[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4351":3,"related-4351":119},{"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":27,"primary_topic":33,"secondary_topics":39,"classification":46,"search_phrases":99,"slug":102,"view_count":103,"doi":9,"paper":104,"created_at":118},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。",null,"MELSpace (ICARDA (The International Center for Agricultural Research in Dry Areas))","2026-10-05T00:00:00Z","论文",25,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,8,1,"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。",[24,25],{"name":10,"url":6},{"name":10,"url":26},"https:\u002F\u002Fhdl.handle.net\u002F20.500.11766\u002F70900",[28,29,30,31,32],"智慧农业","农业人工智能","根际微生物","气候智慧型农业","合成菌群",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[40],{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":47,"locked":14,"version":48,"method":49,"evidence":50},"classified","2026-10-22.2","rules",{"candidates":51,"agricultural_evidence":53},[52,60,69,76,84,92],{"code":34,"score":17,"supported":53,"title":54,"abstract":56,"tags":59},true,[55],"rhizosphere",[57,58,35,55],"土壤","根际",[30],{"code":61,"score":62,"supported":53,"title":63,"abstract":65,"tags":68},"I1",15,[64],"smart agriculture",[66,67],"精准农业","precision agriculture",[28],{"code":41,"score":70,"supported":53,"title":71,"abstract":73,"tags":75},12,[72],"decision support",[74],"机器学习",[29],{"code":77,"score":78,"supported":53,"title":79,"abstract":80,"tags":83},"H7",7,[],[81,82],"干旱","drought",[31],{"code":85,"score":86,"supported":53,"title":87,"abstract":88,"tags":91},"I3",6,[],[89,90],"遥感","remote sensing",[],{"code":93,"score":94,"supported":53,"title":95,"abstract":96,"tags":98},"F4",3,[],[97],"生物防治",[],[100,101],"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",4,{"doi":9,"openalex_id":105,"authors":106,"venue":10,"cited_by_count":109,"oa_url":6,"card":110,"direction":116,"ingested_from":117},"W7220812607",[107],{"name":108,"orcid":9},"Nourhan Fouad Mahmoud",0,{"tldr":111,"method":112,"finding":113,"direction":114,"opportunity":115},"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。","农业人工智能与决策模型","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。","智慧农业 \u002F 农业物联网","openalex","2026-10-07T23:30:12.909703Z",{"total":86,"page":21,"page_size":86,"items":120},[121,181,257,312,372,422],{"id":122,"title":123,"url":124,"summary":125,"summary_zh":126,"content":9,"source_name":127,"source_url":124,"published_at":128,"category":12,"cover_url":9,"hotness":129,"is_selected":14,"score":130,"score_detail":131,"sources":136,"tags":138,"primary_topic":142,"secondary_topics":143,"classification":145,"search_phrases":161,"slug":164,"view_count":109,"doi":165,"paper":166,"created_at":180},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",10,64,{"impact":20,"substance":132,"depth":133,"authority":134,"freshness":129,"relevant":21,"comment":135},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。",[137],{"name":127,"url":124},[28,29,139,140,141],"主动学习","线虫检测","土壤采样",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[144],{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},{"status":47,"locked":14,"version":48,"method":49,"evidence":146},{"candidates":147,"agricultural_evidence":53},[148,152,157],{"code":34,"score":19,"supported":53,"title":149,"abstract":150,"tags":151},[35],[57,35],[],{"code":41,"score":134,"supported":53,"title":153,"abstract":155,"tags":156},[154],"artificial intelligence",[],[29],{"code":61,"score":78,"supported":53,"title":158,"abstract":159,"tags":160},[],[66,67],[28],[162,163],"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":165,"openalex_id":167,"authors":168,"venue":127,"cited_by_count":109,"oa_url":124,"card":175,"direction":114,"ingested_from":117},"W7223082195",[169,171,173],{"name":170,"orcid":9},"Amine Ben Salah",{"name":172,"orcid":9},"Ines Trabelsi",{"name":174,"orcid":9},"Mohamed Aziz