[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2300":3,"related-2300":83},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":62,"slug":65,"view_count":58,"doi":66,"paper":67,"created_at":82},2300,"Smart Agriculture Optimization in Off-Grid Terrains via Satellite- Coupled Soil Sensing Framework","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10720368\u002Fv1","Smart Agriculture Optimization in Off-Grid Terrains via Satellite- Coupled Soil Sensing Framework。Research Square",null,"Research Square","2026-09-11T00:00:00Z","论文",10,false,57,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":19,"relevant":20,"comment":21},12,15,14,8,1,"预印本论文提出卫星耦合土壤感知框架，面向离网地形智慧农业优化，方法有创新但尚缺同行评议，属细分领域进展。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","精准农业","遥感","土壤监测","卫星遥感",{"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":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":46},[43,51,56],{"code":44,"score":45,"supported":46,"title":47,"abstract":49,"tags":50},"I1",13,true,[48],"smart agriculture",[48],[25,26],{"code":31,"score":52,"supported":46,"title":53,"abstract":54,"tags":55},11,[32],[32],[],{"code":57,"score":58,"supported":13,"title":59,"abstract":60,"tags":61},"I3",2,[],[],[29,27],[63,64],"卫星遥感 土壤监测 智慧农业 精准农业","卫星遥感 土壤监测","卫星遥感土壤监测智慧农业精准农业-2300","10.21203\u002Frs.3.rs-10720368\u002Fv1",{"doi":66,"openalex_id":68,"authors":69,"venue":9,"cited_by_count":79,"oa_url":6,"card":8,"direction":80,"ingested_from":81},"W7212267038",[70,72,74,77],{"name":71,"orcid":8},"Masood Ahmad",{"name":73,"orcid":8},"Shahid Kamal",{"name":75,"orcid":76},"Fasee Ullah","https:\u002F\u002Forcid.org\u002F0000-0001-6167-5253",{"name":78,"orcid":8},"Ishtiaq Wahid",0,"智慧农业 \u002F 农业物联网","openalex","2026-09-13T23:30:09.626390Z",{"total":84,"page":20,"page_size":84,"items":85},6,[86,186,264,341,424,479],{"id":87,"title":88,"url":89,"summary":90,"summary_zh":91,"content":8,"source_name":92,"source_url":89,"published_at":93,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":94,"score_detail":95,"sources":100,"tags":102,"primary_topic":104,"secondary_topics":109,"classification":116,"search_phrases":170,"slug":173,"view_count":58,"doi":8,"paper":174,"created_at":185},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":96,"substance":97,"depth":98,"authority":45,"freshness":19,"relevant":20,"comment":99},16,20,17,"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。",[101],{"name":92,"url":89},[25,26,27,103,28],"盐碱地治理",{"code":57,"slug":105,"name":106,"path":107,"parent_code":108},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[110,115],{"code":111,"slug":112,"name":113,"path":114,"parent_code":108},"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":13,"version":39,"method":40,"evidence":117},{"candidates":118,"agricultural_evidence":46},[119,126,134,139,144,151,158,164],{"code":57,"score":120,"supported":46,"title":121,"abstract":123,"tags":125},18,[122],"remote sensing",[27,122,124],"multispectral",[27],{"code":111,"score":98,"supported":46,"title":127,"abstract":129,"tags":133},[128],"GIS",[130,131,128,132],"地理信息","空间分析","geographic information",[],{"code":31,"score":17,"supported":46,"title":135,"abstract":136,"tags":138},[32],[137,32],"土壤",[103],{"code":44,"score":12,"supported":46,"title":140,"abstract":142,"tags":143},[141],"precision agriculture",[],[25,26],{"code":145,"score":84,"supported":46,"title":146,"abstract":147,"tags":150},"I2",[],[148,149],"机器学习","machine