[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2316":3,"related-2316":97},{"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":62,"slug":65,"view_count":66,"doi":67,"paper":68,"created_at":96},2316,"Vision Transformers Enable Advanced Plant Phenotyping in Controlled Environments","https:\u002F\u002Fdoi.org\u002F10.64898\u002F2026.09.04.748299","Reliable plant segmentation in high-throughput phenotyping must transfer across species and imaging conditions without repeated model tuning or extensive reannotation. We compare three segmentation strategies using images from Oak Ridge National Laboratory's Advanced Plant Phenotyping Laboratory: (i) fixed color-based thresholding, (ii) supervised U-Nets trained from scratch, and (iii) pretrained vision transformers fine-tuned for binary segmentation. Models were evaluated on a held-out test set and a generalization set that comprised unseen species. On the held-out test set, thresholding, the best U-Net, and the best vision transformer achieved mean Dice scores of 58.3, 96.6, and 97.3, respectively. On the generalization set, the corresponding Dice scores were 56.5, 86.2, and 95.7. Thresholding remained effective on some datasets but failed when plant appearance changed. Supervised U-Net training resolved within-distribution errors but failed to generalize to novel species and backgrounds. Pretrained vision transformers consistently produced high-accuracy segmentations across the evaluated species, views, soil backgrounds, and tray types. These results benchmark the practical progression from fixed rules to task-specific supervision and pretrained visual representations for controlled-environment plant phenotyping.","在高通量表型分析中，可靠的植物分割必须能够在不同物种和成像条件之间迁移，而无需重复调整模型或进行大量重新标注。我们利用橡树岭国家实验室先进植物表型实验室的图像，比较了三种分割策略：（i）基于颜色的固定阈值法，（ii）从零开始训练的有监督U-Net，以及（iii）针对二值分割进行微调的预训练视觉Transformer。模型在留出测试集和包含未见物种的泛化集上进行了评估。在留出测试集上，阈值法、最佳U-Net和最佳视觉Transformer的平均Dice分数分别为58.3、96.6和97.3。在泛化集上，相应的Dice分数分别为56.5、86.2和95.7。阈值法在某些数据集上仍然有效，但当植物外观发生变化时则失效。有监督U-Net训练解决了分布内误差，但未能泛化到新物种和背景。预训练视觉Transformer在所评估的物种、视角、土壤背景和托盘类型上始终产生高精度分割。这些结果基准了受控环境植物表型分析中从固定规则到任务特定监督再到预训练视觉表示的实际进展。",null,"bioRxiv (Cold Spring Harbor Laboratory)","2026-09-10T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,8,1,"预训练视觉Transformer在跨物种植物分割上显著优于U-Net与阈值法，为受控环境高通量表型提供可复用基准，方法新颖、数据扎实，值得入选。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","高通量表型","植物表型","图像分割",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,57],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":50},6,true,[],[49,33],"土壤",[],{"code":52,"score":53,"supported":14,"title":54,"abstract":55,"tags":56},"I2",2,[],[],[27,30],{"code":58,"score":21,"supported":14,"title":59,"abstract":60,"tags":61},"I1",[],[],[26],[63,64],"农业人工智能 高通量表型 图像分割 智慧农业","农业人工智能 高通量表型","农业人工智能高通量表型图像分割智慧农业-2316",4,"10.64898\u002F2026.09.04.748299",{"doi":67,"openalex_id":69,"authors":70,"venue":10,"cited_by_count":88,"oa_url":9,"card":89,"direction":93,"ingested_from":95},"W7212309344",[71,73,76,79,82,85],{"name":72,"orcid":9},"Janou Milligan",{"name":74,"orcid":75},"Anand Seethepalli","https:\u002F\u002Forcid.org\u002F0000-0003-0937-9128",{"name":77,"orcid":78},"Aristeidis Tsaris","https:\u002F\u002Forcid.org\u002F0000-0002-7734-3349",{"name":80,"orcid":81},"Xiao Wang","https:\u002F\u002Forcid.org\u002F0000-0001-6545-1943",{"name":83,"orcid":84},"Larry M. York","https:\u002F\u002Forcid.org\u002F0000-0002-1995-9479",{"name":86,"orcid":87},"John Lagergren","https:\u002F\u002Forcid.org\u002F0000-0002-8092-7433",0,{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"比较阈值法、U-Net和预训练ViT在植物分割中的跨物种泛化能力。","使用ORNL表型实验室图像，对比阈值法、U-Net和微调ViT的分割性能。","预训练ViT在未见物种上Dice达95.7，显著优于U-Net的86.2和阈值法的56.5。","农业遥感与作物表型","可探索预训练ViT在田间复杂场景的跨物种泛化，并降低微调数据需求。","openalex","2026-09-13T23:30:21.474320Z",{"total":45,"page":21,"page_size":45,"items":98},[99,167,249,377,435,493],{"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":113,"tags":115,"primary_topic":119,"secondary_topics":120,"classification":126,"search_phrases":146,"slug":149,"view_count":88,"doi":150,"paper":151,"created_at":166},5583,"Active-Labeling