[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2787":3,"related-2787":96},{"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":31,"secondary_topics":37,"classification":38,"search_phrases":67,"slug":70,"view_count":21,"doi":71,"paper":72,"created_at":95},2787,"Bioremediation of mining waste and tailings in Sub-Saharan Africa: a PRISMA-2020 evidence synthesis from bench to field","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffagro.2026.1939826","Sub-Saharan Africa is a global centre of copper, cobalt, gold, and tin production, and mining practices — especially artisanal and small-scale mining — have left soils, sediments, water, and food crops contaminated with arsenic, lead, mercury, copper, and cobalt, often above WHO thresholds. Conventional physico-chemical remediation is typically unaffordable in these settings and poorly suited to the diffuse, multi-metal contamination characteristic of SSA mining landscapes. This PRISMA-2020-aligned systematic review synthesises the evidence base for bioremediation of mining-derived waste in SSA, identifying the biophysical, operational, and socio-economic conditions under which laboratory innovation translates to field-realistic application. Following PRISMA 2020, we synthesised 75 sources (69 articles, 6 grey-literature reports; January 2020 to mid-2026) drawn from six databases plus targeted grey-literature channels. Two reviewers screened, extracted, and appraised each source, with third-reviewer adjudication. Risk of bias was assessed using an adapted CASP checklist. Four findings emerged; First, SSA harbours distinctive autochthonous resources: Cu\u002FCo-tolerant mycobiota dominated by Basidiomycota (64.6%) and Ascomycota (21.3%), native hyperaccumulator flora (shoot Cu up to ~13,700 mg kg –1 in Aeollanthus subacaulis var. linearis ), and arbuscular mycorrhizal consortia. Second, laboratory efficacy is high: bioleaching achieves 65–88% Cu recovery; sulfate-reducing bacteria remove 70–95% of dissolved metals; biosorption with fungal\u002Fagricultural-waste biomass removes 60–90% of Cu, Pb, and Cd. Third, field efficacy is consistently attenuated (factors ≈0.20–0.55), driven by ASM governance fragmentation, francophone\u002Flusophone indexing deficits, land-tenure insecurity, weak regulatory enforcement, and subsistence-economy coupling. Fourth, mineral-recovery economics can partly underwrite remediation, but no techno-economic case alone closes the lab-to-field gap. Operationalising SSA’s biological capital requires tiered lab-to-pilot-to-field pipelines with mandatory geochemical baselines, ASM formalisation backed by enforceable closure bonds, and active investment in francophone\u002Flusophone bibliographic coverage. Research priorities are SSA-specific reporting protocols, longitudinal climate-resilient field monitoring, life-cycle–techno-economic integration, vulnerability-differentiated impact assessment, and integration of remote sensing (Sentinel-2, hyperspectral, Landsat\u002FMODIS archives) with biological endpoints.","撒哈拉以南非洲是全球铜、钴、金和锡的生产中心，采矿活动——尤其是手工和小规模采矿——已使土壤、沉积物、水体和粮食作物受到砷、铅、汞、铜和钴的污染，且往往超过世界卫生组织（WHO）的阈值。传统的物理化学修复方法在这些地区通常难以负担，且不适合撒哈拉以南非洲采矿景观所特有的弥散性多金属污染。本系统综述遵循PRISMA-2020指南，综合了撒哈拉以南非洲采矿废弃物生物修复的证据基础，识别了实验室创新转化为实地应用所需的生物物理、操作和社会经济条件。按照PRISMA 2020框架，我们综合了75篇文献（69篇期刊论文、6篇灰色文献报告；2020年1月至2026年中），来源于六个数据库及定向灰色文献渠道。两名评审员对每篇文献进行筛选、数据提取和质量评价，并由第三名评审员裁定分歧。偏倚风险采用改编的CASP清单进行评估。研究得出四项发现：第一，撒哈拉以南非洲拥有独特的本土资源：以担子菌门（Basidiomycota，64.6%）和子囊菌门（Ascomycota，21.3%）为主的耐铜\u002F钴真菌群落、本土超富集植物区系（Aeollanthus subacaulis var. linearis地上部铜含量高达约13,700 mg kg⁻¹），以及丛枝菌根共生体。第二，实验室效能较高：生物浸出可实现65–88%的铜回收率；硫酸盐还原菌可去除70–95%的溶解态金属；利用真菌\u002F农业废弃物生物质的生物吸附可去除60–90%的铜、铅和镉。第三，实地效能持续衰减（衰减因子约为0.20–0.55），驱动因素包括手工和小规模采矿治理碎片化、法语\u002F葡语文献索引不足、土地权属不安全、监管执法薄弱以及生计经济耦合。第四，矿物回收经济收益可部分支撑修复成本，但仅靠技术经济论证无法弥合实验室到实地的差距。要将撒哈拉以南非洲的生物资本投入实际应用，需要建立实验室—中试—实地的分级管道，强制实施地球化学基线监测，在可执行的闭矿保证金支持下推进手工和小规模采矿正规化，并积极投资于法语\u002F葡语文献覆盖。