learning",[],{"code":152,"score":153,"supported":46,"title":154,"abstract":155,"tags":157},"H3",3,[],[156],"灌溉",[],{"code":159,"score":153,"supported":46,"title":160,"abstract":161,"tags":163},"H7",[],[162],"干旱",[],{"code":165,"score":153,"supported":46,"title":166,"abstract":167,"tags":169},"I7",[],[168],"methodological framework",[],[171,172],"遥感 GIS 土壤盐渍化","Landsat Sentinel-2 盐分监测","遥感GIS土壤盐渍化-5013",{"doi":8,"openalex_id":175,"authors":176,"venue":92,"cited_by_count":79,"oa_url":89,"card":179,"direction":183,"ingested_from":81},"W7221843179",[177],{"name":178,"orcid":8},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":180,"method":181,"finding":182,"direction":183,"opportunity":184},"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":187,"title":188,"url":189,"summary":190,"summary_zh":191,"content":8,"source_name":192,"source_url":189,"published_at":93,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":193,"score_detail":194,"sources":198,"tags":200,"primary_topic":203,"secondary_topics":204,"classification":206,"search_phrases":246,"slug":249,"view_count":20,"doi":250,"paper":251,"created_at":263},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":16,"substance":195,"depth":98,"authority":45,"freshness":196,"relevant":20,"comment":197},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。",[199],{"name":192,"url":189},[25,26,27,201,202],"土壤健康","覆盖作物",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[205],{"code":57,"slug":105,"name":106,"path":107,"parent_code":108},{"status":38,"locked":13,"version":39,"method":40,"evidence":207},{"candidates":208,"agricultural_evidence":46},[209,213,217,221,228,235,241],{"code":31,"score":17,"supported":46,"title":210,"abstract":211,"tags":212},[32],[137,32],[201],{"code":57,"score":16,"supported":46,"title":214,"abstract":215,"tags":216},[122],[27],[27],{"code":44,"score":19,"supported":46,"title":218,"abstract":219,"tags":220},[],[26,141],[25,26],{"code":222,"score":84,"supported":46,"title":223,"abstract":224,"tags":227},"A4",[],[225,226],"油菜","rapeseed",[],{"code":229,"score":84,"supported":46,"title":230,"abstract":231,"tags":234},"H8",[],[232,233],"可持续农业","sustainable agriculture",[],{"code":236,"score":153,"supported":46,"title":237,"abstract":238,"tags":240},"B1",[],[239],"萝卜",[],{"code":242,"score":20,"supported":13,"title":243,"abstract":244,"tags":245},"A7",[],[],[202],[247,248],"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":250,"openalex_id":252,"authors":253,"venue":192,"cited_by_count":79,"oa_url":8,"card":258,"direction":183,"ingested_from":81},"W7221669942",[254,256],{"name":255,"orcid":8},"Ali Mirzakhani Nafchi",{"name":257,"orcid":8},"Salman Mirzaee",{"tldr":259,"method":260,"finding":261,"direction":183,"opportunity":262},"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":265,"title":266,"url":267,"summary":268,"summary_zh":269,"content":8,"source_name":270,"source_url":267,"published_at":271,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":272,"score_detail":273,"sources":275,"tags":277,"primary_topic":280,"secondary_topics":286,"classification":293,"search_phrases":315,"slug":318,"view_count":58,"doi":319,"paper":320,"created_at":340},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.","2026-10-06T00:00:00Z",62,{"impact":16,"substance":120,"depth":17,"authority":196,"freshness":19,"relevant":20,"comment":274},"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。",[276],{"name":270,"url":267},[25,278,26,28,279],"物联网","水稻种植",{"code":281,"slug":282,"name":283,"path":284,"parent_code":285},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[287,292],{"code":288,"slug":289,"name":290,"path":291,"parent_code":108},"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":13,"version":39,"method":40,"evidence":294},{"candidates":295,"agricultural_evidence":46},[296,302,307,311],{"code":288,"score":120,"supported":46,"title":297,"abstract":299,"tags":301},[298],"internet