[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4797":3,"related-4797":120},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":45,"search_phrases":82,"slug":85,"view_count":86,"doi":87,"paper":88,"created_at":119},4797,"Beyond single-element toxicity: chromium-salinity interactions, food-chain transfer and mitigation in sugarcane agroecosystems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1942892","Chromium contamination in sugarcane soils is often combined with co-metals and salinity caused by tannery effluent, electroplating effluent, paper mill wastewater, vinasse fertigation and filter cake. Hence, single element toxicity assessment does not provide realistic assessment of its exposure. The present review summarizes research on chromium behaviour along the soil, root, stalk and cane juice in sugarcane. Cr mobility depends on soil pH and redox potential (Eh), while salinity adds an additional control through ionic strength and chloride complexation. Sequential extraction and speciation analysis prove that bioavailable fraction is more important for plant toxicity than total Cr concentration. Cr(VI) enters roots via sulphate and phosphate transporters via mimicking oxyanions and this mechanism differs from that of other metal ions that use ion channels, a route that salt-induced PHT1 expression could plausibly modulate, although this has not been tested in sugarcane. In proxy species, combined Cr and salinity exposure raises reactive oxygen species production and membrane damage beyond the level induced by either stress alone, whereas in sugarcane the downregulation of PEP carboxylase, pyruvate orthophosphate dikinase and photosystem proteins has so far been documented only under salinity applied by itself. Whether the two stresses interact on C4 carbon assimilation in cane remains untested. Root-to-stalk transfer of Cr is low at most sites examined, but not at all of them, and measurable Cr reaches cane juice at concentrations that have driven hazard quotients above unity in two of the four field studies assessed. Bagasse biochar, biochar with nZVI, halotolerant Cr-reducing microorganisms and water management based on redox reactions all reduce Cr bioavailability, but field experiments in cane are lacking. Factorial experiments investigating Cr behaviour depending on salinity gradient, juice level speciation and field trials in ratoons are required.","甘蔗土壤中的铬污染通常与制革废水、电镀废水、造纸厂废水、酒糟灌溉和滤泥带入的伴生金属及盐分复合存在。因此，单一元素毒性评估无法对其暴露状况作出符合实际的评价。本文综述了铬在甘蔗土壤、根系、茎秆及蔗汁中行为的研究进展。铬的迁移性取决于土壤pH和氧化还原电位（Eh），而盐分通过离子强度和氯络合作用对其施加额外调控。连续提取和形态分析表明，生物有效态组分对植物毒性的意义大于总铬浓度。Cr(VI)通过模拟含氧阴离子经硫酸盐和磷酸盐转运蛋白进入根系，这一机制不同于其他利用离子通道的金属离子，而盐诱导的PHT1表达可能调控该途径，但尚未在甘蔗中验证。在替代物种中，铬与盐分复合暴露引起的活性氧产生和膜损伤超过单一胁迫诱导的水平，而在甘蔗中，磷酸烯醇式丙酮酸羧化酶、丙酮酸磷酸双激酶和光系统蛋白的下调目前仅在单独盐胁迫下得到证实。两种胁迫是否在甘蔗C4碳同化过程中发生交互作用仍未经检验。在多数已考察的位点，铬从根系向茎秆的转移量较低，但并非所有位点均如此；在所评估的四项田间研究中，有两项显示可检测到的铬到达蔗汁，其浓度已使危害商数超过1。蔗渣生物炭、负载纳米零价铁（nZVI）的生物炭、耐盐铬还原微生物以及基于氧化还原反应的水分管理均可降低铬的生物有效性，但甘蔗田间试验尚属缺乏。有必要开展考察铬行为随盐度梯度变化的析因实验、蔗汁水平上的形态分析以及宿根蔗田间试验。",null,"Frontiers in Sustainable Food Systems","2026-10-12T00:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,13,9,1,"综述系统梳理铬-盐交互、食物链迁移与阻控手段，指出甘蔗田间试验空白，对污染蔗区安全利用有参考价值，但属机理综述、非产业级突破。",[25],{"name":10,"url":6},[27,28,29,30,31],"甘蔗","食品安全","盐渍化","土壤重金属","生物炭修复",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":53},[51,58,64,71,77],{"code":33,"score":52,"supported":53,"title":54,"abstract":56,"tags":57},15,true,[55],"sugarcane",[27,55],[27],{"code":40,"score":59,"supported":53,"title":60,"abstract":61,"tags":63},8,[],[62,41],"土壤",[30,29],{"code":65,"score":66,"supported":53,"title":67,"abstract":68,"tags":70},"F3",3,[],[69],"fertigation",[],{"code":72,"score":66,"supported":53,"title":73,"abstract":74,"tags":76},"H3",[],[75],"灌溉",[],{"code":78,"score":22,"supported":14,"title":79,"abstract":80,"tags":81},"E4",[],[],[28],[83,84],"甘蔗 