[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2042":3,"related-2042":94},{"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":31,"secondary_topics":37,"classification":44,"search_phrases":71,"slug":74,"view_count":75,"doi":76,"paper":77,"created_at":93},2042,"An ensemble deep learning approach for soil pH classification leveraging GLCM-based texture features and neural networks","https:\u002F\u002Fdoi.org\u002F10.69598\u002Fsehs.20.26020002","Soil pH-level classification is a critical challenge in precision agriculture owing to data scarcity, high intra-class similarity, and subtle visual variations between pH levels. This research proposed an ensemble-feature deep learning framework that integrates convolutional neural networks with gray-level co-occurrence matrix (GLCM) texture descriptors to address fundamental limitations in pH-level classification. Through comprehensive experiments, we evaluated diverse architectures (i.e., InceptionV3, Inception ResNetV2, ResNet50, and VGG16) enhanced by GLCM features, demonstrating a substantial increase in classification accuracy, particularly under minimal visual distinctions between pH levels. The optimal Inception ResNetV2 w\u002FGLCM configuration achieved the best overall performance, with an F1-score of 82.10%, an accuracy of 82.22%, and a precision of 82.01%, outperforming the conventional machine learning and deep learning approaches evaluated in this study. The proposed method innovatively provides a synergistic integration of hierarchical deep learning features with statistical texture analysis, achieving robust discrimination of visually similar soil sample images capturing different pH levels. These findings advance automated soil analysis and contribute to precision agriculture by establishing a robust solution for image-based soil pH-level classification.","土壤pH值分级是精准农业中的一个关键挑战，其原因在于数据稀缺、类内相似度高以及不同pH值之间细微的视觉差异。本研究提出了一种集成特征深度学习框架，将卷积神经网络与灰度共生矩阵（GLCM）纹理描述符相结合，以解决pH值分级中的根本性局限。通过全面的实验，我们评估了多种经GLCM特征增强的架构（即InceptionV3、Inception ResNetV2、ResNet50和VGG16），结果表明分类准确率显著提升，尤其是在pH值之间视觉差异极小的情况下。最优的Inception ResNetV2结合GLCM配置取得了最佳整体性能，F1分数为82.10%，准确率为82.22%，精确率为82.01%，优于本研究中评估的传统机器学习和深度学习方法。所提出的方法创新性地将层次化深度学习特征与统计纹理分析协同集成，实现了对捕捉不同pH值的视觉相似土壤样本图像的稳健判别。这些发现推进了自动化土壤分析，并通过建立基于图像的土壤pH值分级的稳健解决方案，为精准农业做出了贡献。",null,"Science, Engineering and Health Studies","2026-09-09T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,20,17,12,8,1,"该研究提出融合GLCM纹理特征与CNN的集成深度学习框架，在土壤pH分级上取得82%以上准确率，方法新颖且对精准农业有实用价值，但属细分技术进展，影响范围有限。",[25],{"name":10,"url":6},[27,28,29,30],"智慧农业","农业人工智能","精准农业","土壤检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,60,65],{"code":32,"score":51,"supported":52,"title":53,"abstract":55,"tags":59},18,true,[54],"deep learning",[56,57,58,54],"深度学习","机器学习","machine learning",[28],{"code":39,"score":17,"supported":52,"title":61,"abstract":62,"tags":64},[40],[63,40],"土壤",[30],{"code":66,"score":21,"supported":52,"title":67,"abstract":68,"tags":70},"I1",[],[29,69],"precision agriculture",[27,29],[72,73],"农业人工智能 土壤检测 智慧农业 精准农业","农业人工智能 土壤检测","农业人工智能土壤检测智慧农业精准农业-2042",0,"10.69598\u002Fsehs.20.26020002",{"doi":76,"openalex_id":78,"authors":79,"venue":10,"cited_by_count":75,"oa_url":84,"card":85,"direction":91,"ingested_from":92},"W7212037232",[80,82],{"name":81,"orcid":9},"Sujitranan Mungklachaiya",{"name":83,"orcid":9},"Anongporn Salaiwarakul","https:\u002F\u002Fli01.tci-thaijo.org\u002Findex.php\u002Fsehs\u002Farticle\u002Fdownload\u002F266062\u002F182682",{"tldr":86,"method":87,"finding":88,"direction":89,"opportunity":90},"提出融合GLCM纹理特征与CNN的集成深度学习框架，实现土壤pH等级图像分类。","用InceptionV3、ResNet50等CNN结合GLCM纹理特征，在土壤图","Inception