of things",[278,298,300],"IoT",[278],{"code":281,"score":17,"supported":46,"title":303,"abstract":305,"tags":306},[304],"paddy",[283,282],[279],{"code":31,"score":18,"supported":46,"title":308,"abstract":309,"tags":310},[32],[137,32],[],{"code":44,"score":19,"supported":46,"title":312,"abstract":313,"tags":314},[],[26,141],[25,26],[316,317],"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":319,"openalex_id":321,"authors":322,"venue":270,"cited_by_count":79,"oa_url":267,"card":335,"direction":80,"ingested_from":81},"W7220841659",[323,325,328,330,333],{"name":324,"orcid":8},"Fariha Laqania",{"name":326,"orcid":327},"Oviyanti Mulyani","https:\u002F\u002Forcid.org\u002F0000-0003-3941-8896",{"name":329,"orcid":8},"Ade Setiawan",{"name":331,"orcid":332},"Pujawati Suryatmana","https:\u002F\u002Forcid.org\u002F0000-0002-4012-8645",{"name":334,"orcid":8},"Wahyu Daradjat",{"tldr":336,"method":337,"finding":338,"direction":80,"opportunity":339},"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":342,"title":343,"url":344,"summary":345,"summary_zh":346,"content":8,"source_name":347,"source_url":344,"published_at":271,"category":11,"cover_url":8,"hotness":12,"is_selected":46,"score":348,"score_detail":349,"sources":352,"tags":354,"primary_topic":357,"secondary_topics":358,"classification":360,"search_phrases":388,"slug":391,"view_count":20,"doi":392,"paper":393,"created_at":423},4345,"HyBEAR: a hyperspectral benchmark for bare soil detection","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fessd-18-7301-2026","Detecting bare soil areas is an important step in the analysis of Earth observation data in a variety of Precision Agriculture (PA) applications focused on quantifying soil properties and assessing soil quality. In this paper, we introduce the HyBEAR benchmark – a novel large-scale collection of high-resolution hyperspectral aerial images (with 2 m ground sampling distance) accompanied by manual bare soil annotations verified by domain experts. Usually, the bare soil detection problem is tackled at the pixel level, meaning that detection methods classify all pixels as either bare soil or background. In contrast to this approach, we provide pixel-level annotations for the entire agricultural parcels (if the parcel is labeled as bare soil, then all pixels within that parcel are labeled accordingly), and aim to support the development of methods that identify entire fields with no vegetation. Commonly, such fields undergo further analysis to determine specific soil parameters and characteristics that are important when planning various PA activities, such as fertilization. The HyBEAR benchmark includes (i) the largest-to-date (108 064 591 pixels, corresponding to 43 225 ha) and most heterogeneous dataset for bare soil detection, as well as (ii) the validation procedure (training-test splits and quality metrics) and a set of baseline results, obtained for a set of machine learning bare soil detection models. From the FULL collection of 1954 images in HyBEAR, which we divided into 5 spatially-disjoint folds, we additionally selected a random, stratified subset (MINI) of the images which may be useful for designing and verifying bare soil detection algorithms. Overall, HyBEAR is a step toward standardizing the way the community builds and confronts bare soil detection algorithms in a thorough, reproducible, and unbiased way. The dataset is publicly available at Zenodo: https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.17607897 (Wijata et al., 2025).","在多种精准农业（PA）应用中，裸土区域检测是对地观测数据分析的重要步骤，其重点在于量化土壤性质和评估土壤质量。