Evaluation for Computer Agriculture Nematode-Cyst Detection Under Sparse Soil-Sample Ground Truth: An Artificial Intelligence Annotation-Efficiency Bound","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544145","Active learning promises to reduce the annotation burden of training nematode-cyst detection models for precision agriculture, but the size of that reduction is not known when ground truth comes from sparse soil samples rather than exhaustive visual inspection. the present analysis derives an annotation-efficiency bound for pool-based active learning under sparse soil-sample ground truth. The detection task is formalised as a two-stage process in which a classifier selects candidate cysts from soil imagery and a laboratory assay confirms a small labelled subset. Under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64, the bound is evaluated across a design grid of 8, 16, 24 and 32 clusters and effect sizes of 3, 5 and 8 percentage points, with 2000 replications per cell. The annotation-efficiency gain is bounded by the ratio of label noise to cluster-level variance, and that 52 clusters are required to detect a 5-percentage-point improvement at 80% power. The bound provides a design rule for allocating annotation effort between active selection and confirmatory soil sampling.","主动学习有望减轻精准农业中训练线虫囊检测模型所需的标注负担，但当真实标签来自稀疏土壤样本而非详尽的视觉检查时，这种负担减轻的幅度尚不清楚。本分析推导了在稀疏土壤样本真实标签下基于池的主动学习的标注效率界限。检测任务被形式化为两阶段过程：分类器从土壤图像中筛选候选线虫囊，实验室检测确认一个小的已标注子集。在设定的方差结构下——组间标准差为6.0、残差标准差为4.5、组内相关系数为0.64——该界限在8、16、24和32个簇的设计网格以及3、5和8个百分点的效应量下进行评估，每个单元格重复2000次。标注效率增益受标签噪声与簇级方差之比的约束，并且在80%检验功效下检测5个百分点的改进需要52个簇。该界限为在主动选择与验证性土壤采样之间分配标注工作量提供了设计规则。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",64,{"impact":20,"substance":109,"depth":110,"authority":111,"freshness":13,"relevant":21,"comment":112},20,17,9,"该研究给出稀疏土壤样本下主动学习标注效率的理论边界与聚类设计规则，方法新颖、结论具体，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[114],{"name":105,"url":102},[26,27,116,117,118],"主动学习","线虫检测","土壤采样",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[121],{"code":52,"slug":122,"name":123,"path":124,"parent_code":125},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":127},{"candidates":128,"agricultural_evidence":46},[129,134,139],{"code":32,"score":130,"supported":46,"title":131,"abstract":132,"tags":133},14,[33],[49,33],[],{"code":52,"score":111,"supported":46,"title":135,"abstract":137,"tags":138},[136],"artificial intelligence",[],[27],{"code":58,"score":140,"supported":46,"title":141,"abstract":142,"tags":145},7,[],[143,144],"精准农业","precision agriculture",[26],[147,148],"线虫孢囊 检测 主动学习","稀疏土壤样本 标注效率","线虫孢囊检测主动学习-5583","10.5281\u002Fzenodo.23544145",{"doi":150,"openalex_id":152,"authors":153,"venue":105,"cited_by_count":88,"oa_url":102,"card":160,"direction":164,"ingested_from":95},"W7223082195",[154,156,158],{"name":155,"orcid":9},"Amine Ben Salah",{"name":157,"orcid":9},"Ines Trabelsi",{"name":159,"orcid":9},"Mohamed Aziz Khelifi",{"tldr":161,"method":162,"finding":163,"direction":164,"opportunity":165},"推导稀疏土壤样本真值下主动学习标注效率的理论上界。","基于方差结构与设计网格的统计模拟，2000次重复。","标注效率增益受标签噪声与簇间方差之比限制，检测5个百分点提升需52簇。","农业人工智能与决策模型","可将该上界推广到其他稀疏真值农业检测任务，并探索主动选择与确认采样的最优分配策略。","2026-10-22T11:20:58.901747Z",{"id":168,"title":169,"url":170,"summary":171,"summary_zh":172,"content":9,"source_name":173,"source_url":170,"published_at":174,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":175,"score_detail":176,"sources":180,"tags":182,"primary_topic":186,"secondary_topics":191,"classification":194,"search_phrases":223,"slug":226,"view_count":88,"doi":227,"paper":228,"created_at":248},5006,"Mapping Soil Salinity in Arid Agricultural Regions Using Multi-Source Remote Sensing and a Long-Range Dependency-Enhanced Deep Learning Framework","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101966","Soil salinity estimation across