ResNetV2+GLCM最优，F1达82.10%、准确率82.22%，优于常规","农业遥感与作物表型","可探索多源数据融合与轻量化模型，提升小样本下土壤属性分类泛化能力。","智慧农业 \u002F 农业物联网","openalex","2026-09-10T23:30:09.461905Z",{"total":95,"page":22,"page_size":95,"items":96},6,[97,164,261,328,383,452],{"id":98,"title":99,"url":100,"summary":101,"summary_zh":102,"content":9,"source_name":103,"source_url":100,"published_at":104,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":105,"score_detail":106,"sources":111,"tags":113,"primary_topic":116,"secondary_topics":117,"classification":119,"search_phrases":142,"slug":145,"view_count":146,"doi":147,"paper":148,"created_at":163},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。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-06T00:00:00Z",50,{"impact":21,"substance":107,"depth":108,"authority":95,"freshness":109,"relevant":22,"comment":110},14,13,9,"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。",[112],{"name":103,"url":100},[27,28,29,114,115],"作物推荐","土壤肥力",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[118],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":120},{"candidates":121,"agricultural_evidence":52},[122,126,131,135],{"code":39,"score":17,"supported":52,"title":123,"abstract":124,"tags":125},[40],[63,40],[115],{"code":32,"score":13,"supported":52,"title":127,"abstract":128,"tags":130},[],[57,58,129],"decision support",[28],{"code":66,"score":21,"supported":52,"title":132,"abstract":133,"tags":134},[],[29,69],[27,29],{"code":136,"score":95,"supported":52,"title":137,"abstract":138,"tags":141},"F3",[],[139,140],"肥料","fertilizer",[],[143,144],"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",3,"10.5281\u002Fzenodo.23179023",{"doi":147,"openalex_id":149,"authors":150,"venue":103,"cited_by_count":75,"oa_url":100,"card":157,"direction":161,"ingested_from":92},"W7220664944",[151,153,155],{"name":152,"orcid":9},"Gunashekar B.",{"name":154,"orcid":9},"Saiteja G.",{"name":156,"orcid":9},"Maniteja K.",{"tldr":158,"method":159,"finding":160,"direction":161,"opportunity":162},"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。","农业人工智能与决策模型","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":165,"title":166,"url":167,"summary":168,"summary_zh":169,"content":9,"source_name":170,"source_url":167,"published_at":171,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":172,"score_detail":173,"sources":176,"tags":178,"primary_topic":181,"secondary_topics":182,"classification":189,"search_phrases":224,"slug":227,"view_count":95,"doi":228,"paper":229,"created_at":260},2947,"AI-enabled UAV-based Soil Organic Carbon Mapping in Arid Environments: A Pilot Study Protocol","https:\u002F\u002Fdoi.org\u002F10.2174\u002F0118743315495282260915110324","Introduction Soil organic carbon (SOC) is an important indicator of soil health, agricultural productivity, and carbon sequestration potential. However, accurate and scalable SOC mapping in arid environments is constrained by high spatial heterogeneity and the limitations of conventional soil sampling. This study aims to develop a standardized UAV-enabled framework for high-resolution SOC mapping in arid agricultural environments. Methods A pilot-study protocol integrating UAV-based hyperspectral remote sensing with artificial intelligence and machine learning was developed. The workflow encompasses study-site selection, ground-reference sampling, UAV hyperspectral data acquisition, radiometric and geometric preprocessing, spectral