铬污染 盐渍化","甘蔗汁 风险","甘蔗铬污染盐渍化-4797",0,"10.3389\u002Ffsufs.2026.1942892",{"doi":87,"openalex_id":89,"authors":90,"venue":10,"cited_by_count":86,"oa_url":6,"card":112,"direction":116,"ingested_from":118},"W7221577366",[91,94,96,98,100,102,104,106,108,110],{"name":92,"orcid":93},"Gowtham Sornam","https:\u002F\u002Forcid.org\u002F0009-0009-1150-1568",{"name":95,"orcid":9},"Sugumaran Melkumaramangalam Palani",{"name":97,"orcid":9},"Parameswari Ettiyagounder",{"name":99,"orcid":9},"Kalaiselvi Thangavel",{"name":101,"orcid":9},"Saravanan Naaganoor Ananthan",{"name":103,"orcid":9},"Elayarajan Maharajan",{"name":105,"orcid":9},"Selva Kumar Manikkam",{"name":107,"orcid":9},"Sassikumar Datchinamoorthy",{"name":109,"orcid":9},"Sinduja Murugaiyan",{"name":111,"orcid":9},"Priyatharsini Sengottaiyan",{"tldr":113,"method":114,"finding":115,"direction":116,"opportunity":117},"综述甘蔗农业生态系统中铬与盐分交互作用、食物链传递及缓解措施，指出单元素毒性评估不足。","综述土壤-根-茎-汁中铬形态、转运与缓解技术，结合顺序提取与形态分析。","铬与盐分交互对C4碳同化影响未测，蔗汁铬危害商在部分田间研究中超1，缓解技术缺乏田间验证。","农业绿色发展与碳","可开展盐度梯度下铬形态与C4光合交互的因子实验，并验证生物炭等缓解措施的田间效果。","openalex","2026-10-13T23:30:07.200287Z",{"total":121,"page":22,"page_size":121,"items":122},6,[123,264,322,383,429,514],{"id":124,"title":125,"url":126,"summary":127,"summary_zh":128,"content":9,"source_name":129,"source_url":126,"published_at":130,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":131,"score_detail":132,"sources":136,"tags":138,"primary_topic":143,"secondary_topics":144,"classification":152,"search_phrases":215,"slug":218,"view_count":22,"doi":219,"paper":220,"created_at":263},1442,"Morphological, Taxonomic and Soil Suitability Evaluation for Sustainable Crop Production Using GIS and Remote Sensing in Gudvella Block, Balangir District, Odisha State, India","https:\u002F\u002Fdoi.org\u002F10.56557\u002Fjoban\u002F2026\u002Fv18i211054","A comprehensive soil-landscape assessment was undertaken to characterise the morphological, physicochemical and taxonomic properties of soils and to evaluate their suitability for major crops using a GIS-based approach. The study area exhibited considerable physiographic heterogeneity, comprising upper and lower pedi-plains, depressions, hillside slopes, foothill slopes, hillocks and stream-bank positions. Twenty-five representative pedons were examined for morphological and physicochemical characteristics and classified according to Soil Taxonomy. Soil depth varied from 14 to 125 cm, while textures ranged from loamy-skeletal to fine. Soil reaction ranged from strongly acidic to slightly alkaline (pH 4.97-7.85), whereas electrical conductivity (0.01-0.08 dS m-1) and exchangeable sodium percentage (0.56-8.35%) indicated negligible salinity and sodicity constraints. Organic carbon was generally low (0.05-0.50%), while CEC (3.85-37.69 cmol(p⁺) kg-1) and base saturation (21.04-72.90%) varied substantially among pedons. The soils were classified mainly as Entisols, Inceptisols and Alfisols, with Aquic, Lithic, Vertic and related intergrades reflecting contrasting soil-forming environments. Crop suitability assessment revealed marked differences among land-use types. Paddy had 68.3% of the area within the S1-S3 classes, whereas maize, potato and sugarcane each occupied approximately 86% within S2-S3. Cotton, mustard and sunflower were predominantly marginally suitable, with substantial areas classified as N1. Approximately 4% of the area was permanently unsuitable (N2). The results demonstrate that soil suitability is controlled by the interaction of soil properties and landscape processes. The integration of soil survey, taxonomy and GIS-based suitability evaluation provides a robust basis for crop-specific land-use planning, soil and water conservation and sustainable agricultural intensification.","为表征土壤的形态学、物理化学及分类学特性，并采用基于GIS的方法评估其对主要作物的适宜性，开展了一项综合的土壤-景观评估。