本文介绍了HyBEAR基准——一个全新的大规模高分辨率高光谱航空影像数据集（地面采样距离为2 m），并附有经领域专家验证的人工裸土标注。通常，裸土检测问题在像素层面进行处理，即检测方法将所有像素分类为裸土或背景。与此方法不同，我们为整个农业地块提供像素级标注（如果地块被标记为裸土，则该地块内的所有像素均被相应标记），旨在支持开发能够识别无植被整块田地的方法。通常，此类田地会经过进一步分析以确定特定的土壤参数和特征，这些在规划施肥等各种精准农业活动时至关重要。HyBEAR基准包含：（i）迄今为止规模最大（108 064 591个像素，对应43 225公顷）且最具异质性的裸土检测数据集；（ii）验证流程（训练-测试划分和质量指标）以及一组基线结果，这些结果是在一组机器学习裸土检测模型上获得的。从HyBEAR中全部1954幅图像（我们将其划分为5个空间不相交的折）中，我们额外选择了一个随机分层子集（MINI），该子集可能有助于设计和验证裸土检测算法。总体而言，HyBEAR朝着以全面、可复现且无偏见的方式标准化社区构建和对比裸土检测算法的方向迈出了一步。该数据集已在Zenodo上公开：https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.17607897（Wijata et al., 2025）。","Earth system science data",81,{"impact":120,"substance":350,"depth":120,"authority":18,"freshness":196,"relevant":20,"comment":351},22,"发布迄今最大规模高光谱裸土检测基准数据集，含像素级标注与基线结果，对精准农业土壤分析有实质推动。",[353],{"name":347,"url":344},[25,355,356,27,28],"农业人工智能","高光谱",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[359],{"code":57,"slug":105,"name":106,"path":107,"parent_code":108},{"status":38,"locked":13,"version":39,"method":40,"evidence":361},{"candidates":362,"agricultural_evidence":46},[363,367,372,377,381],{"code":31,"score":18,"supported":46,"title":364,"abstract":365,"tags":366},[32],[137,32],[],{"code":57,"score":45,"supported":46,"title":368,"abstract":370,"tags":371},[369],"hyperspectral",[369],[27,356],{"code":44,"score":373,"supported":46,"title":374,"abstract":375,"tags":376},7,[],[26,141],[25],{"code":145,"score":373,"supported":46,"title":378,"abstract":379,"tags":380},[],[148,149],[355],{"code":382,"score":84,"supported":46,"title":383,"abstract":384,"tags":387},"F3",[],[385,386],"施肥","fertilization",[],[389,390],"HyBEAR 裸土检测 高光谱","裸土检测 基准数据集 遥感","HyBEAR裸土检测高光谱-4345","10.5194\u002Fessd-18-7301-2026",{"doi":392,"openalex_id":394,"authors":395,"venue":347,"cited_by_count":79,"oa_url":417,"card":418,"direction":80,"ingested_from":81},"W7220738771",[396,399,402,405,408,411,414],{"name":397,"orcid":398},"Agata M. Wijata","https:\u002F\u002Forcid.org\u002F0000-0001-6180-9979",{"name":400,"orcid":401},"Bogdan Ruszczak","https:\u002F\u002Forcid.org\u002F0000-0003-1089-1778",{"name":403,"orcid":404},"Adriana Niepala","https:\u002F\u002Forcid.org\u002F0009-0005-8274-9852",{"name":406,"orcid":407},"Michał Gumiela","https:\u002F\u002Forcid.org\u002F0009-0000-2501-2786",{"name":409,"orcid":410},"Krzysztof Smykała","https:\u002F\u002Forcid.org\u002F0000-0003-1970-5388",{"name":412,"orcid":413},"Nicolas Longépé","https:\u002F\u002Forcid.org\u002F0000-0002-6832-3274",{"name":415,"orcid":416},"Jakub Nalepa","https:\u002F\u002Forcid.org\u002F0000-0002-4026-1569","https:\u002F\u002Fessd.copernicus.org\u002Farticles\u002F18\u002F7301\u002F2026\u002Fessd-18-7301-2026.pdf",{"tldr":419,"method":420,"finding":421,"direction":183,"opportunity":422},"提出HyBEAR高光谱航空影像基准数据集，用于整田块裸土检测。","2米分辨率高光谱航空影像，专家验证的田块级像素标注，含训练测试划分与基线模型。","构建了迄今最大（43225公顷）且异质的裸土检测数据集，并给出可复现的评估流程与基线结果。","可基于该基准探索田块级裸土检测的迁移学习与多光谱\u002F高光谱融合方法，并关联土壤参数反演。","2026-10-07T23:30:09.147409Z",{"id":425,"title":426,"url":427,"summary":428,"summary_zh":8,"content":8,"source_name":429,"source_url":8,"published_at":430,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":431,"score_detail":432,"sources":434,"tags":436,"primary_topic":437,"secondary_topics":441,"classification":443,"search_phrases":465,"slug":468,"view_count":457,"doi":8,"paper":469,"created_at":478},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":120,"substance":350,"depth":120,"authority":18,"freshness":84,"relevant":20,"comment":433},"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。",[435],{"name":429,"url":427},[25,355,148,27,28],{"code":145,"slug":438,"name":439,"path":440,"parent_code":108},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[442],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":444},{"candidates":445,"agricultural_evidence":46},[446,452,456,461],{"code":145,"score":96,"supported":46,"title":447,"abstract":449,"tags":451},[448],"artificial