feature extraction and selection, machine-learning model development, validation, uncertainty assessment, and performance evaluation using R 2 , RMSE, and MAE. The protocol also incorporates assessment of environmental confounders, including soil moisture, surface roughness, and crop residues. Results The resulting framework provides a systematic and reproducible workflow for UAV-based SOC estimation, integrating field observations, hyperspectral features, predictive modelling, and uncertainty assessment. It establishes defined procedures for evaluating model robustness and transferability across varying field conditions. Discussion The framework addresses an important methodological gap in UAV-enabled SOC mapping by integrating remote sensing and AI within a standardized pilot-study design. Its emphasis on environmental confounders and uncertainty assessment can improve the reliability and comparability of SOC mapping studies. However, field validation across diverse arid environments remains necessary. Conclusion The proposed protocol provides a practical foundation for reproducible SOC mapping and subsequent field validation, supporting precision agriculture, sustainable soil management, and carbon monitoring, reporting, and verification (MRV) in arid regions.","引言 土壤有机碳（SOC）是衡量土壤健康、农业生产力及碳固存潜力的重要指标。然而，干旱环境中高空间异质性和传统土壤采样的局限性制约了准确且可扩展的SOC制图。本研究旨在开发一个标准化的无人机（UAV）框架，用于干旱农业环境中的高分辨率SOC制图。方法 开发了一套整合无人机高光谱遥感与人工智能及机器学习的试点研究方案。该工作流程涵盖研究地点选择、地面参考采样、无人机高光谱数据采集、辐射与几何预处理、光谱特征提取与选择、机器学习模型开发、验证、不确定性评估，以及使用R²、RMSE和MAE进行的性能评价。该方案还包括对环境混杂因素的评估，包括土壤水分、地表粗糙度和作物残茬。结果 所构建的框架为基于无人机的SOC估算提供了系统且可重复的工作流程，整合了野外观测、高光谱特征、预测建模和不确定性评估。它建立了明确的程序，用于评估模型在不同田间条件下的稳健性和可迁移性。讨论 该框架通过将遥感与人工智能整合于标准化的试点研究设计中，填补了无人机SOC制图领域的重要方法学空白。其对环境混杂因素和不确定性评估的重视，可提高SOC制图研究的可靠性和可比性。然而，仍需在不同干旱环境中进行田间验证。结论 所提出的方案为可重复的SOC制图及后续田间验证提供了实用基础，支持干旱地区的精准农业、可持续土壤管理以及碳监测、报告与核查（MRV）。","The Open Agriculture Journal","2026-09-18T00:00:00Z",67,{"impact":20,"substance":51,"depth":174,"authority":108,"freshness":21,"relevant":22,"comment":175},16,"提出无人机高光谱结合AI的干旱区土壤有机碳制图标准化方案，方法框架清晰但尚属试点协议、缺乏实地验证，具备一定参考价值。",[177],{"name":170,"url":167},[27,28,29,179,180],"遥感","土壤碳汇",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[183,184],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":185,"slug":186,"name":187,"path":188,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":45,"locked":14,"version":46,"method":47,"evidence":190},{"candidates":191,"agricultural_evidence":52},[192,196,201,207,211,218],{"code":39,"score":107,"supported":52,"title":193,"abstract":194,"tags":195},[40],[63,40],[],{"code":32,"score":13,"supported":52,"title":197,"abstract":198,"tags":200},[],[57,58,199],"artificial intelligence",[28],{"code":185,"score":13,"supported":52,"title":202,"abstract":203,"tags":206},[],[179,204,205],"remote sensing","hyperspectral",[179],{"code":66,"score":21,"supported":52,"title":208,"abstract":209,"tags":210},[],[29,69],[27,29],{"code":212,"score":213,"supported":52,"title":214,"abstract":215,"tags":217},"H4",4,[],[216],"carbon sequestration",[180],{"code":219,"score":146,"supported":52,"title":220,"abstract":221,"tags":223},"H7",[],[222],"干旱",[],[225,226],"无人机 土壤有机碳 制图","AI 高光谱 干旱农业","无人机土壤有机碳制图-2947","10.2174\u002F0118743315495282260915110324",{"doi":228,"openalex_id":230,"authors":231,"venue":170,"cited_by_count":75,"oa_url":167,"card":255,"direction":89,"ingested_from":92},"W7213561504",[232,235,238,241,244,247,249,251,253],{"name":233,"orcid":234},"Moath Awawdeh","https:\u002F\u002Forcid.org\u002F0000-0003-1404-6782",{"name":236,"orcid":237},"Irfan