研究区域表现出显著的地貌异质性，包括上、下麓原、洼地、山坡、山麓坡、丘陵及河岸带。对25个代表性土壤剖面进行了形态学和物理化学特性分析，并根据土壤系统分类法进行分类。土壤深度变化范围为14至125厘米，质地从壤质砾石到细质不等。土壤反应范围从强酸性到微碱性（pH 4.97-7.85），而电导率（0.01-0.08 dS m⁻¹）和交换性钠百分比（0.56-8.35%）表明盐分和钠质限制可忽略不计。有机碳普遍较低（0.05-0.50%），而阳离子交换量（3.85-37.69 cmol(p⁺) kg⁻¹）和盐基饱和度（21.04-72.90%）在各剖面间差异显著。土壤主要归类为新成土、始成土和淋溶土，其中包含潮湿润、石质、变性及相关的过渡类型，反映了不同的成土环境。作物适宜性评估揭示了不同土地利用类型间的显著差异。水稻有68.3%的面积属于S1-S3等级，而玉米、马铃薯和甘蔗在S2-S3等级中各占约86%。棉花、芥菜和向日葵主要为边缘适宜，有相当面积的区域被归类为N1。约4%的区域为永久不适宜（N2）。结果表明，土壤适宜性受土壤性质与景观过程相互作用的控制。土壤调查、分类学与基于GIS的适宜性评价的整合，为特定作物的土地利用规划、水土保持及可持续农业集约化提供了坚实基础。","Journal of biology and nature","2026-09-01T00:00:00Z",52,{"impact":59,"substance":19,"depth":133,"authority":59,"freshness":134,"relevant":22,"comment":135},16,2,"区域土壤适宜性评价研究，数据详实，但影响范围有限，时效性较低。",[137],{"name":129,"url":126},[139,140,141,142],"农业遥感","土壤调查","作物适宜性","GIS",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[145,151],{"code":146,"slug":147,"name":148,"path":149,"parent_code":150},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":153},{"candidates":154,"agricultural_evidence":53},[155,160,164,170,176,183,190,197,203,209],{"code":40,"score":156,"supported":53,"title":157,"abstract":158,"tags":159},14,[41],[62,41],[],{"code":146,"score":17,"supported":53,"title":161,"abstract":162,"tags":163},[142],[142],[142],{"code":33,"score":21,"supported":53,"title":165,"abstract":166,"tags":169},[],[167,27,168,55],"棉花","cotton",[],{"code":171,"score":21,"supported":53,"title":172,"abstract":174,"tags":175},"I3",[173],"remote sensing",[],[139],{"code":177,"score":121,"supported":53,"title":178,"abstract":179,"tags":182},"A1",[],[180,181],"水稻","paddy",[],{"code":184,"score":121,"supported":53,"title":185,"abstract":186,"tags":189},"A3",[],[187,188],"玉米","maize",[],{"code":191,"score":121,"supported":53,"title":192,"abstract":193,"tags":196},"A5",[],[194,195],"马铃薯","potato",[],{"code":198,"score":66,"supported":53,"title":199,"abstract":200,"tags":202},"A4",[],[201],"向日葵",[],{"code":204,"score":66,"supported":53,"title":205,"abstract":206,"tags":208},"H6",[],[207],"水土保持",[],{"code":210,"score":66,"supported":53,"title":211,"abstract":212,"tags":214},"H8",[],[213],"可持续农业",[],[216,217],"作物适宜性 农业遥感 土壤调查 GIS","作物适宜性 农业遥感","作物适宜性农业遥感土壤调查GIS-1442","10.56557\u002Fjoban\u002F2026\u002Fv18i211054",{"doi":219,"openalex_id":221,"authors":222,"venue":129,"cited_by_count":86,"oa_url":126,"card":257,"direction":261,"ingested_from":118},"W7204891281",[223,225,227,230,232,235,237,239,241,243,245,247,249,251,253,255],{"name":224,"orcid":9},"D. N. Roy",{"name":226,"orcid":9},"Shaikh Mohitpasha",{"name":228,"orcid":229},"Saumen Saha","https:\u002F\u002Forcid.org\u002F0000-0003-2768-883X",{"name":231,"orcid":9},"D. C. Sarkar",{"name":233,"orcid":234},"M. K. Singh","https:\u002F\u002Forcid.org\u002F0000-0003-0118-3614",{"name":236,"orcid":9},"Sumit Raizada",{"name":238,"orcid":9},"Ravi Gautam",{"name":240,"orcid":9},"Monika",{"name":242,"orcid":9},"Indra Bahadur",{"name":244,"orcid":9},"R.T. Bankar",{"name":246,"orcid":9},"R. K. Setia",{"name":248,"orcid":9},"R. L. Meena",{"name":250,"orcid":9},"N. S. Gahlod",{"name":252,"orcid":9},"Y. Suresh Kumar",{"name":254,"orcid":9},"Kumar Rajeev",{"name":256,"orcid":9},"S. K. Tripathi",{"tldr":258,"method":259,"finding":260,"direction":261,"opportunity":262},"利用GIS和遥感评估印度某地块土壤特性及作物适宜性，为可持续生产提供规划依据。","土壤调查、形态理化分析、土壤分类及GIS适宜性评价。","水稻适宜性高，棉花等边际适宜，约4%永久不宜，需针对性规划。","农业遥感与作物表型","可结合高分辨率遥感与机器学习，动态监测土壤属性并预测作物适宜性，提升区域精准农业决策。","2026-09-02T23:30:53.627384Z",{"id":265,"title":266,"url":267,"summary":268,"summary_zh":9,"content":269,"source_name":270,"source_url":9,"published_at":271,"category":272,"cover_url":9,"hotness":13,"is_selected":53,"score":273,"score_detail":274,"sources":278,"tags":280,"primary_topic":286,"secondary_topics":287,"classification":295,"search_phrases":317,"slug":320,"view_count":21,"doi":9,"paper":9,"created_at":321},411,"科研新发现:地膜微塑料破坏棉田根际微生态 加剧棉花黄萎病发生——河北省农科院植保所成果发表于Environmental Microbiome","https:\u002F\u002Fwww.farmer.com.cn\u002F2026\u002F08\u002F10\u002Fwap_991025691.html","河北省农林科学院植物保护研究所国家棉花产业技术体系棉花病害防控岗位科研团队揭示地膜微塑料加重棉花黄萎病发生的作用机制,成果发表于Environmental Microbiome。