arid agricultural regions is challenging due to pronounced environmental heterogeneity and complex dependencies among multi-source variables. This study proposes a Long-Range Dependency-Enhanced Network (LRD-Net) for regional-scale soil salinity estimation in multiple agricultural oasis regions using multi-source remote sensing and environmental covariates. A dataset comprising 180 field samples from three agricultural oasis regions in Xinjiang, China, was constructed with 25 covariates derived from spectral, topographic, climatic, and soil property data. LRD-Net consists of three stages: a multi-scale feature fusion backbone based on depthwise separable convolutions and cross-scale interaction; a Long-Range Dependency Enhancement Module (LDEM) with an asymmetric dual-branch architecture that captures forward and reverse-aware dependencies through reverse alignment and cross-directional fusion; and multi-scale attention refinement with a regression head for continuous salinity estimation. Under the region-stratified random-split evaluation, LRD-Net achieved an R2 of 0.905, an RMSE of 1.207 g·kg−1, and an MAE of 0.868 g·kg−1. Performance decreased under more spatially independent validation, with an R2 of 0.874 under spatial cross-validation and LORO test R2 values ranging from 0.739 to 0.801, indicating that prediction across spatially and geographically independent samples is more challenging than region-stratified random-split evaluation. The proposed framework effectively integrates multi-source information and captures complementary long-range dependencies, providing a potential approach for regional-scale soil salinity estimation within the studied arid agricultural oasis environments and supporting soil salinization monitoring and sustainable land management in the study region.","干旱农业区域土壤盐分估算面临显著的环境异质性和多源变量间复杂依赖关系的挑战。本研究提出了一种长程依赖增强网络（LRD-Net），利用多源遥感与环境协变量对多个农业绿洲区域进行区域尺度土壤盐分估算。研究构建了包含中国新疆三个农业绿洲区域180个野外样本的数据集，并衍生出25个协变量，涵盖光谱、地形、气候和土壤属性数据。LRD-Net由三个阶段组成：基于深度可分离卷积和跨尺度交互的多尺度特征融合骨干网络；具有非对称双分支架构的长程依赖增强模块（LDEM），通过反向对齐和跨方向融合捕获前向与反向感知依赖关系；以及多尺度注意力细化与回归头，用于连续盐分估算。在区域分层随机划分评估下，LRD-Net取得了R²为0.905、RMSE为1.207 g·kg⁻¹、MAE为0.868 g·kg⁻¹的性能。在空间独立性更强的验证下性能有所下降，空间交叉验证R²为0.874，留一区域测试R²范围为0.739至0.801，表明跨空间和地理独立样本的预测比区域分层随机划分评估更具挑战性。所提出的框架有效整合了多源信息并捕获了互补的长程依赖关系，为研究区干旱农业绿洲环境下的区域尺度土壤盐分估算提供了潜在方法，并支持研究区土壤盐渍化监测与可持续土地管理。","Land","2026-10-14T00:00:00Z",72,{"impact":177,"substance":178,"depth":110,"authority":19,"freshness":111,"relevant":21,"comment":179},12,21,"方法新颖、数据扎实的遥感土壤盐渍化估算研究，对干旱区盐碱地监测与治理有参考价值，但属细分领域学术进展，公共影响有限。",[181],{"name":173,"url":170},[26,27,183,184,185],"遥感","土壤盐渍化","新疆农业",{"code":187,"slug":188,"name":189,"path":190,"parent_code":125},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[192,193],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":52,"slug":122,"name":123,"path":124,"parent_code":125},{"status":39,"locked":14,"version":40,"method":41,"evidence":195},{"candidates":196,"agricultural_evidence":46},[197,203,207,212,219],{"code":187,"score":198,"supported":46,"title":199,"abstract":201,"tags":202},15,[200],"remote sensing",[183,200],[183],{"code":32,"score":130,"supported":46,"title":204,"abstract":205,"tags":206},[33],[49,33],[],{"code":52,"score":111,"supported":46,"title":208,"abstract":210,"tags":211},[209],"deep learning",[],[27],{"code":213,"score":214,"supported":46,"title":215,"abstract":216,"tags":218},"H7",3,[],[217],"干旱",[],{"code":58,"score":21,"supported":14,"title":220,"abstract":221,"tags":222},[],[],[26],[224,225],"新疆 农业绿洲 土壤盐渍化 遥感","LRD-Net 土壤盐分 估算","新疆农业绿洲土壤盐渍化遥感-5006","10.3390\u002Fland15101966",{"doi":227,"openalex_id":229,"authors":230,"venue":173,"cited_by_count":88,"oa_url":170,"card":243,"direction":93,"ingested_from":95},"W7221816183",[231,233,236,238,240],{"name":232,"orcid":9},"Mingjie Qian",{"name":234,"orcid":235},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":237,"orcid":9},"Peng Wang",{"name":239,"orcid":9},"Hangyuan