研究优先方向包括：撒哈拉以南非洲专属报告规范、纵向气候韧性实地监测、生命周期—技术经济整合、脆弱性差异化影响评估，以及遥感（Sentinel-2、高光谱）技术的整合。",null,"Frontiers in Agronomy","2026-09-17T00:00:00Z","论文",10,false,84,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},18,23,19,14,1,"PRISMA-2020系统综述揭示撒哈拉以南非洲矿区生物修复从实验室到田间的衰减规律，方法严谨、数据翔实，对农业环境治理与污染耕地修复有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"土壤修复","农业环境","矿山治理","重金属污染","生物修复",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,53,60],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":52},9,true,[],[49,50,51],"遥感","remote sensing","hyperspectral",[],{"code":54,"score":55,"supported":46,"title":56,"abstract":57,"tags":59},"H2",5,[],[58],"土壤",[26,29],{"code":61,"score":62,"supported":46,"title":63,"abstract":64,"tags":66},"H5",3,[],[65],"农业废弃物",[],[68,69],"重金属污染 农业环境 土壤修复 生物修复","重金属污染 农业环境","重金属污染农业环境土壤修复生物修复-2787","10.3389\u002Ffagro.2026.1939826",{"doi":71,"openalex_id":73,"authors":74,"venue":10,"cited_by_count":86,"oa_url":6,"card":87,"direction":93,"ingested_from":94},"W7213466518",[75,77,79,82,84],{"name":76,"orcid":9},"Daniel Masumbuko Nhunda",{"name":78,"orcid":9},"Mohanadoss Ponraj",{"name":80,"orcid":81},"Charles Bwalya Chisanga","https:\u002F\u002Forcid.org\u002F0000-0002-7388-5415",{"name":83,"orcid":9},"Subbaiya Ramasamy",{"name":85,"orcid":9},"Stephen Syampungani",0,{"tldr":88,"method":89,"finding":90,"direction":91,"opportunity":92},"系统综述撒哈拉以南非洲采矿废弃物生物修复证据，揭示实验室高效但田间效果衰减的鸿沟。","PRISMA-2020系统综述，75篇文献，CASP偏倚评估，涵盖生物浸出、SR","实验室金属去除率60-95%，但田间效率衰减至0.20-0.55，受治理与土地权属制约。","农业绿色发展与碳","可探索遥感与生物修复耦合的田间监测，以及法语\u002F葡语区数据缺口与全生命周期经济评估。","农业遥感与作物表型","openalex","2026-09-17T23:30:34.520300Z",{"total":97,"page":21,"page_size":97,"items":98},6,[99,181,295,387,449,498],{"id":100,"title":101,"url":102,"summary":103,"summary_zh":104,"content":9,"source_name":105,"source_url":102,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":107,"score_detail":108,"sources":111,"tags":113,"primary_topic":118,"secondary_topics":119,"classification":120,"search_phrases":149,"slug":152,"view_count":86,"doi":153,"paper":154,"created_at":180},2533,"Quantifying the Environmental Impact of Artisanal and Small‐Scale Mining: An Integrated Approach for Sustainable Development Insights","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsd.71693","ABSTRACT Artisanal and small‐scale mining (ASM) provides livelihoods for over 44.5 million people globally, yet its largely unregulated nature causes severe environmental degradation in host communities. In Nigeria, where ASM accounts for over 90% of solid mineral production, the sector increasingly encroaches on agricultural land. Despite growing recognition of ASM's environmental impacts across West Africa, no study has yet applied the novel SDGSAT‐1 satellite purpose‐built for SDG monitoring at 10 m resolution to quantify ASM land degradation in Nigeria, and few studies simultaneously integrate multi‐sensor remote sensing, in‐situ soil contamination assessment, and hydrological transport modelling within a single integrated framework. This study addresses that gap. This study integrates SDGSAT‐1 Multispectral Imager and Landsat 9 imagery (both acquired 12 January 2024) with field‐based soil sampling (November 2023, dry season) and GIS hydrological modelling for Igbojaiye community, Oyo State, Nigeria. Eight vegetation, water and soil indices (NDVI, EVI, SAVI, TSAVI, TVI, NDWI, BSI) and Land Surface Temperature were derived from satellite imagery. Soil samples from eight mining‐affected points and four undisturbed control points were analyzed for physicochemical properties and heavy metals (Fe, Pb, Zn) using Atomic Absorption Spectrometry. Contamination Factor, Geoaccumulation Index, Pollution Load Index, and a Potential Ecological Risk Index were computed. A Digital Elevation Model was used to delineate contaminant transport pathways. ASM‐affected areas exhibited severe vegetation loss (68% of land within 500 m of mining pits classified as Degraded; mean NDVI −0.08 vs. +0.18 in control areas) and elevated Land Surface Temperature (mean 35.2°C ± 1.3°C vs. 28.9°C ± 0.8°C in controls). Mining soils showed markedly degraded physicochemical properties (organic carbon 4× lower, moisture 5× lower, pH more acidic). Lead exhibited very high contamination (mean Contamination Factor 10.0; Geoaccumulation Index Class 3: moderately to heavily contaminated) with a statistically significant distance‐decay gradient ( r = −0.60, p = 0.04). No metal exceeded WHO\u002FFAO soil limits in absolute terms. Hydrological modelling identified direct flow pathways from mining pits to downstream farmland and waterbodies. ASM in Igbojaiye