研究发现,地膜微塑料抑制棉花生长,高浓度处理下棉花出苗率和植株生物量均下降;0.5%-2.5%浓度LDPE微塑料使棉花黄萎病病情指数提高54.29%-71.67%,但土壤中黄萎病菌数量并未显著增加,说明病害加重并非病原菌增多,而是微塑料破坏土壤微生态平衡。研究揭示'地膜微塑料—土壤养分紊乱—根际微生物失衡—棉花黄萎病加重'的完整链条,建议在新疆、黄河流域等主产棉区将残膜减量回收、微塑料污染治理和根际微生态修复纳入综合防控体系。","长期以来，地膜覆盖为棉花稳产增产发挥了重要作用，但残膜不断破碎形成的微塑料会不会影响棉花健康，一直缺乏明确答案。近日，河北省农林科学院植物保护研究所国家棉花产业技术体系棉花病害防控岗位科研团队揭示了地膜微塑料加重棉花黄萎病发生的作用机制，为棉田残膜治理、土壤微生态修复和黄萎病综合防控提供了新的科学依据。相关成果发表于《Environmental Microbiome》。\n\n棉花黄萎病是危害棉花生产的重要土传病害。研究团队针对长期地膜覆盖条件下微塑料对棉田生态的影响开展系统研究，通过设置不同浓度低密度聚乙烯（LDPE）微塑料处理，分析棉花生长、土壤养分以及根际微生物变化情况。\n\n研究发现，地膜微塑料会抑制棉花生长，高浓度处理下，棉花出苗率和植株生物量均有所下降。更重要的是，研究首次证实，微塑料虽然没有显著增加土壤中黄萎病菌数量，但0.5%~2.5%浓度LDPE微塑料却会使棉花黄萎病病情指数提高54.29%~71.67%，说明病害加重并非病原菌增多，而是微塑料破坏了土壤微生态平衡。\n\n进一步研究表明，微塑料会改变土壤养分水平，干扰氮、硫等元素循环，并重塑棉花根际微生物群落结构。其中，真菌群落结构与功能紊乱是导致黄萎病加重的重要原因，细菌群落变化则主要与土壤养分失衡相关。研究揭示了“地膜微塑料—土壤养分紊乱—根际微生物失衡—棉花黄萎病加重”的完整链条，也解释了生产上长期存在的“土壤病菌基数不高，但病害逐年加重”现象。\n\n研究团队建议，在新疆、黄河流域等主产棉区，应将残膜减量回收、微塑料污染治理和根际微生态修复纳入棉花病害综合防控体系，通过改善土壤生态环境，从源头降低土传病害发生风险，促进棉花产业绿色高质量发展。","农民日报 2026-08-10","2026-08-10T09:00:00Z","报道",88,{"impact":275,"substance":276,"depth":19,"authority":20,"freshness":59,"relevant":22,"comment":277},25,24,"科研新发现，揭示地膜微塑料加重棉花黄萎病机制，对棉田治理有重要指导意义，信息增量大，权威性高。",[279],{"name":270,"url":267},[281,282,283,284,285],"植保所","河北省农科院","地膜微塑料","棉花黄萎病","土壤微生态",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[288,289],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":290,"slug":291,"name":292,"path":293,"parent_code":294},"F4","crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection","F",{"status":46,"locked":14,"version":47,"method":48,"evidence":296},{"candidates":297,"agricultural_evidence":53},[298,303,307,312],{"code":40,"score":156,"supported":53,"title":299,"abstract":301,"tags":302},[300],"根际",[62,300],[],{"code":33,"score":17,"supported":53,"title":304,"abstract":305,"tags":306},[167],[167],[284],{"code":290,"score":59,"supported":53,"title":308,"abstract":310,"tags":311},[309],"植保",[],[],{"code":198,"score":66,"supported":53,"title":313,"abstract":314,"tags":316},[],[315],"花生",[],[318,319],"河北省农科院 土壤微生态 地膜微塑料 棉花黄萎病","河北省农科院 土壤微生态","河北省农科院土壤微生态地膜微塑料棉花黄萎病-411","2026-08-11T23:57:01.901771Z",{"id":323,"title":324,"url":325,"summary":326,"summary_zh":327,"content":9,"source_name":328,"source_url":325,"published_at":329,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":330,"score_detail":331,"sources":334,"tags":336,"primary_topic":341,"secondary_topics":342,"classification":343,"search_phrases":361,"slug":364,"view_count":22,"doi":365,"paper":366,"created_at":382},5234,"A smart agriculture-oriented cross-attention multiscale CNN for sugarcane disease diagnosis","https:\u002F\u002Fdoi.org\u002F10.1080\u002F23080477.2026.2749739","Sugarcane leaf diseases can adversely affect crop health and yield, making timely disease identification important for smart agriculture. Manual inspection of leaf diseases is time-consuming and requires expert knowledge, particularly when large cultivation areas need to be monitored. To tackle this problem, this study proposes a Cross-Attention Multiscale Parallel Convolutional Neural Network for automatic classification of sugarcane leaf diseases. This framework comprises a Parallel Feature Extraction Stem, cross-attention mechanism, Dense Connection Blocks, and Inception Blocks to learn disease features at various spatial scales. The PFES extracts fine and broad disease patterns using parallel