Ahmed","https:\u002F\u002Forcid.org\u002F0000-0002-2172-4177",{"name":239,"orcid":240},"Anees Bashir","https:\u002F\u002Forcid.org\u002F0000-0002-4668-6592",{"name":242,"orcid":243},"Tarig Faisal","https:\u002F\u002Forcid.org\u002F0000-0001-6451-7576",{"name":245,"orcid":246},"Nicky Rahmana Putra","https:\u002F\u002Forcid.org\u002F0000-0003-4886-496X",{"name":248,"orcid":9},"Almaha Jamal",{"name":250,"orcid":9},"Afra Rashed",{"name":252,"orcid":9},"Hamda Yousif",{"name":254,"orcid":9},"Sarah Sadeq",{"tldr":256,"method":257,"finding":258,"direction":89,"opportunity":259},"提出一套无人机高光谱结合AI的干旱区土壤有机碳制图标准化试点方案。","无人机高光谱遥感、地面采样、光谱特征选择与机器学习建模，用R²、RMSE、MAE","构建了可复现的SOC估算流程，并纳入环境混杂因素与不确定性评估。","可在多干旱区开展跨区域验证，探索模型迁移性与不确定性量化方法。","2026-09-19T23:30:33.273156Z",{"id":262,"title":263,"url":264,"summary":265,"summary_zh":266,"content":9,"source_name":267,"source_url":264,"published_at":268,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":269,"score_detail":270,"sources":272,"tags":274,"primary_topic":276,"secondary_topics":277,"classification":284,"search_phrases":308,"slug":311,"view_count":312,"doi":313,"paper":314,"created_at":327},2509,"Artificial Intelligence-Based Crop Recommendation Using Soil and Climate Data: A Comprehensive Review of Machine Learning, Deep Learning, and Smart Agriculture Approaches","https:\u002F\u002Fdoi.org\u002F10.64388\u002Firev10i3-1722922","Crop recommendation systems that integrate soil and climate data with artificial intelligence (AI) have expanded rapidly since 2020, spanning classical machine learning (ML), ensemble methods, deep learning, explainable AI (XAI), and Internet of Things (IoT)-enabled sensing. This review critically synthesizes 35 sources — 33 peer-reviewed journal articles and conference papers plus 2 preprints retained only for background context — comprising 12 studies verified in full against their primary text and 23 studies verified at the bibliographic level, to examine what has been attempted, which data and algorithms have been used, and how reliable the reported results are. The reviewed literature shows convergent use of a narrow feature set (nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall) and recurring near-ceiling accuracy, including cases at or above 98% [1], [5], [6] and, in one case, a reported 1.00 across accuracy, precision, recall, and F1-score following class-balancing [7]. Cross-examination of dataset descriptions across studies reveals inconsistent provenance and documentation: structurally similar seven-feature datasets are described with different national contexts and reported sample sizes ranging from 2,100 to 3,000 records [5], [6], [9]. Validation practice is dominated by random hold-out or k-fold cross-validation; among the studies examined in full, only one tested spatial cross-validation, reporting a substantial performance decline (AUC 0.89 to 0.55–0.62) relative to random-split results [4]. Explainable AI and uncertainty quantification remain minority practices in the reviewed literature. A prior conference proceedings paper self-described as a","自2020年以来，将土壤和气候数据与人工智能（AI）相结合的作物推荐系统迅速扩展，涵盖经典机器学习（ML）、集成方法、深度学习、可解释人工智能（XAI）以及物联网（IoT）赋能的传感技术。本综述批判性地综合了35个来源——33篇同行评审期刊论文和会议论文，以及2篇仅用于背景参考的预印本——其中包括12项经全文核实的研究和23项经书目层面核实的研究，以考察已尝试的研究方向、所使用的数据和算法，以及所报告结果的可靠性。所综述的文献显示，特征集使用趋同且范围狭窄（氮、磷、钾、温度、湿度、pH和降雨量），准确率反复接近上限，包括达到或超过98%的案例[1], [5], [6]，以及一例在类别平衡后准确率、精确率、召回率和F1分数均报告为1.00的研究[7]。对各项研究中数据集描述的交叉审查揭示了来源和文档记录的不一致：结构相似的七特征数据集被描述为不同的国家背景，报告的样本量从2,100到3,000条记录不等[5], [6], [9]。