Khelifi",{"tldr":176,"method":177,"finding":178,"direction":114,"opportunity":179},"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。","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":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":129,"is_selected":14,"score":189,"score_detail":190,"sources":194,"tags":196,"primary_topic":199,"secondary_topics":203,"classification":206,"search_phrases":230,"slug":233,"view_count":109,"doi":234,"paper":235,"created_at":256},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":70,"substance":191,"depth":133,"authority":192,"freshness":134,"relevant":21,"comment":193},21,13,"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。",[195],{"name":187,"url":184},[28,29,89,197,198],"土壤盐渍化","新疆农业",{"code":85,"slug":200,"name":201,"path":202,"parent_code":45},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[204,205],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},{"status":47,"locked":14,"version":48,"method":49,"evidence":207},{"candidates":208,"agricultural_evidence":53},[209,213,217,222,226],{"code":85,"score":62,"supported":53,"title":210,"abstract":211,"tags":212},[90],[89,90],[89],{"code":34,"score":19,"supported":53,"title":214,"abstract":215,"tags":216},[35],[57,35],[],{"code":41,"score":134,"supported":53,"title":218,"abstract":220,"tags":221},[219],"deep learning",[],[29],{"code":77,"score":94,"supported":53,"title":223,"abstract":224,"tags":225},[],[81],[],{"code":61,"score":21,"supported":14,"title":227,"abstract":228,"tags":229},[],[],[28],[231,232],"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":234,"openalex_id":236,"authors":237,"venue":187,"cited_by_count":109,"oa_url":184,"card":250,"direction":254,"ingested_from":117},"W7221816183",[238,240,243,245,247],{"name":239,"orcid":9},"Mingjie Qian",{"name":241,"orcid":242},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":244,"orcid":9},"Peng Wang",{"name":246,"orcid":9},"Hangyuan Liu",{"name":248,"orcid":249},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":251,"method":252,"finding":253,"direction":254,"opportunity":255},"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。","农业遥感与作物表型","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":258,"title":259,"url":260,"summary":261,"summary_zh":262,"content":9,"source_name":127,"source_url":260,"published_at":263,"category":12,"cover_url":9,"hotness":129,"is_selected":14,"score":264,"score_detail":265,"sources":268,"tags":270,"primary_topic":274,"secondary_topics":275,"classification":277,"search_phrases":294,"slug":297,"view_count":109,"doi":298,"paper":299,"created_at":311},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":70,"substance":132,"depth":266,"authority":192,"freshness":20,"relevant":21,"comment":267},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。",[269],{"name":127,"url":260},[28,29,271,272,273],"决策支持系统","土壤墒情","作物选择",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[276],{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},{"status":47,"locked":14,"version":48,"method":49,"evidence":278},{"candidates":279,"agricultural_evidence":53},[280,284,290],{"code":34,"score":62,"supported":53,"title":281,"abstract":282,"tags":283},[35],[57,35],[272],{"code":41,"score":285,"supported":53,"title":286,"abstract":287,"tags":289},11,[],[74,288,72],"machine learning",[29,271],{"code":61,"score":21,"supported":14,"title":291,"abstract":292,"tags":293},[],[],[28],[295,296],"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":298,"openalex_id":300,"authors":301,"venue":127,"cited_by_count":109,"oa_url":260,"card":306,"direction":116,"ingested_from":117},"W7220991946",[302,304],{"name":303,"orcid":9},"C. SAI KUMAR",{"name":305,"orcid":9},"Prof. G SREENIVASULU",{"tldr":307,"method":308,"finding":309,"direction":114,"opportunity":310},"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。","2026-10-09T23:30:12.285129Z",{"id":313,"title":314,"url":315,"summary":316,"summary_zh":317,"content":9,"source_name":127,"source_url":315,"published_at":318,"category":12,"cover_url":9,"hotness":129,"is_selected":14,"score":319,"score_detail":320,"sources":322,"tags":324,"primary_topic":327,"secondary_topics":328,"classification":330,"search_phrases":352,"slug":355,"view_count":94,"doi":356,"paper":357,"created_at":371},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":19,"depth":192,"authority":86,"freshness":134,"relevant":21,"comment":321},"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