convolutional branches, while the cross-attention mechanism refines the extracted features by emphasizing disease-related information. The DCB supports feature propagation and reuse, and the IB further captures multi-scale feature representations. The proposed model was tested on two benchmark datasets of leaf diseases for sugarcane plants. The proposed framework obtained an accuracy (98.79%), precision (98.31%), recall (97.81%), specificity (98.60%), and F1-Score (98.05%). For assessing the classification accuracy and consistency of the framework, the comparative study, ablation studies, and five-fold cross-validation are carried out. The results show that CA-MPCNN provides accurate sugarcane leaf disease classification and can support automated disease diagnosis in smart agriculture applications.","甘蔗叶片病害会对作物健康与产量造成不利影响，因此及时识别病害对智慧农业具有重要意义。人工检查叶片病害耗时且需要专业知识，尤其是在需要监测大面积种植区域时。为解决该问题，本研究提出一种用于甘蔗叶片病害自动分类的交叉注意力多尺度并行卷积神经网络（Cross-Attention Multiscale Parallel Convolutional Neural Network）。该框架由并行特征提取主干（Parallel Feature Extraction Stem）、交叉注意力机制、密集连接块（Dense Connection Blocks）和Inception块组成，用于学习不同空间尺度上的病害特征。PFES通过并行卷积分支提取细粒度与广域病害模式，而交叉注意力机制通过强调病害相关信息对提取的特征进行细化。DCB支持特征传播与复用，IB进一步捕获多尺度特征表示。所提出的模型在两个甘蔗植株叶片病害基准数据集上进行了测试。该框架取得了准确率（98.79%）、精确率（98.31%）、召回率（97.81%）、特异性（98.60%）和F1分数（98.05%）。为评估该框架的分类准确性和一致性，开展了对比研究、消融研究和五折交叉验证。结果表明，CA-MPCNN能够实现准确的甘蔗叶片病害分类，并可支持智慧农业应用中的自动化病害诊断。","Smart Science","2026-10-17T00:00:00Z",69,{"impact":17,"substance":332,"depth":133,"authority":17,"freshness":21,"relevant":22,"comment":333},20,"该研究提出一种面向甘蔗叶部病害自动分类的交叉注意力多尺度CNN，在两个基准数据集上准确率达98.79%，方法新颖、实验充分，但属于细分作物应用研究，产业影响有限，时效性强，值得进入每日精选。",[335],{"name":328,"url":325},[337,338,27,339,340],"智慧农业","农业人工智能","深度学习","病害诊断",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":344},{"candidates":345,"agricultural_evidence":53},[346,350,356],{"code":33,"score":52,"supported":53,"title":347,"abstract":348,"tags":349},[55],[27,55],[27],{"code":351,"score":52,"supported":53,"title":352,"abstract":354,"tags":355},"I1",[353],"smart agriculture",[337,353],[337],{"code":357,"score":134,"supported":14,"title":358,"abstract":359,"tags":360},"I2",[],[],[338,339],[362,363],"甘蔗 叶部病害 智能诊断","CA-MPCNN 甘蔗 病害","甘蔗叶部病害智能诊断-5234","10.1080\u002F23080477.2026.2749739",{"doi":365,"openalex_id":367,"authors":368,"venue":328,"cited_by_count":86,"oa_url":9,"card":375,"direction":381,"ingested_from":118},"W7222832245",[369,371,373],{"name":370,"orcid":9},"Bala Subramanian Chokkalingam",{"name":372,"orcid":9},"Uvarani Meenrajan",{"name":374,"orcid":9},"Shanmugapriya Sankaran",{"tldr":376,"method":377,"finding":378,"direction":379,"opportunity":380},"提出交叉注意力多尺度并行CNN，实现甘蔗叶病自动分类。","并行卷积分支、交叉注意力、密集连接与Inception块，两个基准数据集验证。","模型准确率达98.79%，消融与五折交叉验证证明其有效稳定。","农业人工智能与决策模型","可探索田间复杂背景与轻量化部署，并扩展到多作物病害实时诊断。","智慧农业 \u002F 农业物联网","2026-10-18T23:30:07.777036Z",{"id":384,"title":385,"url":386,"summary":387,"summary_zh":9,"content":9,"source_name":388,"source_url":9,"published_at":389,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":330,"score_detail":390,"sources":394,"tags":396,"primary_topic":399,"secondary_topics":400,"classification":401,"search_phrases":416,"slug":419,"view_count":22,"doi":9,"paper":420,"created_at":428},4965,"Uncertainty-Aware Semi-Supervised Framework with Supervised Contrastive Learning for Sugarcane Leaf Disease Detection [预印本]","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.17996","Shalini Dangi等提出基于Mean-Teacher半监督学习的不确定性感知病害检测网络U-SSDNet,用于甘蔗叶病检测。论文针对(i)同一病害在不同生育期表现差异大,(ii)病健组织类间相似,(iii)标签噪声等问题,先以离散小波变换增强病斑并降低类内差异,再用KL散度优化伪标签减轻确认偏差,同时引入监督对比学习提升类间区分度。