initiates a cascade of compounding degradation, soil toxicity, microclimatic disruption, and hydrological pollutant dispersal that collectively undermine agricultural productivity and water security, directly impeding SDGs 2, 6, 13, and 15. This study represents one of the first applications of SDGSAT‐1 for ASM monitoring in Nigeria, establishing a replicable multi‐sensor framework for evidence‐based environmental governance of ASM in data‐scarce sub‐Saharan African contexts. Remediation priorities include sediment traps at mining pit outflows, phytoremediation with native metal‐tolerant species, and satellite‐based biannual monitoring protocols.","手工和小规模采矿（ASM）为全球超过4450万人提供了生计，但其基本上不受监管的性质对所在社区造成了严重的环境退化。在尼日利亚，手工和小规模采矿占固体矿产产量的90%以上，该行业日益侵占农业用地。尽管西非地区对ASM环境影响的认知不断增加，但尚无研究应用专为可持续发展目标监测而设计的10米分辨率SDGSAT-1卫星来量化尼日利亚的ASM土地退化，且很少有研究将多传感器遥感、原位土壤污染评估和水文输移建模同时整合于单一综合框架中。本研究填补了这一空白。本研究将SDGSAT-1多光谱成像仪和Landsat 9影像（均于2024年1月12日获取）与实地土壤采样（2023年11月，旱季）及GIS水文建模相结合，以尼日利亚奥约州Igbojaiye社区为研究区。从卫星影像中提取了八个植被、水体和土壤指数（NDVI、EVI、SAVI、TSAVI、TVI、NDWI、BSI）及地表温度。采用原子吸收光谱法对来自八个受采矿影响点和四个未受干扰对照点的土壤样品进行了理化性质和重金属（Fe、Pb、Zn）分析。计算了污染因子、地累积指数、污染负荷指数和潜在生态风险指数。利用数字高程模型划定了污染物输移路径。受ASM影响的区域表现出严重的植被丧失（采矿坑500米范围内68%的土地被归类为退化；NDVI均值为−0.08，对照区为+0.18）和升高的地表温度（均值35.2°C ± 1.3°C，对照区为28.9°C ± 0.8°C）。矿区土壤理化性质显著退化（有机碳低4倍，含水量低5倍，pH更偏酸性）。铅表现出极高的污染水平（污染因子均值10.0；地累积指数3级：中度至重度污染），并具有统计学显著的距离衰减梯度（r = −0.60，p = 0.04）。就绝对值而言，无金属超过WHO\u002FFAO土壤限值。水文建模识别出从采矿坑到下游农田和水体的直接径流路径。Igbojaiye的ASM引发了一系列叠加退化、土壤毒性、微气候扰动和水文污染物扩散的级联效应，共同导致","Sustainable Development","2026-09-14T00:00:00Z",82,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":109,"relevant":21,"comment":110},8,"首次将SDGSAT-1遥感与土壤、水文模型整合量化尼日利亚小规模采矿对农地的复合退化，方法可复用于数据稀缺地区农业环境治理，对农业遥感与耕地保护主题有聚合价值。",[112],{"name":105,"url":102},[114,115,116,117,27],"数字乡村","可持续发展","耕地保护","遥感监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":121},{"candidates":122,"agricultural_evidence":46},[123,128,133,139,144],{"code":32,"score":13,"supported":46,"title":124,"abstract":125,"tags":127},[],[49,50,126],"multispectral",[117],{"code":54,"score":97,"supported":46,"title":129,"abstract":130,"tags":132},[],[58,131],"soil",[],{"code":134,"score":62,"supported":46,"title":135,"abstract":136,"tags":138},"I4",[],[137],"GIS",[],{"code":140,"score":21,"supported":14,"title":141,"abstract":142,"tags":143},"H1",[],[],[116],{"code":145,"score":21,"supported":14,"title":146,"abstract":147,"tags":148},"I6",[],[],[114],[150,151],"可持续发展 农业环境 数字乡村 耕地保护","可持续发展 农业环境","可持续发展农业环境数字乡村耕地保护-2533","10.1002\u002Fsd.71693",{"doi":153,"openalex_id":155,"authors":156,"venue":105,"cited_by_count":86,"oa_url":9,"card":175,"direction":93,"ingested_from":94},"W7213179964",[157,160,162,165,167,169,172],{"name":158,"orcid":159},"Sehinde Akinbiola","https:\u002F\u002Forcid.org\u002F0000-0001-9602-2420",{"name":161,"orcid":9},"Julia Blumensaat",{"name":163,"orcid":164},"Ayobami Salami","https:\u002F\u002Forcid.org\u002F0000-0002-7567-9762",{"name":166,"orcid":9},"Jürgen Runge",{"name":168,"orcid":9},"Tella Iyiola",{"name":170,"orcid":171},"Olamide Omolafe Ogunremi","https:\u002F\u002Forcid.org\u002F0000-0002-6776-4983",{"name":173,"orcid":174},"Ayomide Emmanuel Olubaju","https:\u002F\u002Forcid.org\u002F0000-0003-2118-6759",{"tldr":176,"method":177,"finding":178,"direction":93,"opportunity":179},"用SDGSAT-1等多源遥感与实地采样量化尼日利亚手工采矿对土地与农田的环境退化。","SDGSAT-1与Landsat 9影像、8种植被水体土壤指数、土壤重金属AAS","矿区植被严重退化、地表温度升高、铅污染极重，并存在向农田与水体扩散的水文通道。","可将SDGSAT-1 10米影像与土壤、水文模型耦合，构建矿区农田退化与重金属风险的早期预警框架。","2026-09-15T23:30:20.829150Z",{"id":182,"title":183,"url":184,"summary":185,"summary_zh":186,"content":9,"source_name":187,"source_url":184,"published_at":188,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":189,"score_detail":190,"sources":194,"tags":196,"primary_topic":202,"secondary_topics":203,"classification":209,"search_phrases":267,"slug":270,"view_count":86,"doi":271,"paper":272,"created_at":294},5582,"AI-Based RGB Imaging and 14-Channel Multispectral Food Quality Scanner","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85255","Food-quality assessment plays an important role in food safety, agricultural processing, storage, and supply-chain management. Conventional inspection methods depend on visual judgement or laboratory-based testing, which may require