Liu",{"name":241,"orcid":242},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":244,"method":245,"finding":246,"direction":93,"opportunity":247},"提出LRD-Net深度学习框架，融合多源遥感与环境协变量，估算新疆干旱绿洲区土壤盐分。","构建180个样点、25个协变量的数据集，设计含长程依赖增强模块的多尺度网络。","区域分层随机划分下R²达0.905，但空间独立验证R²降至0.739-0.874，跨区域预测更难。","可探索跨区域迁移学习与不确定性量化，提升空间独立场景下盐分制图泛化能力。","2026-10-15T23:30:29.667342Z",{"id":250,"title":251,"url":252,"summary":253,"summary_zh":254,"content":9,"source_name":255,"source_url":252,"published_at":256,"category":12,"cover_url":9,"hotness":13,"is_selected":46,"score":257,"score_detail":258,"sources":260,"tags":262,"primary_topic":266,"secondary_topics":267,"classification":268,"search_phrases":342,"slug":345,"view_count":88,"doi":346,"paper":347,"created_at":376},4832,"Applications and Future Prospects of Plant Multi-Omics Technologies in Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fijms27209034","Against the backdrop of global population growth, intensifying climate change, and limited arable land, ensuring food security and promoting sustainable agriculture have become critical issues. The limitations of conventional agricultural systems are becoming increasingly apparent, creating an urgent need for innovation driven by science and technology. In this context, plant multi-omics technologies serve as the foundational data and technical cornerstone of systems biology and precision agriculture. Integrating multi-level data from genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics offers substantial advantages for systematically understanding and improving plants. Unlike previous reviews that mainly catalog omics technologies or focus on individual crops, this review adopts a unified discovery-to-translation framework and critically evaluates the strength of evidence across applications. This review focuses on cutting-edge applications of multi-omics in three key areas: improving the traits of cereal crops (rice, wheat and maize), remediating soil health, and innovating products such as biofertilizers and biostimulants for agriculture. We systematically explore the roles of multi-omics in accelerating breeding processes, deciphering complex agronomic traits, restoring soil ecosystems, and constructing intelligent decision-support systems. The review also examines current challenges, including the complexity of data integration and the difficulties of predicting genotype-by-environment interactions. Furthermore, it outlines future directions involving deeper convergence with artificial intelligence, high-throughput phenomics, and digital twins. In summary, plant multi-omics technology is a vital tool for unravelling the mysteries of plant life and is also a key enabler of intelligent, precise and sustainable agricultural transformation.","在全球人口增长、气候变化加剧和耕地有限的背景下，保障粮食安全和促进可持续农业已成为关键议题。传统农业系统的局限性日益显现，迫切需要由科学和技术驱动的创新。在此背景下，植物多组学技术作为系统生物学和精准农业的基础数据与技术基石，整合基因组学、转录组学、蛋白质组学、代谢组学、表观基因组学和微生物组学等多层次数据，为系统理解和改良植物提供了显著优势。与以往主要罗列组学技术或聚焦单一作物的综述不同，本综述采用统一的“从发现到转化”框架，并批判性评估各应用中的证据强度。本综述聚焦多组学在三个关键领域的前沿应用：改良谷类作物（水稻、小麦和玉米）的性状、修复土壤健康，以及创新生物肥料和生物刺激素等农业产品。我们系统探讨了多组学在加速育种进程、解析复杂农艺性状、恢复土壤生态系统和构建智能决策支持系统中的作用。本综述还审视了当前挑战，包括数据整合的复杂性和预测基因型与环境互作的困难。此外，文章展望了未来方向，涉及与人工智能、高通量表型组学和数字孪生的更深层次融合。总之，植物多组学技术是揭示植物生命奥秘的重要工具，也是智能、精准和可持续农业转型的关键使能技术。","International Journal of Molecular Sciences","2026-10-12T00:00:00Z",83,{"impact":18,"substance":178,"depth":17,"authority":130,"freshness":20,"relevant":21,"comment":259},"核心期刊综述，系统梳理植物多组学在精准农业中的应用与AI、数字孪生等未来方向，专业深度与信源权威度俱佳，值得进入每日精选。",[261],{"name":255,"url":252},[26,27,263,143,264,265],"种业振兴","土壤健康","多组学",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":269},{"candidates":270,"agricultural_evidence":46},[271,277,281,288,295,302,309,315,322,326,332,337],{"code":58,"score":272,"supported":46,"title":273,"abstract":274,"tags":276},19,[144],[143,275,144],"数字孪生",[26,143],{"code":32,"score":140,"supported":46,"title":278,"abstract":279,"tags":280},[],[49,33],[264],{"code":282,"score":45,"supported":46,"title":283,"abstract":284,"tags":287},"A1",[],[285,286],"水稻","rice",[],{"code":289,"score":45,"supported":46,"title":290,"abstract":291,"tags":294},"A2",[],[292,293],"小麦","wheat",[],{"code":296,"score":45,"supported":46,"title":297,"abstract":298,"tags":301},"A3",[],[299,300],"玉米","maize",[],{"code":303,"score":45,"supported":46,"title":304,"abstract":305,"tags":308},"A8",[],[306,307],"粮食安全","food