。",[323],{"name":127,"url":315},[28,29,66,325,326],"作物推荐","土壤肥力",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[329],{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},{"status":47,"locked":14,"version":48,"method":49,"evidence":331},{"candidates":332,"agricultural_evidence":53},[333,337,341,345],{"code":34,"score":62,"supported":53,"title":334,"abstract":335,"tags":336},[35],[57,35],[326],{"code":41,"score":129,"supported":53,"title":338,"abstract":339,"tags":340},[],[74,288,72],[29],{"code":61,"score":20,"supported":53,"title":342,"abstract":343,"tags":344},[],[66,67],[28,66],{"code":346,"score":86,"supported":53,"title":347,"abstract":348,"tags":351},"F3",[],[349,350],"肥料","fertilizer",[],[353,354],"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":356,"openalex_id":358,"authors":359,"venue":127,"cited_by_count":109,"oa_url":315,"card":366,"direction":114,"ingested_from":117},"W7220664944",[360,362,364],{"name":361,"orcid":9},"Gunashekar B.",{"name":363,"orcid":9},"Saiteja G.",{"name":365,"orcid":9},"Maniteja K.",{"tldr":367,"method":368,"finding":369,"direction":114,"opportunity":370},"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":373,"title":374,"url":375,"summary":376,"summary_zh":9,"content":9,"source_name":377,"source_url":9,"published_at":378,"category":12,"cover_url":9,"hotness":129,"is_selected":14,"score":379,"score_detail":380,"sources":382,"tags":384,"primary_topic":386,"secondary_topics":387,"classification":389,"search_phrases":409,"slug":412,"view_count":103,"doi":9,"paper":413,"created_at":421},4286,"Applications of Artificial Intelligence in Soil Characterization and Agriculture: A Systematic Review","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37386408","Navarro Rubio等系统综述2018-2026年196项AI土壤特性与农业应用研究，遵循PRISMA 2020框架。综述发现研究明显向数据驱动方法转变，机器学习和深度学习模型主导近期研究；随机森林、支持向量机、梯度提升方法、人工神经网络、卷积神经网络、长短期记忆架构最常被报告；主要数据源包括原位传感、实验室测量、遥感影像和环境协变量，常通过多源数据融合框架集成；树集成模型在不同土壤特性上提供稳健性能。","Agronomy 2026 16(13):1241 DOI:10.3390\u002Fagronomy16131241","2026-10-02T00:00:00Z",78,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":86,"relevant":21,"comment":381},"PRISMA框架下196项研究的系统综述，方法学扎实、结论可靠，对农业AI土壤应用具较高参考价值。",[383],{"name":377,"url":375},[28,29,74,89,385],"土壤监测",{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},[388],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":47,"locked":14,"version":48,"method":49,"evidence":390},{"candidates":391,"agricultural_evidence":53},[392,397,401,405],{"code":41,"score":266,"supported":53,"title":393,"abstract":394,"tags":396},[154],[395,74],"深度学习",[29,74],{"code":34,"score":285,"supported":53,"title":398,"abstract":399,"tags":400},[35],[57],[],{"code":85,"score":103,"supported":53,"title":402,"abstract":403,"tags":404},[],[89],[89],{"code":61,"score":21,"supported":14,"title":406,"abstract":407,"tags":408},[],[],[28],[410,411],"AI 土壤特性 系统综述","机器学习 土壤 遥感","AI土壤特性系统综述-4286",{"doi":9,"openalex_id":9,"authors":414,"venue":9,"cited_by_count":109,"oa_url":9,"card":415,"direction":114,"ingested_from":420},[],{"tldr":416,"method":417,"finding":418,"direction":114,"opportunity":419},"系统综述2018-2026年196项AI用于土壤特性与农业的研究。","PRISMA 2020框架，分析机器学习与深度学习模型及多源数据融合。","研究转向数据驱动，树集成模型在不同土壤特性上表现稳健。","可探索可解释AI与多源数据融合在土壤特性预测中的泛化能力。","agent","2026-10-06T00:05:04.976398Z",{"id":423,"title":424,"url":425,"summary":426,"summary_zh":427,"content":9,"source_name":428,"source_url":425,"published_at":429,"category":12,"cover_url":9,"hotness":129,"is_selected":14,"score":430,"score_detail":431,"sources":433,"tags":435,"primary_topic":439,"secondary_topics":440,"classification":442,"search_phrases":469,"slug":472,"view_count":473,"doi":474,"paper":475,"created_at":499},3805,"Machine Learning Prediction of Soil Nutrients and pH in Tibetan Alpine Croplands: A Comparison of Nine Algorithms","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu18199875","Soils underpin terrestrial ecosystem functioning and exert strong control over farmland productivity, nutrient turnover, environmental quality, and agricultural sustainability. Reliable prediction of soil nutrients and pH is therefore important for precision fertilization, soil-quality assessment, and the sustainability