根系为靶标的植物–土壤相互作用调控，以及刺激参数与特定生物响应的情境依赖性匹配。本文进一步指出，将实验室尺度的效益转化为可靠的田间应用，需要能够在多变环境条件下稳定、可扩展、节能且具备反馈控制运行的智能递送系统。未来研究应优先开展分子机制的多尺度研究、刺激参数的标准化评估、智能与协同调控平台的开发，以及在多样化作物、土壤和生产系统中的验证。这些进展将促进电刺激技术的实际部署，并支持其融入可持续和精准农业。","Plant Signaling & Behavior","2026-10-07T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":577},"系统综述植物电刺激从细胞机制到田间应用的研究进展，提出智能反馈调控平台方向，对智慧农业与精准农业有参考价值。",[579],{"name":574,"url":571},[26,28,245,581,582],"植物生理","电刺激调控",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":586},{"candidates":587,"agricultural_evidence":45},[588,594,598],{"code":31,"score":209,"supported":45,"title":589,"abstract":590,"tags":593},[],[54,591,32,592],"根际","rhizosphere",[],{"code":44,"score":20,"supported":45,"title":595,"abstract":596,"tags":597},[],[28,48],[26,28],{"code":242,"score":306,"supported":45,"title":599,"abstract":600,"tags":601},[],[245,246],[245],[603,604],"植物电刺激 农业应用","电场调控 作物生长","植物电刺激农业应用-4419","10.1080\u002F15592324.2026.2740676",{"doi":606,"openalex_id":608,"authors":609,"venue":574,"cited_by_count":85,"oa_url":571,"card":640,"direction":90,"ingested_from":92},"W7220853808",[610,612,615,617,620,622,625,627,630,632,634,636,638],{"name":611,"orcid":9},"Yu Dong",{"name":613,"orcid":614},"Ye Tian","https:\u002F\u002Forcid.org\u002F0000-0003-2740-0955",{"name":616,"orcid":9},"Jingxuan Pan",{"name":618,"orcid":619},"Weiwen Qiu","https:\u002F\u002Forcid.org\u002F0000-0003-4924-7365",{"name":621,"orcid":9},"Xinyu ZHOU",{"name":623,"orcid":624},"Yangyang Xie","https:\u002F\u002Forcid.org\u002F0009-0006-1606-3585",{"name":626,"orcid":9},"Mei Liu",{"name":628,"orcid":629},"Juntao Wang","https:\u002F\u002Forcid.org\u002F0000-0002-1106-0848",{"name":631,"orcid":9},"Gao Fengru",{"name":633,"orcid":9},"Liang Jin",{"name":635,"orcid":9},"魏丹",{"name":637,"orcid":9},"Y. Wang",{"name":639,"orcid":9},"Qiang Xiao",{"tldr":641,"method":642,"finding":643,"direction":90,"opportunity":644},"综述植物电刺激从细胞分子机制到田间应用，强调参数匹配与智能调控。","综述细胞、生理、整株及田间尺度研究，区分连续与高压脉冲处理。","效果取决于膜电位与离子运输、根际调控及参数与生物响应的匹配。","开发稳定、可扩展、节能且反馈控制的智能电刺激递送系统，并跨作物土壤验证。","2026-10-08T23:30:09.607013Z"]