验证实践以随机留出法或k折交叉验证为主；在经全文审查的研究中，仅有一项测试了空间交叉验证，报告称相对于随机划分结果，性能显著下降（AUC从0.89降至0.55–0.62）[4]。可解释人工智能和不确定性量化在所综述文献中仍属少数实践。一篇先前会议论文集中自述为","Iconic Research and Engineering Journals","2026-09-14T00:00:00Z",61,{"impact":20,"substance":51,"depth":174,"authority":95,"freshness":109,"relevant":22,"comment":271},"系统综述揭示作物推荐模型普遍存在数据集来源混乱与验证方法单一（仅一项空间交叉验证即大幅掉点）的问题，对智慧农业AI落地有实质警示价值，但期刊影响力有限。",[273],{"name":267,"url":264},[27,28,29,114,275],"土壤数据",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[278,279],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":280,"slug":281,"name":282,"path":283,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":45,"locked":14,"version":46,"method":47,"evidence":285},{"candidates":286,"agricultural_evidence":52},[287,292,296,301],{"code":32,"score":288,"supported":52,"title":289,"abstract":290,"tags":291},34,[58,54,199],[56,57,58,54,199],[28],{"code":39,"score":17,"supported":52,"title":293,"abstract":294,"tags":295},[40],[63,40],[275],{"code":66,"score":13,"supported":52,"title":297,"abstract":299,"tags":300},[298],"smart agriculture",[],[27,29],{"code":280,"score":109,"supported":52,"title":302,"abstract":303,"tags":307},[],[304,305,306],"物联网","internet of things","IoT",[],[309,310],"农业人工智能 作物推荐 土壤数据 智慧农业","农业人工智能 作物推荐","农业人工智能作物推荐土壤数据智慧农业-2509",2,"10.64388\u002Firev10i3-1722922",{"doi":313,"openalex_id":315,"authors":316,"venue":267,"cited_by_count":75,"oa_url":321,"card":322,"direction":91,"ingested_from":92},"W7212594050",[317,319],{"name":318,"orcid":9},"Snehal Sanjay Raut",{"name":320,"orcid":9},"Yogesh V. Chimate","https:\u002F\u002Fwww.irejournals.com\u002Fformatedpaper\u002F1722922.pdf",{"tldr":323,"method":324,"finding":325,"direction":161,"opportunity":326},"综述AI作物推荐研究，指出高准确率多源于数据与验证缺陷。","系统综述35篇文献，对比ML、DL、XAI与IoT方法及验证方式。","常用7特征数据集来源不一，随机验证致准确率虚高，空间验证性能骤降。","需建立标准化数据集并推广空间交叉验证与不确定性量化，提升模型真实泛化能力。","2026-09-15T23:30:08.397298Z",{"id":329,"title":330,"url":331,"summary":332,"summary_zh":333,"content":9,"source_name":103,"source_url":331,"published_at":334,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":335,"score_detail":336,"sources":338,"tags":340,"primary_topic":344,"secondary_topics":345,"classification":347,"search_phrases":363,"slug":366,"view_count":75,"doi":367,"paper":368,"created_at":382},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。","2026-10-21T00:00:00Z",64,{"impact":21,"substance":18,"depth":19,"authority":109,"freshness":13,"relevant":22,"comment":337},"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。",[339],{"name":103,"url":331},[27,28,341,342,343],"主动学习","线虫检测","土壤采样",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[346],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":348},{"candidates":349,"agricultural_evidence":52},[350,354,358],{"code":39,"score":107,"supported":52,"title":351,"abstract":352,"tags":353},[40],[63,40],[],{"code":32,"score":109,"supported":52,"title":355,"abstract":356,"tags":357},[199],[],[28],{"code":66,"score":359,"supported":52,"title":360,"abstract":361,"tags":362},7,[],[29,69],[27],[364,365],"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":367,"openalex_id":369,"authors":370,"venue":103,"cited_by_count":75,"oa_url":331,"card":377,"direction":161,"ingested_from":92},"W7223082195",[371,373,375],{"name":372,"orcid":9},"Amine Ben Salah",{"name":374,"orcid":9},"Ines Trabelsi",{"name":376,"orcid":9},"Mohamed Aziz