security",[],{"code":213,"score":45,"supported":46,"title":310,"abstract":311,"tags":314},[],[312,313],"气候变化","climate change",[],{"code":316,"score":45,"supported":46,"title":317,"abstract":318,"tags":321},"H8",[],[319,320],"可持续农业","sustainable agriculture",[],{"code":52,"score":66,"supported":46,"title":323,"abstract":324,"tags":325},[],[136],[27],{"code":327,"score":214,"supported":46,"title":328,"abstract":329,"tags":331},"F3",[],[330],"肥料",[],{"code":333,"score":21,"supported":14,"title":334,"abstract":335,"tags":336},"F1",[],[],[263],{"code":338,"score":21,"supported":14,"title":339,"abstract":340,"tags":341},"F2",[],[],[265],[343,344],"植物多组学 精准农业","多组学 智能育种","植物多组学精准农业-4832","10.3390\u002Fijms27209034",{"doi":346,"openalex_id":348,"authors":349,"venue":255,"cited_by_count":88,"oa_url":252,"card":370,"direction":375,"ingested_from":95},"W7221567543",[350,353,355,358,360,362,365,368],{"name":351,"orcid":352},"Tao Tong","https:\u002F\u002Forcid.org\u002F0000-0002-9003-9667",{"name":354,"orcid":9},"Wang Qingxia",{"name":356,"orcid":357},"Fanrong Zeng","https:\u002F\u002Forcid.org\u002F0000-0001-7742-5195",{"name":359,"orcid":9},"Shuzhen Ye",{"name":361,"orcid":9},"Zhijuan Ji",{"name":363,"orcid":364},"Zhong‐Hua Chen","https:\u002F\u002Forcid.org\u002F0000-0002-7531-320X",{"name":366,"orcid":367},"Jiaoyu Wang","https:\u002F\u002Forcid.org\u002F0000-0002-0249-808X",{"name":369,"orcid":9},"Younan Ouyang",{"tldr":371,"method":372,"finding":373,"direction":164,"opportunity":374},"综述植物多组学在精准农业中的应用，涵盖作物改良、土壤修复和生物产品创新。","整合基因组、转录组、蛋白组、代谢组等多组学数据，采用发现到转化框架。","多组学可加速育种、解析复杂农艺性状、修复土壤生态并构建智能决策系统。","多组学与AI、高通量表型、数字孪生融合，解决基因型-环境互作预测难题。","数字乡村与农业信息化","2026-10-13T23:30:29.504594Z",{"id":378,"title":379,"url":380,"summary":381,"summary_zh":382,"content":9,"source_name":105,"source_url":380,"published_at":383,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":384,"score_detail":385,"sources":388,"tags":390,"primary_topic":394,"secondary_topics":395,"classification":397,"search_phrases":416,"slug":419,"view_count":88,"doi":420,"paper":421,"created_at":434},4498,"SMART CROP SELECTION MODEL USING SOIL PH VALUE, TEMPERATURE, HUMIDITY, AND RAINFALL PREDICTION","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23211381","Crop suitability with soil and climatic conditions plays a vital role in increasing productivity of agriculture. This study proposes a smart crop selection model which recommends the suitable crop based on the machine learning techniques with the input parameters of soil pH, soil temperature, soil humidity and rainfall. The data set consisted of 1000 observations and 8 different crop classes and missing values were handled, crop labels were encoded and numerical features were selected. Five-fold cross validation was used to compare the performance of the three classifiers: Random Forest, XGBoost and Support Vector Machine (SVM). Finally, a Gradient-Boosted classification model was trained and evaluated with test-set accuracy, classification metrics, Multiclass ROC analysis and feature importance. The results of the application were 93.60% of the final test accuracy and the model comparison was used to compare the three classification methods. The contribution of four environmental parameters to the crop classification was analyzed by feature-importance analysis. To facilitate practical accessibility, the entire process was carried out using an interactive dashboard developed with Streamlit, allowing for the upload of datasets, evaluation of models, selection of parameters and the generation of predictions in real time for the crops of interest. The developed system is an example of how machine learning can be integrated with an interactive decision support interface to develop a data-driven, crop selection system, relying on simple soil and weather parameters.","作物适宜性与土壤及气候条件对提高农业生产率至关重要。