considerable time, specialised equipment, and manual effort. Recent developments in computer vision, multispectral sensing, and artificial intelligence (AI) provide opportunities for automated, non-destructive food-quality assessment. This paper reviews the application of RGB imaging, multispectral sensing, machine learning, and deep learning for fruit-ripeness estimation, surfacedefect detection, grain classification, and food-authenticity assessment. The reviewed literature demonstrates that RGB imaging can extract visible features such as colour, shape, and texture, whereas multispectral sensing provides wavelength-dependent information associated with food characteristics. Published studies have reported promising results in tomato-ripeness classification, apple-defect detection, rice-grain analysis, and honey-adulteration screening. Based on these findings, a compact food-quality scanner integrating an RGB camera, a 14-channel multispectral sensor, controlled illumination, and an embedded processing unit is proposed. The review discusses sensor integration, data preprocessing, feature fusion, classification methods, evaluation metrics, and implementation challenges. It also identifies the need for broader datasets, standardised illumination, independent validation, and resource-efficient AI models to improve the reliability of portable food-inspection systems.","食品质量评估在食品安全、农产品加工、储运和供应链管理中发挥着重要作用。传统检测方法依赖人工目视判断或实验室检测，往往需要较长时间、专用设备和人工投入。近年来，计算机视觉、多光谱传感和人工智能（AI）的发展为自动化、无损食品质量评估提供了新的机遇。本文综述了RGB成像、多光谱传感、机器学习和深度学习在果实成熟度估计、表面缺陷检测、谷物分类和食品真实性评估中的应用。所综述的文献表明，RGB成像能够提取颜色、形状和纹理等可见特征，而多光谱传感则可提供与食品特性相关的波长依赖信息。已发表研究在番茄成熟度分类、苹果缺陷检测、稻米籽粒分析和蜂蜜掺假筛查等方面取得了良好结果。基于上述研究结果，本文提出一种集成了RGB相机、14通道多光谱传感器、受控照明和嵌入式处理单元的紧凑型食品质量扫描仪。综述讨论了传感器集成、数据预处理、特征融合、分类方法、评价指标和实现挑战，并指出需要更广泛的数据集、标准化照明、独立验证和资源高效的AI模型，以提高便携式食品检测系统的可靠性。","International Journal for Research in Applied Science and Engineering Technology","2026-10-21T00:00:00Z",65,{"impact":191,"substance":17,"depth":192,"authority":45,"freshness":13,"relevant":21,"comment":193},12,16,"综述性论文，提出集成RGB与14通道多光谱的便携食品质量扫描仪方案，方法框架清晰但缺乏自有实验数据，属细分领域技术进展，可作参考但非重大突破。",[195],{"name":187,"url":184},[197,198,199,200,201],"智慧农业","农业人工智能","无损检测","多光谱成像","食品质量检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[204],{"code":205,"slug":206,"name":207,"path":208,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":39,"locked":14,"version":40,"method":41,"evidence":210},{"candidates":211,"agricultural_evidence":46},[212,216,227,235,242,249,256,262],{"code":32,"score":191,"supported":46,"title":213,"abstract":214,"tags":215},[126],[126],[200],{"code":205,"score":13,"supported":46,"title":217,"abstract":218,"tags":226},[],[219,220,221,222,223,224,225],"深度学习","机器学习","计算机视觉","machine learning","deep learning","artificial intelligence","computer vision",[198],{"code":228,"score":229,"supported":46,"title":230,"abstract":231,"tags":234},"E4",7,[],[232,233],"食品安全","food safety",[199],{"code":236,"score":97,"supported":46,"title":237,"abstract":238,"tags":241},"A1",[],[239,240],"稻米","rice",[],{"code":243,"score":97,"supported":46,"title":244,"abstract":245,"tags":248},"B1",[],[246,247],"番茄","tomato",[],{"code":250,"score":97,"supported":46,"title":251,"abstract":252,"tags":255},"B2",[],[253,254],"苹果","apple",[],{"code":257,"score":62,"supported":46,"title":258,"abstract":259,"tags":261},"E1",[],[260],"农产品加工",[],{"code":263,"score":21,"supported":14,"title":264,"abstract":265,"tags":266},"I1",[],[],[197],[268,269],"RGB成像 多光谱 食品质量","14通道 多光谱 扫描仪","RGB成像多光谱食品质量-5582","10.22214\u002Fijraset.2026.85255",{"doi":271,"openalex_id":273,"authors":274,"venue":187,"cited_by_count":86,"oa_url":184,"card":287,"direction":293,"ingested_from":94},"W7223139905",[275,277,279,281,283,285],{"name":276,"orcid":9},"Eshwari Take",{"name":278,"orcid":9},"Avanish Raut",{"name":280,"orcid":9},"Vedant Joshi",{"name":282,"orcid":9},"Shravani Akolkar",{"name":284,"orcid":9},"R. M. Gharat",{"name":286,"orcid":9},"Shruti Dive",{"tldr":288,"method":289,"finding":290,"direction":291,"opportunity":292},"综述RGB与多光谱成像结合AI的食品品质无损检测，并提出一款集成14通道多光谱传感器的紧凑型扫描仪方","综述RGB成像、多光谱传感与机器学习\u002F深度学习在食品检测中的应用，并提出传感器集","RGB与多光谱融合在番茄成熟度、苹果缺陷、稻米分类和蜂蜜掺假检测中表现良好。","农业人工智能与决策模型","可研究多源光谱特征融合与轻量化模型，构建标准化数据集和便携设备独立验证体系。","数字乡村与农业信息化","2026-10-22T11:20:48.684398Z",{"id":296,"title":297,"url":298,"summary":299,"summary_zh":300,"content":9,"source_name":301,"source_url":298,"published_at":188,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":302,"score_detail":303,"sources":306,"tags":308,"primary_topic":312,"secondary_topics":318,"classification":326,"search_phrases":355,"slug":358,"view_count":86,"doi":359,"paper":360,"created_at":386},5573,"Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128375","Accurate estimation of nitrogen (N) concentration in alfalfa ( Medicago sativa L .) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance ( R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.","准确估算紫花苜蓿（Medicago sativa L.）的氮浓度有助于饲草品质评估和收获期安排。经验性遥感模型常因三维冠层结构的混杂效应而出现过拟合。引入结构参数可以稳定生化反演，但同步开展原位形态学测量难以大规模获取。我们开发了形态感知潜在嵌入网络（Morphology-Aware Latent Embedding Network，MALENet），这是一个将生理与形态机制同高光谱数据相结合的混合框架。农业技术转移决策支持系统（Decision Support System for Agrotechnology Transfer，DSSAT）与PROSAIL辐射传输模型耦合，生成了结构信息丰富的合成高光谱先验数据。在此基础上，预训练了一个带有挤压—激励（Squeeze-and-Excitation，SE）注意力机制的卷积神经网络（convolutional neural network，CNN），将生态生理学知识提炼为稠密潜在表征。随后，将这一机制性导出的嵌入与在不同田间条件下采集的经验性高光谱测量数据相融合。利用河西走廊多年田间样方进行的评估显示，相较于传统机器学习和深度学习模型（LASSO-ANN：R² = 0.61；CNN：R² = 0.66），预测性能有所提升（R² = 0.71，RMSE = 0.30%）。特征归因表明，MALENet利用近红外多次散射（归因占比58.3%）作为结构代理，以校准氮浓度反演，抵御冠层结构的混杂效应。尽管受样本量和PROSAIL一维冠层假设的限制，这些发现表明，物理引导的深度学习有潜力利用结构信息，在精准饲草管理中进行农业性状反演。","European Journal of Agronomy",80,{"impact":192,"substance":304,"depth":17,"authority":20,"freshness":13,"relevant":21,"comment":305},22,"该研究将作物模型与辐射传输模型耦合生成结构先验，并结合无人机高光谱数据反演苜蓿氮素，方法新颖、田间验证扎实，对精准饲草管理有参考价值，值得进入每日精选。",[307],{"name":301,"url":298},[197,198,309,310,311],"农业遥感","氮素监测","紫花苜蓿",{"code":313,"slug":314,"name":315,"path":316,"parent_code":317},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[319,320],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":321,"slug":322,"name":323,"path":324,"parent_code":325},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed","C",{"status":39,"locked":14,"version":40,"method":41,"evidence":327},{"candidates":328,"agricultural_evidence":46},[329,333,340,346,351],{"code":32,"score":17,"supported":46,"title":330,"abstract":331,"tags":332},[51],[49,50,51],[309],{"code":313,"score":334,"supported":46,"title":335,"abstract":337,"tags":339},15,[336],"alfalfa",[338,336],"苜蓿",[311],{"code":321,"score":13,"supported":46,"title":341,"abstract":342,"tags":345},[],[343,338,344],"饲草","forage",[311],{"code":205,"score":13,"supported":46,"title":347,"abstract":348,"tags":350},[],[219,220,222,223,349],"decision support",[198],{"code":263,"score":21,"supported":14,"title":352,"abstract":353,"tags":354},[],[],[197],[356,357],"DSSAT PROSAIL 苜蓿 氮素","无人机 高光谱 苜蓿","DSSATPROSAIL苜蓿氮素-5573","10.1016\u002Fj.eja.2026.128375",{"doi":359,"openalex_id":361,"authors":362,"venue":301,"cited_by_count":86,"oa_url":298,"card":381,"direction":93,"ingested_from":94},"W7223154576",[363,365,367,369,371,374,376,379],{"name":364,"orcid":9},"Shuai Fu",{"name":366,"orcid":9},"Yunhao Li",{"name":368,"orcid":9},"Chunli Miao",{"name":370,"orcid":9},"Jie Liu",{"name":372,"orcid":373},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":375,"orcid":9},"Yongjia Hu",{"name":377,"orcid":378},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":380,"orcid":9},"吴彩霞",{"tldr":382,"method":383,"finding":384,"direction":93,"opportunity":385},"构建MALENet融合DSSAT-PROSAIL结构先验与无人机高光谱，提升苜蓿氮浓度估算精度。","DSSAT耦合PROSAIL生成合成高光谱先验，预训练CNN-SE网络，再融合田","MALENet精度优于传统机器学习和深度学习模型，R²达0.71，近红外多次散射作为结构代理贡献58","可探索将作物模型结构先验迁移至其他牧草或作物生化参数反演，并缓解PROSAIL一维冠层假设局限。","2026-10-22T11:20:39.622844Z",{"id":388,"title":389,"url":390,"summary":391,"summary_zh":392,"content":9,"source_name":393,"source_url":390,"published_at":188,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":394,"score_detail":395,"sources":399,"tags":401,"primary_topic":404,"secondary_topics":405,"classification":406,"search_phrases":424,"slug":427,"view_count":86,"doi":428,"paper":429,"created_at":448},5571,"Temporal Multi-Scale Spatio-Temporal Network (TMST): Polarization–Temporal Feature Selection and Multi-Scale Spatiotemporal Fusion for Crop Mapping","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202068","Accurate and timely crop mapping supports agricultural resource management, yield prediction, and food