Khelifi",{"tldr":378,"method":379,"finding":380,"direction":161,"opportunity":381},"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":384,"title":385,"url":386,"summary":387,"summary_zh":388,"content":9,"source_name":389,"source_url":386,"published_at":390,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":391,"sources":394,"tags":396,"primary_topic":399,"secondary_topics":400,"classification":403,"search_phrases":426,"slug":429,"view_count":75,"doi":430,"paper":431,"created_at":451},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",{"impact":20,"substance":392,"depth":19,"authority":108,"freshness":109,"relevant":22,"comment":393},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。",[395],{"name":389,"url":386},[27,28,179,397,398],"土壤盐渍化","新疆农业",{"code":185,"slug":186,"name":187,"path":188,"parent_code":36},[401,402],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":404},{"candidates":405,"agricultural_evidence":52},[406,410,414,418,422],{"code":185,"score":17,"supported":52,"title":407,"abstract":408,"tags":409},[204],[179,204],[179],{"code":39,"score":107,"supported":52,"title":411,"abstract":412,"tags":413},[40],[63,40],[],{"code":32,"score":109,"supported":52,"title":415,"abstract":416,"tags":417},[54],[],[28],{"code":219,"score":146,"supported":52,"title":419,"abstract":420,"tags":421},[],[222],[],{"code":66,"score":22,"supported":14,"title":423,"abstract":424,"tags":425},[],[],[27],[427,428],"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":430,"openalex_id":432,"authors":433,"venue":389,"cited_by_count":75,"oa_url":386,"card":446,"direction":89,"ingested_from":92},"W7221816183",[434,436,439,441,443],{"name":435,"orcid":9},"Mingjie Qian",{"name":437,"orcid":438},"Liangjun Wang","https:\u002F\u002Forcid.org\u002F0009-0006-8718-8326",{"name":440,"orcid":9},"Peng Wang",{"name":442,"orcid":9},"Hangyuan Liu",{"name":444,"orcid":445},"Weitao Chen","https:\u002F\u002Forcid.org\u002F0000-0002-6272-1618",{"tldr":447,"method":448,"finding":449,"direction":89,"opportunity":450},"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":453,"title":454,"url":455,"summary":456,"summary_zh":457,"content":9,"source_name":103,"source_url":455,"published_at":458,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":459,"score_detail":460,"sources":462,"tags":464,"primary_topic":468,"secondary_topics":469,"classification":471,"search_phrases":487,"slug":490,"view_count":75,"doi":491,"paper":492,"created_at":504},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":20,"substance":18,"depth":174,"authority":108,"freshness":21,"relevant":22,"comment":461},"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。",[463],{"name":103,"url":455},[27,28,465,466,467],"决策支持系统","土壤墒情","作物选择",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[470],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":45,"locked":14,"version":46,"method":47,"evidence":472},{"candidates":473,"agricultural_evidence":52},[474,478,483],{"code":39,"score":17,"supported":52,"title":475,"abstract":476,"tags":477},[40],[63,40],[466],{"code":32,"score":479,"supported":52,"title":480,"abstract":481,"tags":482},11,[],[57,58,129],[28,465],{"code":66,"score":22,"supported":14,"title":484,"abstract":485,"tags":486},[],[],[27],[488,489],"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":491,"openalex_id":493,"authors":494,"venue":103,"cited_by_count":75,"oa_url":455,"card":499,"direction":91,"ingested_from":92},"W7220991946",[495,497],{"name":496,"orcid":9},"C. SAI KUMAR",{"name":498,"orcid":9},"Prof. G SREENIVASULU",{"tldr":500,"method":501,"finding":502,"direction":161,"opportunity":503},"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"]