甘蔗叶病数据集上的实验显示,U-SSDNet仅需少量标注即可接近全监督模型精度,显著优于当前主流监督与半监督方法。","arXiv 2610.17996 2026 年 10 月 13 日","2026-10-13T00:00:00Z",{"impact":17,"substance":332,"depth":391,"authority":392,"freshness":21,"relevant":22,"comment":393},17,11,"预印本提出不确定性感知半监督框架，方法新颖且少标注下接近全监督精度，对作物病害智能识别有参考价值，但尚属实验室阶段成果。",[395],{"name":388,"url":386},[337,338,27,397,398],"病害检测","半监督学习",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":402},{"candidates":403,"agricultural_evidence":53},[404,408,412],{"code":33,"score":17,"supported":53,"title":405,"abstract":406,"tags":407},[55],[27],[27],{"code":351,"score":22,"supported":14,"title":409,"abstract":410,"tags":411},[],[],[337],{"code":357,"score":22,"supported":14,"title":413,"abstract":414,"tags":415},[],[],[338],[417,418],"甘蔗叶病 检测 半监督学习","U-SSDNet 甘蔗","甘蔗叶病检测半监督学习-4965",{"doi":9,"openalex_id":9,"authors":421,"venue":9,"cited_by_count":86,"oa_url":9,"card":422,"direction":379,"ingested_from":427},[],{"tldr":423,"method":424,"finding":425,"direction":379,"opportunity":426},"提出半监督不确定性感知网络U-SSDNet，用少量标注实现甘蔗叶病高精度检测。","Mean-Teacher半监督、离散小波变换、KL散度伪标签优化、监督对比学习。","仅需少量标注即接近全监督精度，显著优于主流监督与半监督方法。","可将该不确定性半监督框架迁移至其他作物病害，并探索田间复杂光照与多生育期泛化。","agent","2026-10-15T00:06:08.498104Z",{"id":430,"title":431,"url":432,"summary":433,"summary_zh":434,"content":9,"source_name":435,"source_url":432,"published_at":436,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":437,"score_detail":438,"sources":440,"tags":442,"primary_topic":447,"secondary_topics":448,"classification":453,"search_phrases":474,"slug":477,"view_count":86,"doi":478,"paper":479,"created_at":513},4366,"Article topic: comprehensive assessment of soil and flora contamination by heavy metals in the zone of influence of the Nikolaevskaya enrichment plant","https:\u002F\u002Fdoi.org\u002F10.51886\u002F1999-740x_2026_3_46","Agricultural lands in East Kazakhstan region cover a significant area, with large portions of pasture and cropland situated in close proximity to operating industrial facilities. Despite this, no comprehensive integrated assessment combining remote sensing, ground-based soil monitoring, and flora survey had previously been conducted for the zone of influence of the Nikolaevskaya enrichment plant. This study aimed to fill that gap by a 40-year land cover analysis (1985-2024) using publicly available satellite data processed in Google Earth Engine with soil sampling and vegetation surveys across 17 sites. Soil analyses revealed not only elevated heavy metal concentrations but also a sharp pH decrease, conditions under which heavy metals become significantly more mobile and available for plant uptake. Technosol formation was observed at multiple sampling points, indicating a permanent transformation of the natural soil profile. The flora survey recorded 72 species from 62 genera, with ruderal and synanthropic species domina-ting disturbed habitats and native species persisting only at control points more distant from the facility. Taken together, these findings point to ongoing ecosystem degradation and raise concrete concerns for the regional food chain given the pastureland and cropland adjacent to the plant.","东哈萨克斯坦地区的农业用地面积广阔，其中大片牧场和农田与正在运营的工业设施相距不远。尽管如此，此前尚未针对尼古拉耶夫斯克选矿厂影响区域开展过将遥感、地面土壤监测与植物区系调查相结合的综合评估。本研究旨在填补这一空白，通过为期40年的土地覆盖分析（1985—2024年），利用Google Earth Engine处理的公开卫星数据，并结合17个样点的土壤采样与植被调查。土壤分析不仅揭示了重金属浓度升高，还发现pH值急剧下降，在这种条件下，重金属的迁移性和植物可利用性显著增强。在多个采样点观察到技术土（Technosol）的形成，表明自然土壤剖面发生了永久性改变。植物区系调查共记录到62属72种植物，其中扰动生境以杂草类和伴人植物物种为主，原生物种仅存于距设施较远的对照点。