本研究提出了一种智能作物选择模型，该模型基于机器学习技术，以土壤pH值、土壤温度、土壤湿度和降雨量为输入参数，推荐适宜的作物。数据集包含1000个观测值和8个不同作物类别，对缺失值进行了处理，对作物标签进行了编码，并筛选了数值特征。采用五折交叉验证比较了三种分类器——随机森林（Random Forest）、XGBoost和支持向量机（Support Vector Machine, SVM）的性能。最终，训练了一个梯度提升分类模型，并通过测试集准确率、分类指标、多类ROC分析和特征重要性进行了评估。应用结果为最终测试准确率93.60%，并通过模型比较对三种分类方法进行了对比。通过特征重要性分析，探讨了四个环境参数对作物分类的贡献。为便于实际应用，整个流程通过使用Streamlit开发的交互式仪表板实现，支持数据集上传、模型评估、参数选择以及针对感兴趣作物的实时预测生成。所开发的系统展示了如何将机器学习与交互式决策支持界面相结合，构建一个数据驱动的作物选择系统，且仅依赖于简单的土壤和气象参数。","2026-10-07T00:00:00Z",69,{"impact":177,"substance":109,"depth":386,"authority":19,"freshness":20,"relevant":21,"comment":387},16,"基于土壤pH、温湿度与降雨的机器学习选种模型，含93.6%测试精度与Streamlit交互看板，方法完整但属常规应用型研究，产业影响有限。",[389],{"name":105,"url":380},[26,27,391,392,393],"决策支持系统","土壤墒情","作物选择",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[396],{"code":52,"slug":122,"name":123,"path":124,"parent_code":125},{"status":39,"locked":14,"version":40,"method":41,"evidence":398},{"candidates":399,"agricultural_evidence":46},[400,404,412],{"code":32,"score":198,"supported":46,"title":401,"abstract":402,"tags":403},[33],[49,33],[392],{"code":52,"score":405,"supported":46,"title":406,"abstract":407,"tags":411},11,[],[408,409,410],"机器学习","machine learning","decision support",[27,391],{"code":58,"score":21,"supported":14,"title":413,"abstract":414,"tags":415},[],[],[26],[417,418],"土壤pH 降雨量 作物选择模型","机器学习 作物推荐 智慧农业","土壤pH降雨量作物选择模型-4498","10.5281\u002Fzenodo.23211381",{"doi":420,"openalex_id":422,"authors":423,"venue":105,"cited_by_count":88,"oa_url":380,"card":428,"direction":433,"ingested_from":95},"W7220991946",[424,426],{"name":425,"orcid":9},"C. SAI KUMAR",{"name":427,"orcid":9},"Prof. G SREENIVASULU",{"tldr":429,"method":430,"finding":431,"direction":164,"opportunity":432},"基于土壤pH、温湿度和降雨，用机器学习构建智能选种模型并开发交互式仪表板。","1000条8类作物数据，五折交叉验证比较RF、XGBoost、SVM，最终用梯度","梯度提升模型测试准确率达93.60%，特征重要性揭示四参数对选种的贡献。","可引入时序气象与多源土壤数据，提升模型泛化性并适配不同区域作物推荐。","智慧农业 \u002F 农业物联网","2026-10-09T23:30:12.285129Z",{"id":436,"title":437,"url":438,"summary":439,"summary_zh":440,"content":9,"source_name":105,"source_url":438,"published_at":441,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":442,"score_detail":443,"sources":445,"tags":447,"primary_topic":450,"secondary_topics":451,"classification":453,"search_phrases":473,"slug":476,"view_count":214,"doi":477,"paper":478,"created_at":492},4374,"AI-Driven Crop Recommendation and Growth Prediction System Using Soil Fertility and Climate Intelligence","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23179023","Agricultural decisions are increasingly affected by changing climate conditions, soil fertility, and the availability of timely technical guidance. This mini project presents an AI-Driven Agricultural Intelligence System that combines crop recommendation, soil fertility assessment, weather-risk prediction, yield estimation, fertilizer recommendation, and multilingual conversational support in one web platform. The system accepts soil and climate parameters such as nitrogen, phosphorus, potassium, pH, temperature, humidity, rainfall, location, crop, and season. The supplied implementation uses a Random Forest model for crop recommendation, XGBoost for weather-risk prediction, a Random Forest regressor for yield estimation, a Random Forest classifier for fertilizer recommendation, and a deterministic rule-based soilfertility module with model-independent fallback logic. A FastAPI backend performs validation, model loading, chained inference, weather integration, authentication, and response formatting, while a React-based Progressive Web Application provides the user interface. OpenWeatherMap supplies current weather context and Google Gemini AI supports multilingual agricultural explanations