security decision-making. In this study, we propose the Temporal Multi-scale Spatio-Temporal Network (TMST), a crop mapping method that integrates multi-source remote sensing time series through polarization–temporal feature selection and multi-scale spatiotemporal fusion. TMST combines Sentinel-1 dual-polarization synthetic aperture radar (SAR), Sentinel-2 multispectral data, and vegetation indices. Its Temporal Feature Importance Explainer (TFIExplainer) selects informative polarization–time features, the Multi-Scale Attention Aggregation (MSAA) module enhances spatial representations, and the Spatio-Temporal Feature Encoder (STFE) models long-range spatial dependencies and phenological dynamics; a composite loss improves minority-class and difficult-pixel recognition. On the Xinxiang dataset, TMST achieved an overall accuracy (OA) of 0.8625 and a mean F1-score (mF1) of 0.8329. Module and data-source ablations suggested benefits of the combined modules and multi-source fusion, while a cropland mask reduced non-cropland misclassification. In direct cross-dataset evaluation, models trained on a self-constructed Austrian dataset and tested on the radar-extended Panoptic Agricultural Satellite Time Series (PASTIS-R) dataset showed that TMST outperformed the evaluated baselines under the specified source–target setting. Annual mapping of Xinxiang for 2019–2023, using year-specific samples and independently trained models, provided map-based estimates of regional cropping structures and spatial patterns. Under the evaluated settings, TMST provides an effective approach to crop classification and annually updated crop mapping.","准确的作物制图有助于农业资源管理、产量预测和粮食安全决策。本研究提出了一种时间多尺度时空网络（Temporal Multi-scale Spatio-Temporal Network，TMST），这是一种通过极化—时间特征选择和多尺度时空融合来整合多源遥感时间序列的作物制图方法。TMST结合了Sentinel-1双极化合成孔径雷达（SAR）、Sentinel-2多光谱数据和植被指数。其时间特征重要性解释器（Temporal Feature Importance Explainer，TFIExplainer）用于选择信息量丰富的极化—时间特征，多尺度注意力聚合（Multi-Scale Attention Aggregation，MSAA）模块增强空间表征，时空特征编码器（Spatio-Temporal Feature Encoder，STFE）建模长程空间依赖关系和物候动态；复合损失函数改善了少数类和困难像元的识别。在新乡数据集上，TMST取得了0.8625的总体精度（OA）和0.8329的平均F1分数（mF1）。模块和数据源消融实验表明，组合模块和多源融合具有优势，而耕地掩膜减少了非耕地误分类。在直接的跨数据集评估中，在自行构建的奥地利数据集上训练并在雷达扩展的Panoptic农业卫星时间序列（PASTIS-R）数据集上测试的模型表明，在指定的源—目标设置下，TMST优于所评估的基线方法。使用逐年样本和独立训练的模型对新乡2019—2023年进行年度制图，提供了基于制图的区域种植结构和空间格局估计。在所评估的设置下，TMST为作物分类和年度更新的作物制图提供了一种有效方法。","Agronomy",73,{"impact":191,"substance":396,"depth":397,"authority":20,"freshness":13,"relevant":21,"comment":398},20,17,"提出多源遥感融合的作物制图新方法，方法新颖、实验充分，但属细分技术进展，公共影响有限。",[400],{"name":393,"url":390},[197,402,49,403],"作物制图","SAR",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":407},{"candidates":408,"agricultural_evidence":46},[409,413,420],{"code":32,"score":13,"supported":46,"title":410,"abstract":411,"tags":412},[],[49,50,126],[49],{"code":414,"score":97,"supported":46,"title":415,"abstract":416,"tags":419},"A8",[],[417,418],"粮食安全","food security",[],{"code":263,"score":21,"supported":14,"title":421,"abstract":422,"tags":423},[],[],[197],[425,426],"Sentinel-1 Sentinel-2 作物分类","TMST 多尺度时空网络","Sentinel-1Sentinel-2作物分类-5571","10.3390\u002Fagronomy16202068",{"doi":428,"openalex_id":430,"authors":431,"venue":393,"cited_by_count":86,"oa_url":390,"card":443,"direction":93,"ingested_from":94},"W7223117203",[432,435,437,440],{"name":433,"orcid":434},"Xi Kan","https:\u002F\u002Forcid.org\u002F0000-0002-4629-9357",{"name":436,"orcid":9},"Wentao Lv",{"name":438,"orcid":439},"Lei Gong","https:\u002F\u002Forcid.org\u002F0000-0001-6429-267X",{"name":441,"orcid":442},"Linglong Zhu","https:\u002F\u002Forcid.org\u002F0000-0002-8863-9911",{"tldr":444,"method":445,"finding":446,"direction":93,"opportunity":447},"提出TMST网络，融合SAR与多光谱时序数据实现作物制图。","Sentinel-1\u002F2时序、极化-时序特征选择、多尺度注意力与时空编码器。","新乡OA达0.8625，多源融合与模块组合有效，跨数据集优于基线。","可探索极化-时序特征选择在跨区域迁移与少样本作物制图中的泛化能力。","2026-10-22T11:20:39.359881Z",{"id":450,"title":451,"url":452,"summary":453,"summary_zh":454,"content":9,"source_name":455,"source_url":452,"published_at":188,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":456,"score_detail":457,"sources":459,"tags":461,"primary_topic":464,"secondary_topics":465,"classification":467,"search_phrases":482,"slug":485,"view_count":86,"doi":486,"paper":487,"created_at":497},5570,"Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545079","Counterfactual explanations are increasingly proposed as the interface through which cooperative storage managers interrogate computer-vision yield-forecast models, yet their reliability under the satellite cloud-mask gaps that routinely afflict agricultural remote sensing has not been bounded. The bound is derived from a Lipschitz continuity assumption on the forecast model and a masking-operator perturbation analysis, and it is expressed as a function of the cloud-mask gap fraction, the model's local Lipschitz constant, and the counterfactual search tolerance. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The reliability bound degrades non-linearly as the gap fraction rises, and that the intraclass correlation of 0.64 implies that cooperative-level aggregation of counterfactual recourse is necessary to preserve explanation stability. The contribution is a decision-relevant reliability bound that storage cooperatives can use to set a maximum tolerable cloud-mask gap before counterfactual recourse is suspended.","反事实解释日益被提出作为合作式仓储管理者质询计算机视觉产量预测模型的接口，然而在农业遥感中经常出现的卫星云掩膜缺口条件下，其可靠性尚未得到界定。该界基于预测模型上的Lipschitz连续性假设和掩膜算子扰动分析推导得出，并表示为云掩膜缺口比例、模型局部Lipschitz常数和反事实搜索容差的函数。对前后对照影响估计量进行了基于重复的评估，以表征该设计在给定方差结构（组间标准差=6.0，残差标准差=4.5，组内相关系数=0.64）下的运行特征，并在2000个设计单元中估计了检验效能。可靠性界随缺口比例上升呈非线性退化，且组内相关系数0.64意味着需要对反事实救济进行合作社层面的聚合，以保持解释的稳定性。本文的贡献在于提供了一个与决策相关的可靠性界，仓储合作社可据此设定在暂停反事实救济之前可容忍的最大云掩膜缺口。","Zenodo (CERN European Organization for Nuclear Research)",67,{"impact":109,"substance":396,"depth":397,"authority":191,"freshness":13,"relevant":21,"comment":458},"该论文提出反事实解释在卫星云掩膜缺口下的可靠性边界，方法新颖且结论可用于合作社决策，但属细分方法学研究，产业影响有限。",[460],{"name":455,"url":452},[197,198,462,49,463],"产量预测","可解释性",{"code":205,"slug":206,"name":207,"path":208,"parent_code":36},[466],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":468},{"candidates":469,"agricultural_evidence":46},[470,474,478],{"code":205,"score":191,"supported":46,"title":471,"abstract":472,"tags":473},[225],[221],[198],{"code":32,"score":109,"supported":46,"title":475,"abstract":476,"tags":477},[],[49,50],[462,49],{"code":263,"score":21,"supported":14,"title":479,"abstract":480,"tags":481},[],[],[197],[483,484],"农业人工智能 产量预测 可解释性 智慧农业","农业人工智能 产量预测","农业人工智能产量预测可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":486,"openalex_id":488,"authors":489,"venue":455,"cited_by_count":86,"oa_url":452,"card":492,"direction":93,"ingested_from":94},"W7223114002",[490],{"name":491,"orcid":9},"Kadiatou Barry",{"tldr":493,"method":494,"finding":495,"direction":93,"opportunity":496},"推导卫星云掩膜缺口下反事实解释可靠性的理论界，并评估其对合作社储粮决策的影响。","基于Lipschitz连续性假设与掩膜扰动分析推导可靠性界，并用2000个设计单","缺口比例上升使可靠性非线性下降；ICC=0.64表明需合作社层级聚合以保持解释稳定。","可研究不同作物区云掩膜统计特性下的自适应解释暂停阈值与聚合策略。","2026-10-22T11:20:39.240791Z",{"id":499,"title":500,"url":501,"summary":502,"summary_zh":503,"content":9,"source_name":504,"source_url":501,"published_at":505,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":506,"score_detail":507,"sources":509,"tags":511,"primary_topic":514,"secondary_topics":515,"classification":517,"search_phrases":539,"slug":542,"view_count":86,"doi":543,"paper":544,"created_at":586},5567,"Capturing Spatial Heterogeneity Using UAV-Derived Multispectral Phenotypes and Random Regression Models Improves Genetic Evaluation in Alfalfa Breeding Trials","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.10.14.759249","Spatial heterogeneity is a major source of environmental noise in field trials, reducing genetic evaluation accuracy. Conventional spatial correction methods use field layout and plot proximity but often fail to capture in-season dynamics or leverage temporal data from high-throughput phenotyping. We hypothesized that longitudinal UAV-derived multispectral measurements could characterize field-scale environmental heterogeneity and improve genetic evaluation in breeding trials. Multispectral imagery was collected throughout the growing season across eight alfalfa trials in New York and New Mexico (20222023). Vegetation indices were evaluated for heritability and association with biomass yield using univariate and multi-trait models. Longitudinal measurements were modeled with random regression (RR) to estimate plot-specific permanent environmental effects (PEE), incorporated