综合来看，这些发现表明生态系统正在持续退化，并且鉴于该厂邻近牧场和农田，对区域食物链构成了切实的担忧。","Pochvovedenie i agrokhimiya","2026-10-05T00:00:00Z",73,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":59,"relevant":22,"comment":439},"结合40年遥感与地面采样的重金属污染综合评估，方法扎实、结论具体，但属区域案例研究，公共影响有限。",[441],{"name":435,"url":432},[443,444,445,446,30],"耕地保护","农业生态","遥感监测","Google Earth Engine",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[449],{"code":171,"slug":450,"name":451,"path":452,"parent_code":150},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":46,"locked":14,"version":47,"method":48,"evidence":454},{"candidates":455,"agricultural_evidence":53},[456,460,465,470],{"code":40,"score":52,"supported":53,"title":457,"abstract":458,"tags":459},[41],[62,41],[30],{"code":171,"score":59,"supported":53,"title":461,"abstract":462,"tags":464},[],[463,173],"遥感",[446,445],{"code":466,"score":22,"supported":14,"title":467,"abstract":468,"tags":469},"H1",[],[],[443],{"code":204,"score":22,"supported":14,"title":471,"abstract":472,"tags":473},[],[],[444],[475,476],"Nikolaevskaya 选矿厂 土壤污染","东哈萨克斯坦 农田 重金属","Nikolaevskaya选矿厂土壤污染-4366","10.51886\u002F1999-740x_2026_3_46",{"doi":478,"openalex_id":480,"authors":481,"venue":435,"cited_by_count":86,"oa_url":507,"card":508,"direction":261,"ingested_from":118},"W7220713350",[482,485,487,490,493,496,499,502,505],{"name":483,"orcid":484},"Yuliya Tugunova","https:\u002F\u002Forcid.org\u002F0009-0006-1037-1480",{"name":486,"orcid":9},"M. Altynbayeva",{"name":488,"orcid":489},"Жанар Абилда","https:\u002F\u002Forcid.org\u002F0000-0001-8505-5243",{"name":491,"orcid":492},"Рахим Канат","https:\u002F\u002Forcid.org\u002F0009-0009-2418-5920",{"name":494,"orcid":495},"Abylay Begaly","https:\u002F\u002Forcid.org\u002F0009-0009-2848-336X",{"name":497,"orcid":498},"D. Kairkhanov","https:\u002F\u002Forcid.org\u002F0009-0006-1017-1980",{"name":500,"orcid":501},"Iskander Isgandarov","https:\u002F\u002Forcid.org\u002F0009-0001-4386-3303",{"name":503,"orcid":504},"ELENA A. ISAKOVA","https:\u002F\u002Forcid.org\u002F0000-0002-9056-6504",{"name":506,"orcid":9},"Kh. Choi","https:\u002F\u002Fjournal.soil.kz\u002Fjour\u002Farticle\u002Fdownload\u002F1135\u002F802",{"tldr":509,"method":510,"finding":511,"direction":116,"opportunity":512},"综合遥感、土壤采样与植被调查，评估哈萨克斯坦一选矿厂影响区40年重金属污染与生态退化。","Google Earth Engine处理1985-2024卫星数据，结合17个","土壤重金属升高且pH骤降，重金属迁移性增强，形成技术土，植被以杂草为主，威胁食物链。","可延伸研究重金属活化与pH耦合机制，并构建污染区农田食物链风险预警模型。","2026-10-07T23:30:29.146414Z",{"id":515,"title":516,"url":517,"summary":518,"summary_zh":519,"content":9,"source_name":520,"source_url":517,"published_at":521,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":522,"score_detail":523,"sources":525,"tags":527,"primary_topic":533,"secondary_topics":534,"classification":535,"search_phrases":573,"slug":576,"view_count":86,"doi":577,"paper":578,"created_at":607},4210,"Nutrient cycling, environmental outcomes and mitigation pathways in sugarcane agroecosystems: A comparative synthesis of Brazil and Australia","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agee.2026.110780","Sugarcane is a major tropical crop with strong potential to support climate-smart agriculture through efficient nutrient cycling, improved nitrogen use efficiency, biofuel production and enhanced soil carbon dynamics. However, environmental outcomes vary substantially across production systems. This review compares two leading high-efficiency sugarcane systems, Brazil and Australia, examining nutrient cycling, greenhouse gas emissions, nutrient losses and soil organic carbon storage. In Brazil, large-scale rainfed operations (commonly exceeding 10,000 ha) promote high organic inputs through residue retention and recycling of vinasse and filter cake, enhancing soil carbon stocks and internal nutrient recycling; however, these practices can elevate nitrogen losses and greenhouse gas emissions when poorly synchronised with crop demand. In Australia, smaller family-owned systems (~95 ha) operate under environmental regulations and the Six Easy Steps framework, where soil-test–based management, subsurface fertiliser placement and enhanced-efficiency fertilisers target nutrient transport to Great Barrier Reef catchments. Fertiliser-N use in Australian sugarcane has progressively declined to among the lowest rates globally for the crop; however, nitrogen