with offline FAQ and rulebased fallbacks. Reported module performance is approximately 88-95%, and representative API, fallback, authentication, translation, rate-limiting, and chatbot tests pass in the supplied project material. The project demonstrates how machine learning, web development, external APIs, and multilingual AI can be combined to deliver practical, accessible, and data-driven agricultural decision support. Keywords: Machine Learning, Agriculture, Crop Recommendation, Soil Fertility, Weather Risk, Yield Prediction, Fertilizer Recommendation, FastAPI, React, XGBoost, Google Gemini AI, Precision Agriculture.","农业决策日益受到气候条件变化、土壤肥力以及及时技术指导可得性的影响。本小型项目提出了一套人工智能驱动的农业智能系统，将作物推荐、土壤肥力评估、天气风险预测、产量估算、肥料推荐和多语言对话支持集成于一个网络平台。该系统接受土壤和气候参数，如氮、磷、钾、pH值、温度、湿度、降雨量、地点、作物和季节。所提供的实现采用随机森林（Random Forest）模型进行作物推荐，采用XGBoost进行天气风险预测，采用随机森林回归器进行产量估算，采用随机森林分类器进行肥料推荐，并采用基于确定性规则的土壤肥力模块，具备与模型无关的回退逻辑。FastAPI后端执行验证、模型加载、链式推理、天气集成、身份认证和响应格式化，而基于React的渐进式网络应用提供用户界面。OpenWeatherMap提供当前天气上下文，Google Gemini AI支持多语言农业解释，并具备离线FAQ和基于规则的回退机制。所报告的模块性能约为88-95%，且在所提供的项目材料中，具有代表性的API、回退、身份认证、翻译、速率限制和聊天机器人测试均通过。该项目展示了如何将机器学习、网络开发、外部API和多语言AI相结合，以提供实用、可及且数据驱动的农业决策支持。关键词：机器学习、农业、作物推荐、土壤肥力、天气风险、产量预测、肥料推荐、FastAPI、React、XGBoost、Google Gemini AI、精准农业。","2026-10-06T00:00:00Z",50,{"impact":20,"substance":130,"depth":19,"authority":45,"freshness":111,"relevant":21,"comment":444},"学生级迷你项目论文，方法组合常规、性能数据自报，工程整合有一定参考价值但学术与产业增量有限，可作智慧农业应用案例收录。",[446],{"name":105,"url":438},[26,27,143,448,449],"作物推荐","土壤肥力",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[452],{"code":52,"slug":122,"name":123,"path":124,"parent_code":125},{"status":39,"locked":14,"version":40,"method":41,"evidence":454},{"candidates":455,"agricultural_evidence":46},[456,460,464,468],{"code":32,"score":198,"supported":46,"title":457,"abstract":458,"tags":459},[33],[49,33],[449],{"code":52,"score":13,"supported":46,"title":461,"abstract":462,"tags":463},[],[408,409,410],[27],{"code":58,"score":20,"supported":46,"title":465,"abstract":466,"tags":467},[],[143,144],[26,143],{"code":327,"score":45,"supported":46,"title":469,"abstract":470,"tags":472},[],[330,471],"fertilizer",[],[474,475],"AI 作物推荐 土壤肥力","XGBoost 天气风险 产量预测","AI作物推荐土壤肥力-4374","10.5281\u002Fzenodo.23179023",{"doi":477,"openalex_id":479,"authors":480,"venue":105,"cited_by_count":88,"oa_url":438,"card":487,"direction":164,"ingested_from":95},"W7220664944",[481,483,485],{"name":482,"orcid":9},"Gunashekar B.",{"name":484,"orcid":9},"Saiteja G.",{"name":486,"orcid":9},"Maniteja K.",{"tldr":488,"method":489,"finding":490,"direction":164,"opportunity":491},"构建集成作物推荐、产量预测、肥料建议与多语言问答的AI农业决策Web平台。","随机森林、XGBoost、FastAPI、React PWA、OpenWeath","各模块准确率约88-95%，API、认证、翻译与聊天机器人测试均通过。","可引入真实田间时序数据与可解释性，验证模型跨区域跨季节泛化能力。","2026-10-07T23:30:47.248380Z",{"id":494,"title":495,"url":496,"summary":497,"summary_zh":498,"content":9,"source_name":499,"source_url":496,"published_at":500,"category":12,"cover_url":9,"hotness":501,"is_selected":14,"score":502,"score_detail":503,"sources":505,"tags":509,"primary_topic":513,"secondary_topics":514,"classification":516,"search_phrases":549,"slug":552,"view_count":66,"doi":9,"paper":553,"created_at":563},4351,"Rhizosphere Microbiome Engineering for Climate-Smart Agriculture: From Synthetic Consortia to Precision Decision Support","https:\u002F\u002Fmel.cgiar.org\u002Freporting\u002Fdownloadmelspace\u002Fhash\u002F095138d10184dc7b696f79e27e716f84","Rhizosphere microbiome engineering is a promising approach that can enhance crop resilience and input use efficiency by redirecting plant–microbe–soil interactions toward predictable functions. Here, we review the mechanistic bases underlying rhizosphere assembly and stability, including root exudate-mediated selection, priority effects, keystone taxa, and metabolite-driven signaling, and connect these principles to proposed design rules for microbial inoculants. We present a generalizable Design–Build–Test–Learn (DBTL) framework for engineering synthetic