of farmland management, particularly in fragile alpine agroecosystems. However, the comparative performance of candidate algorithms for estimating soil properties in alpine croplands of the Qinghai-Tibetan Plateau remains poorly characterized. Here, we assessed nine approaches, namely random forest (RF), generalized boosting regression (GBR), multiple linear regression (MLR), artificial neural network (ANN), generalized linear regression (GLR), conditional inference tree (CIT), extreme gradient boosting (eXGB), support vector machine (SVM), and recursive partitioning regression tree (RRT), for predicting soil carbon, nitrogen, phosphorus, potassium, and pH from annual mean temperature, annual precipitation, annual solar radiation, and maximum normalized difference vegetation index (NDVImax). Field observations were obtained from 103 sampling plots, with four individual soil samples collected per plot and depth, resulting in 412 observations for each depth layer. Clear contrasts in model performance emerged under alpine agricultural conditions. Across all soil variables and depths, eXGB and RF showed the best overall performance, with the lowest mean relative bias values of 1.35% and 1.55%, respectively, and the lowest mean RMSE values of 0.43 and 0.47. These results indicate that nonlinear ensemble algorithms better captured the heterogeneous relationships between environmental covariates and soil properties than linear models or single-tree approaches. Overall, RF and eXGB provide a practical methodological basis for precision fertilization, soil-quality assessment, and long-term sustainability in fragile alpine agroecosystems.","土壤是陆地生态系统功能的基础，并对农田生产力、养分周转、环境质量和农业可持续性具有强烈控制作用。因此，可靠预测土壤养分和pH对于精准施肥、土壤质量评估以及农田管理的可持续性十分重要，尤其是在脆弱的高寒农业生态系统中。然而，在青藏高原高寒农田中，用于估算土壤属性的候选算法的比较性能仍缺乏系统表征。本研究评估了九种方法，即随机森林（RF）、广义提升回归（GBR）、多元线性回归（MLR）、人工神经网络（ANN）、广义线性回归（GLR）、条件推断树（CIT）、极端梯度提升（eXGB）、支持向量机（SVM）和递归分割回归树（RRT），利用年平均气温、年降水量、年太阳辐射和最大归一化差异植被指数（NDVImax）预测土壤碳、氮、磷、钾和pH。野外观测来自103个采样样地，每个样地和每个深度采集4个独立土壤样品，每个深度层共获得412个观测值。在高寒农业条件下，模型性能表现出明显差异。在所有土壤变量和深度上，eXGB和RF总体表现最佳，平均相对偏差值最低，分别为1.35%和1.55%，平均RMSE值最低，分别为0.43和0.47。这些结果表明，与线性模型或单树方法相比，非线性集成算法更好地捕捉了环境协变量与土壤属性之间的异质性关系。总体而言，RF和eXGB为脆弱高寒农业生态系统中的精准施肥、土壤质量评估和长期可持续性提供了实用的方法学基础。","Sustainability","2026-09-27T00:00:00Z",73,{"impact":70,"substance":18,"depth":17,"authority":192,"freshness":20,"relevant":21,"comment":432},"基于103个样点412份观测对比九种算法预测高原农田土壤养分与pH，方法扎实、结论明确，对精准施肥有直接参考价值，但属区域性研究，产业影响有限。",[434],{"name":428,"url":425},[28,29,436,437,438],"精准施肥","土壤养分","青藏高原",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[441],{"code":41,"slug":42,"name":43,"path":44,"parent_code":45},{"status":47,"locked":14,"version":48,"method":49,"evidence":443},{"candidates":444,"agricultural_evidence":53},[445,449,453,459,465],{"code":34,"score":62,"supported":53,"title":446,"abstract":447,"tags":448},[35],[57,35],[437],{"code":41,"score":134,"supported":53,"title":450,"abstract":451,"tags":452},[288],[],[29],{"code":346,"score":78,"supported":53,"title":454,"abstract":455,"tags":458},[],[456,457],"施肥","fertilization",[436],{"code":460,"score":94,"supported":53,"title":461,"abstract":462,"tags":464},"H6",[],[463],"农业生态",[],{"code":61,"score":21,"supported":14,"title":466,"abstract":467,"tags":468},[],[],[28],[470,471],"青藏高原 农田 土壤养分 机器学习","土壤pH 预测 随机森林 XGBoost","青藏高原农田土壤养分机器学习-3805",2,"10.3390\u002Fsu18199875",{"doi":474,"openalex_id":476,"authors":477,"venue":428,"cited_by_count":109,"oa_url":425,"card":494,"direction":114,"ingested_from":117},"W7214680092",[478,480,482,485,487,490,492],{"name":479,"orcid":9},"Chenjun Zhao",{"name":481,"orcid":9},"Shaowei Li",{"name":483,"orcid":484},"Yuan Tian","https:\u002F\u002Forcid.org\u002F0000-0002-8758-1826",{"name":486,"orcid":9},"ChengQun Yu",{"name":488,"orcid":489},"Gang Fu","https:\u002F\u002Forcid.org\u002F0000-0002-6055-8730",{"name":491,"orcid":9},"Zhiming Zhong",{"name":493,"orcid":9},"Wei Sun",{"tldr":495,"method":496,"finding":497,"direction":114,"opportunity":498},"比较九种机器学习算法预测青藏高原高寒农田土壤养分与pH，发现集成算法最优。","基于103个样点412个观测，用气候与NDVI变量比较RF、eXGB等九种算法。","eXGB和RF表现最佳，平均相对偏差最低（1.35%和1.55%），优于线性与单树模型。","可引入多时相遥感与深度学习方法，提升高寒区土壤属性制图精度与泛化能力。","2026-09-29T23:30:57.794777Z"]