use efficiency remains relatively low compared with Brazil, and off-site nutrient losses have not been fully resolved at scale. Soil compaction is a critical constraint in both countries, addressed by complementary strategies: satellite-guided controlled traffic farming in Australia and strategic subsoil tillage with GPS-guided traffic in Brazil. Across both systems, residue retention, legume rotations and reduced tillage improve nutrient cycling and soil carbon accumulation. Integrating Brazil's nutrient-recycling scale with Australia's precision management offers a convergent pathway to enhance nitrogen use efficiency, reduce environmental footprints and improve sugarcane agroecosystem sustainability.","甘蔗是一种重要的热带作物，通过高效的养分循环、提高氮利用效率、生物燃料生产以及增强土壤碳动态，具有支持气候智慧型农业的巨大潜力。然而，不同生产体系的环境效益差异显著。本文综述比较了巴西和澳大利亚两个领先的高效甘蔗生产体系，考察了养分循环、温室气体排放、养分损失和土壤有机碳储量。在巴西，大规模雨养作业（通常超过10,000公顷）通过保留残茬以及回收利用酒糟（vinasse）和滤泥（filter cake）促进高有机投入，提升了土壤碳储量和内部养分循环；然而，当这些做法与作物需求同步不佳时，可能导致氮损失和温室气体排放增加。在澳大利亚，较小的家庭经营体系（约95公顷）在环境法规和“六步简易法”（Six Easy Steps）框架下运行，以土壤测试为基础的管理、肥料深施和增效肥料（enhanced-efficiency fertilisers）针对大堡礁集水区的养分迁移进行调控。澳大利亚甘蔗的肥料氮使用量已逐步下降至全球该作物最低水平之列；然而，与巴西相比，氮利用效率仍然相对较低，且异地养分损失尚未在大规模上得到完全解决。土壤压实是两国共同面临的关键限制因素，通过互补策略加以应对：澳大利亚采用卫星导航控制交通耕作（controlled traffic farming），巴西采用GPS导航交通下的战略性心土耕作（subsoil tillage）。在两个体系中，残茬保留、豆科轮作和减少耕作均改善了养分循环和土壤碳积累。将巴西的养分回收规模与澳大利亚的精准管理相结合，为提升氮利用效率、减少环境足迹和提高甘蔗农业生态系统可持续性提供了一条趋同路径。","Agriculture Ecosystems & Environment","2026-10-04T00:00:00Z",80,{"impact":19,"substance":18,"depth":19,"authority":156,"freshness":59,"relevant":22,"comment":524},"核心期刊发表的巴西与澳大利亚甘蔗系统对比综述，数据与结论扎实，对养分管理与减排路径有参考价值，但属学术综述、非政策或产业事件，时效性一般。",[526],{"name":520,"url":517},[27,528,529,530,531,532],"巴西","土壤固碳","养分循环","氮肥利用率","澳大利亚",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":536},{"candidates":537,"agricultural_evidence":53},[538,542,547,554,560,565,569],{"code":33,"score":52,"supported":53,"title":539,"abstract":540,"tags":541},[55],[27,55],[27],{"code":40,"score":543,"supported":53,"title":544,"abstract":545,"tags":546},7,[],[62,41],[529],{"code":548,"score":121,"supported":53,"title":549,"abstract":550,"tags":553},"B7",[],[551,552],"温室","greenhouse",[],{"code":555,"score":66,"supported":53,"title":556,"abstract":557,"tags":559},"A7",[],[558],"轮作",[],{"code":65,"score":66,"supported":53,"title":561,"abstract":562,"tags":564},[],[563],"肥料",[],{"code":204,"score":66,"supported":53,"title":566,"abstract":567,"tags":568},[],[444],[],{"code":351,"score":66,"supported":53,"title":570,"abstract":571,"tags":572},[],[353],[],[574,575],"巴西 澳大利亚 甘蔗 养分循环","甘蔗 氮肥利用率 土壤固碳","巴西澳大利亚甘蔗养分循环-4210","10.1016\u002Fj.agee.2026.110780",{"doi":577,"openalex_id":579,"authors":580,"venue":520,"cited_by_count":86,"oa_url":517,"card":602,"direction":381,"ingested_from":118},"W7218417513",[581,584,587,590,593,596,599],{"name":582,"orcid":583},"Juan Ricardo Rocha","https:\u002F\u002Forcid.org\u002F0000-0003-0590-655X",{"name":585,"orcid":586},"Nicole Robinson","https:\u002F\u002Forcid.org\u002F0000-0001-6285-6179",{"name":588,"orcid":589},"Dalila Lopes da Silva","https:\u002F\u002Forcid.org\u002F0000-0003-4972-5945",{"name":591,"orcid":592},"Kristopher Woodrow-Smith","https:\u002F\u002Forcid.org\u002F0009-0008-4475-7108",{"name":594,"orcid":595},"Prakash Lakshmanan","https:\u002F\u002Forcid.org\u002F0000-0003-1725-0391",{"name":597,"orcid":598},"Cassio Hamilton Abreu–Junior","https:\u002F\u002Forcid.org\u002F0000-0002-5955-4652",{"name":600,"orcid":601},"Susanne Schmidt","https:\u002F\u002Forcid.org\u002F0000-0001-8369-1238",{"tldr":603,"method":604,"finding":605,"direction":116,"opportunity":606},"综述比较巴西与澳大利亚甘蔗农业生态系统的养分循环、环境效应及减排路径。","文献综述，对比两国甘蔗系统的养分管理、温室气体与土壤碳数据。","巴西靠规模回收、澳大利亚靠精准管理，整合二者可提升氮效率并减排。","可量化整合巴西养分回收与澳大利亚精准管理对氮效率及碳汇的协同效应。","2026-10-05T23:30:08.942071Z"]