intelligence",[450,148],"深度学习",[355,148],{"code":31,"score":52,"supported":46,"title":453,"abstract":454,"tags":455},[32],[137],[],{"code":57,"score":457,"supported":46,"title":458,"abstract":459,"tags":460},4,[],[27],[27],{"code":44,"score":20,"supported":13,"title":462,"abstract":463,"tags":464},[],[],[25],[466,467],"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":470,"venue":8,"cited_by_count":79,"oa_url":8,"card":471,"direction":475,"ingested_from":477},[],{"tldr":472,"method":473,"finding":474,"direction":475,"opportunity":476},"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。","农业人工智能与决策模型","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。","agent","2026-10-06T00:05:04.976398Z",{"id":480,"title":481,"url":482,"summary":483,"summary_zh":484,"content":8,"source_name":485,"source_url":482,"published_at":486,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":487,"score_detail":488,"sources":491,"tags":493,"primary_topic":495,"secondary_topics":496,"classification":502,"search_phrases":546,"slug":549,"view_count":20,"doi":550,"paper":551,"created_at":563},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":120,"substance":350,"depth":489,"authority":45,"freshness":19,"relevant":20,"comment":490},19,"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。",[492],{"name":485,"url":482},[25,355,26,494,28],"无线传感器网络",{"code":288,"slug":289,"name":290,"path":291,"parent_code":108},[497,498],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"code":229,"slug":499,"name":500,"path":501,"parent_code":35},"sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture",{"status":38,"locked":13,"version":39,"method":40,"evidence":503},{"candidates":504,"agricultural_evidence":46},[505,511,515,519,523,530,536,542],{"code":288,"score":17,"supported":46,"title":506,"abstract":508,"tags":510},[507],"wireless sensor",[509,507],"无线传感",[494],{"code":31,"score":18,"supported":46,"title":512,"abstract":513,"tags":514},[32],[137,32],[],{"code":229,"score":19,"supported":46,"title":516,"abstract":517,"tags":518},[233],[],[],{"code":44,"score":19,"supported":46,"title":520,"abstract":521,"tags":522},[],[26,141],[25,26],{"code":524,"score":84,"supported":46,"title":525,"abstract":526,"tags":529},"A3",[],[527,528],"玉米","corn",[],{"code":222,"score":84,"supported":46,"title":531,"abstract":532,"tags":535},[],[533,534],"大豆","soybean",[],{"code":537,"score":153,"supported":46,"title":538,"abstract":539,"tags":541},"F5",[],[540],"harvester",[],{"code":145,"score":20,"supported":13,"title":543,"abstract":544,"tags":545},[],[],[355],[547,548],"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":550,"openalex_id":552,"authors":553,"venue":485,"cited_by_count":79,"oa_url":557,"card":558,"direction":80,"ingested_from":81},"W7214927134",[554],{"name":555,"orcid":556},"Muhammad Ovais Akhter","https:\u002F\u002Forcid.org\u002F0000-0002-8928-4489","https:\u002F\u002Fph.pollub.pl\u002Findex.php\u002Fiapgos\u002Farticle\u002Fdownload\u002F9467\u002F5631",{"tldr":559,"method":560,"finding":561,"direction":80,"opportunity":562},"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"]