into genomic mixed models within a two-stage framework. Model performance was compared with conventional autoregressive (AR) and baseline (no correction) models using metrics of consistency and accuracy of estimated genetic effects across replicates. Vegetation indices showed moderate to high heritability, with GNDVI consistently outperforming NDVI and NDRE. Both univariate and multi-trait analyses revealed substantial spatial variability, particularly in New Mexico trials. RR-derived PEE captured field-scale spatial patterns and improved genetic signal recovery relative to baseline and AR models, with the largest gains under strong spatial heterogeneity and comparable performance where variability was limited. The RR framework improved reproducibility of genetic effect estimates across independent field subsets without compromising agreement with full-trial evaluations. These findings show that integrating UAV data via random regression enhances genetic signal recovery and selection accuracy, with broader potential for precision agriculture and biosensor-driven crop management.","空间异质性是田间试验中环境噪声的主要来源，会降低遗传评估的准确性。传统空间校正方法利用田间布局和小区邻近性，但往往无法捕捉生育期内的动态变化，也未能充分利用高通量表型分析产生的时间序列数据。我们假设，基于无人机（UAV）的纵向多光谱测量能够表征田块尺度的环境异质性，并改善育种试验中的遗传评估。在2022—2023年纽约州和新墨西哥州的8个紫花苜蓿试验中，于整个生长季收集了多光谱影像。采用单性状和多性状模型评估了植被指数的遗传力及其与生物量产量的关联。纵向测量数据采用随机回归（RR）建模，以估计小区特异性永久环境效应（PEE），并在两阶段框架中将其纳入基因组混合模型。利用重复间估计遗传效应的一致性和准确性指标，将模型性能与传统的自回归（AR）模型和基线（无校正）模型进行比较。植被指数表现出中到高的遗传力，其中GNDVI始终优于NDVI和NDRE。单性状和多性状分析均揭示了显著的空间变异，尤其在新墨西哥州的试验中更为明显。RR衍生的PEE捕捉到了田块尺度的空间格局，相对于基线模型和AR模型改善了遗传信号的恢复，在强空间异质性条件下增益最大，而在变异有限时性能相当。RR框架提高了遗传效应估计在独立田间子集间的可重复性，同时未损害与全试验评估的一致性。这些结果表明，通过随机回归整合UAV数据可增强遗传信号恢复和选择准确性，在精准农业和生物传感器驱动的作物管理方面具有更广泛的潜力。","bioRxiv (Cold Spring Harbor Laboratory)","2026-10-20T00:00:00Z",79,{"impact":17,"substance":304,"depth":17,"authority":191,"freshness":45,"relevant":21,"comment":508},"该研究利用无人机多光谱表型与随机回归模型校正田间空间异质性，显著提升苜蓿育种遗传评估精度，方法新颖、数据覆盖两州八组试验，对智慧育种有实质参考价值。",[510],{"name":504,"url":501},[197,512,513,49,311],"无人机","育种",{"code":313,"slug":314,"name":315,"path":316,"parent_code":317},[516],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":518},{"candidates":519,"agricultural_evidence":46},[520,524,528,534],{"code":313,"score":334,"supported":46,"title":521,"abstract":522,"tags":523},[336],[338,336],[311],{"code":32,"score":191,"supported":46,"title":525,"abstract":526,"tags":527},[126],[126],[49],{"code":263,"score":229,"supported":46,"title":529,"abstract":530,"tags":533},[],[531,532],"精准农业","precision agriculture",[197],{"code":321,"score":535,"supported":46,"title":536,"abstract":537,"tags":538},4,[],[338],[311],[540,541],"无人机 多光谱 苜蓿 育种","随机回归 空间异质性 遗传评估","无人机多光谱苜蓿育种-5567","10.64898\u002F2026.10.14.759249",{"doi":543,"openalex_id":545,"authors":546,"venue":504,"cited_by_count":86,"oa_url":501,"card":581,"direction":93,"ingested_from":94},"W7223083966",[547,550,553,556,559,561,563,566,569,572,575,578],{"name":548,"orcid":549},"Dinesh Ghimire","https:\u002F\u002Forcid.org\u002F0009-0008-2281-1396",{"name":551,"orcid":552},"Ranjita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-6116-7611",{"name":554,"orcid":555},"Karl H. Kunze","https:\u002F\u002Forcid.org\u002F0000-0003-4548-1808",{"name":557,"orcid":558},"Felipe Sabadin","https:\u002F\u002Forcid.org\u002F0000-0003-2937-1465",{"name":560,"orcid":9},"Julie Hansen",{"name":562,"orcid":9},"Christopher Pierce",{"name":564,"orcid":565},"Liam Wickes-Do","https:\u002F\u002Forcid.org\u002F0009-0003-2927-4590",{"name":567,"orcid":568},"Nicholas Santantonio","https:\u002F\u002Forcid.org\u002F0000-0002-4351-4023",{"name":570,"orcid":571},"Ian M. Ray","https:\u002F\u002Forcid.org\u002F0000-0003-2056-536X",{"name":573,"orcid":574},"Michael A. Gore","https:\u002F\u002Forcid.org\u002F0000-0001-6896-8024",{"name":576,"orcid":577},"Virginia Mae Moore","https:\u002F\u002Forcid.org\u002F0000-0001-7888-3366",{"name":579,"orcid":580},"Kelly R. Robbins","https:\u002F\u002Forcid.org\u002F0000-0001-9522-9585",{"tldr":582,"method":583,"finding":584,"direction":93,"opportunity":585},"用无人机多光谱时序表型和随机回归模型校正田间空间异质性，提升苜蓿育种遗传评估精度。","八项苜蓿试验的无人机多光谱植被指数，随机回归估计小区永久环境效应并嵌入基因组混合","随机回归提取的环境效应能捕捉田块尺度空间格局，在强异质性下显著提升遗传信号恢复与选择准确性。","可将无人机时序表型与随机回归框架推广到其他作物育种试验，并探索多源遥感与基因组选择的联合建模。","2026-10-22T11:20:32.655394Z"]