microbial consortia, covering trait-to-module mapping (nutrient acquisition, phytohormone modulation, ACC deaminase activity, stress-protective metabolites, and biocontrol), compatibility screening, minimal yet robust community architectures, and iterative optimization driven by multi-omics and high-throughput phenotyping. Translation to field settings is framed as an engineering challenge defined by formulation and administration limitations, including carrier type, seed coating and encapsulation methods, shelf life, strain invasiveness, and permanence of colonization amid environmental diversity. We also summarize how integrative measurement pipelines (amplicon and shotgun sequencing, transcriptomics, metabolomics, and network or causal analyses) can advance microbiome studies from correlation to actionability. We describe how precision agriculture (sensors, remote sensing, and variable-rate inputs) and AI\u002FML (split-sample comparisons, transfer learning, and active learning) approaches can accelerate strain discovery, mixture optimization, and adaptive experimentation, driven by the need for stringent controls, metadata-rich reporting, and cross-site comparability. Use cases focus on stress conditions (drought, salinity, thermal extremes, and biotic stress) to demonstrate how microbial functions translate to agronomic outcomes and to highlight critical bottlenecks for reproducible, scalable microbiome products.","根际微生物组工程是一种有前景的方法，通过将植物–微生物–土壤相互作用导向可预测的功能，能够增强作物抗逆性和投入品利用效率。本文综述了根际组装与稳定性背后的机制基础，包括根系分泌物介导的选择、优先效应、关键类群和代谢物驱动的信号传导，并将这些原理与微生物接种剂拟议的设计规则相联系。我们提出了一个可推广的设计–构建–测试–学习（DBTL）框架，用于工程化合成微生物群落，涵盖性状到模块的映射（养分获取、植物激素调节、ACC脱氨酶活性、胁迫保护性代谢物和生物防治）、兼容性筛选、最小但稳健的群落架构，以及由多组学和高通量表型驱动的迭代优化。向田间环境的转化被界定为一项工程挑战，其受限于制剂和施用方面的制约，包括载体类型、种子包衣和包埋方法、货架期、菌株入侵性以及在环境多样性中定殖的持久性。我们还总结了整合性测量流程（扩增子和鸟枪法测序、转录组学、代谢组学以及网络或因果分析）如何推动微生物组研究从相关性走向可操作性。我们描述了精准农业（传感器、遥感和变量投入）以及人工智能\u002F机器学习（分样本比较、迁移学习和主动学习）方法如何加速菌株发现、混合优化和自适应实验，其驱动力在于对严格对照、富含元数据的报告和跨站点可比性的需求。应用案例聚焦于胁迫条件（干旱、盐渍、极端温度和生物胁迫），以展示微生物功能如何转化为农艺结果，并突出可重复、可规模化微生物组产品的关键瓶颈。","MELSpace (ICARDA (The International Center for Agricultural Research in Dry Areas))","2026-10-05T00:00:00Z",25,80,{"impact":17,"substance":18,"depth":17,"authority":130,"freshness":20,"relevant":21,"comment":504},"ICARDA 综述提出根际微生物组工程的 DBTL 框架并衔接精准农业与 AI，方法体系完整、结论可靠，对气候智慧型农业有参考价值，但属综述类论文、非全国性政策或重大突破。",[506,507],{"name":499,"url":496},{"name":499,"url":508},"https:\u002F\u002Fhdl.handle.net\u002F20.500.11766\u002F70900",[26,27,510,511,512],"根际微生物","气候智慧型农业","合成菌群",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[515],{"code":52,"slug":122,"name":123,"path":124,"parent_code":125},{"status":39,"locked":14,"version":40,"method":41,"evidence":517},{"candidates":518,"agricultural_evidence":46},[519,525,530,534,539,543],{"code":32,"score":17,"supported":46,"title":520,"abstract":522,"tags":524},[521],"rhizosphere",[49,523,33,521],"根际",[510],{"code":58,"score":198,"supported":46,"title":526,"abstract":528,"tags":529},[527],"smart agriculture",[143,144],[26],{"code":52,"score":177,"supported":46,"title":531,"abstract":532,"tags":533},[410],[408],[27],{"code":213,"score":140,"supported":46,"title":535,"abstract":536,"tags":538},[],[217,537],"drought",[511],{"code":187,"score":45,"supported":46,"title":540,"abstract":541,"tags":542},[],[183,200],[],{"code":544,"score":214,"supported":46,"title":545,"abstract":546,"tags":548},"F4",[],[547],"生物防治",[],[550,551],"ICARDA 根际微生物 合成菌群","根际微生物 精准农业 决策支持","ICARDA根际微生物合成菌群-4351",{"doi":9,"openalex_id":554,"authors":555,"venue":499,"cited_by_count":88,"oa_url":496,"card":558,"direction":433,"ingested_from":95},"W7220812607",[556],{"name":557,"orcid":9},"Nourhan Fouad Mahmoud",{"tldr":559,"method":560,"finding":561,"direction":164,"opportunity":562},"综述根际微生物组工程，提出DBTL框架设计合成菌群，并融合精准农业与AI实现气候智慧型农业。","综述根际组装机制，提出DBTL框架，整合多组学、高通量表型、传感器与AI\u002FML方","微生物组工程需从相关走向可操作，精准农业与AI可加速菌株发现和菌群优化，但田间可重复性仍是瓶颈。","可研究AI驱动的根际微生物组设计-田间验证闭环，重点解决菌群定殖持久性与跨站点可迁移性。","2026-10-07T23:30:12.909703Z"]