[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3367":3,"related-3367":56},{"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,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":55},3367,"Digital Technology Adoption Conditioning Analysis Model in Agriculture","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202609.1880.v1","Technological advancements have been responsible for a significant part of the growth in agricultural productivity in recent years. Digital technologies have a high potential to enable the development of the agricultural sector, reshape value chains, and significantly contribute to more productive, resilient, and transparent food systems; however, their adoption in Brazil remains uneven due to regional disparities and structural bottlenecks. The research investigated this problem to build and validate the Digital Technology Adoption Conditioning Analysis Model (MAC-AgriTech), through a case study with Brazilian agricultural data, encompassing the identification of conditioning factors, their territorial evaluation, and the proposition of actions, while providing structured data collection and analysis instruments. The spatial analysis revealed deep territorial asymmetries, concentrating resources and infrastructure in the South and Southeast regions. Econometric modeling demonstrated that digital adoption is primarily driven by the producer’s digital familiarity, connectivity quality, and property scale, with 77% of producers identifying acquisition and maintenance costs as the primary barrier. The transition to digital agriculture in Brazil requires targeted, multidimensional public policies—such as expanded rural connectivity, technical training, and subsidized credit—to overcome regional gaps, and to increase agricultural competitiveness, efficiency, and sustainability.","近年来，技术进步对农业生产力增长贡献显著。数字技术具有巨大潜力，能够推动农业部门发展、重塑价值链，并为构建更高产、更具韧性且更透明的粮食体系作出重要贡献；然而，由于区域差异和结构性瓶颈，其在巴西的采用仍不均衡。本研究针对这一问题，通过一项基于巴西农业数据的案例研究，构建并验证了数字技术采用条件分析模型（MAC-AgriTech），涵盖条件因素的识别、其区域性评估以及行动建议的提出，同时提供了结构化的数据收集与分析工具。空间分析揭示了深刻的区域不对称性，资源和基础设施集中在南部和东南部地区。计量经济建模表明，数字采用主要受生产者数字熟悉度、连接质量和财产规模的驱动，其中77%的生产者将购置和维护成本视为主要障碍。巴西向数字农业的转型需要有针对性的、多维度的公共政策——如扩大农村连接、技术培训和补贴信贷——以克服区域差距，并提高农业竞争力、效率和可持续性。",null,"Preprints.org","2026-09-22T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,20,17,6,8,1,"基于巴西农业数据的数字技术采纳条件分析模型研究，方法系统、结论有实证支撑，但属预印本且聚焦巴西，对国内参考价值有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字农业","智慧农业","农业人工智能","巴西农业","农村数字化",[33,34],"巴西 数字农业 技术采纳","MAC-AgriTech 模型","巴西数字农业技术采纳-3367",0,"10.20944\u002Fpreprints202609.1880.v1",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":48,"direction":52,"ingested_from":54},"W7214109425",[41,44,46],{"name":42,"orcid":43},"Isabela Santos","https:\u002F\u002Forcid.org\u002F0009-0002-3659-2020",{"name":45,"orcid":9},"Eduardo Dias",{"name":47,"orcid":9},"Lidia Scoton",{"tldr":49,"method":50,"finding":51,"direction":52,"opportunity":53},"构建并验证MAC-AgriTech模型，分析巴西农业数字技术采纳的条件因素与区域差异。","巴西农业数据案例研究，空间分析与计量经济建模。","采纳主要由数字熟悉度、连接质量和农场规模驱动，77%生产者视成本为首要障碍。","数字乡村与农业信息化","可延伸至中国等发展中国家，探究数字素养、基础设施与政策组合对技术采纳的因果效应。","openalex","2026-09-24T23:30:27.046035Z",{"total":20,"page":22,"page_size":20,"items":57},[58,112,136,176,209,252],{"id":59,"title":60,"url":61,"summary":62,"summary_zh":63,"content":9,"source_name":64,"source_url":61,"published_at":65,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":66,"score_detail":67,"sources":70,"tags":72,"search_phrases":75,"slug":78,"view_count":36,"doi":79,"paper":80,"created_at":111},3347,"Integrating Material Flow Cost Accounting and IoT-Based Monitoring for Eco-Efficient Goat Farm Management","https:\u002F\u002Fdoi.org\u002F10.35145\u002F6e5wnv18","Goat farming plays an important role in supporting rural livelihoods, food production, and agricultural sustainability. However, conventional goat farm management often separates environmental monitoring, financial accounting, and livestock management, limiting the ability to identify resource inefficiencies and associated environmental impacts. This study aims to develop and implement GEMBALA (Green Eco-smart Management-Based Automation for Livestock and Accounting), an integrated digital platform that combines Internet of Things (IoT)-based environmental monitoring, Material Flow Cost Accounting (MFCA), emission analysis, artificial intelligence-based livestock management, and analytical reporting. The research employed a research and development approach in collaboration with CV Cahaya Firdaus (Fathur Farm). An IoT sensor prototype was developed, installed, and tested in a real goat farming environment to monitor temperature, humidity, Heat Index (THI), ammonia gas, and dust density. The platform also incorporates MFCA, emission, AI Estrus, AI Health, and analytical reporting modules. The results demonstrate progress toward integrating environmental, economic, and livestock management information within a unified digital platform. However, further validation is required to improve sensor data transmission, synchronization, emission calculations, MFCA data consistency, and AI performance evaluation. The study provides a foundation for eco-economic decision support, sustainable livestock management, and future commercialization of digital livestock technologies.","山羊养殖在支撑农村生计、粮食生产和农业可持续性方面发挥着重要作用。然而，传统的山羊养殖场管理往往将环境监测、财务核算和畜牧管理相互分离，限制了识别资源低效利用及相关环境影响的能力。本研究旨在开发并实施GEMBALA（基于绿色生态智能管理的畜牧与会计自动化平台），这是一个集成了基于物联网（IoT）的环境监测、物料流成本会计（MFCA）、排放分析、基于人工智能的畜牧管理以及分析报告的综合数字平台。研究采用研发方法，与CV Cahaya Firdaus（Fathur Farm）合作开展。研究开发了物联网传感器原型，并在真实山羊养殖环境中进行安装和测试，用于监测温度、湿度、热指数（THI）、氨气和粉尘密度。该平台还整合了MFCA、排放、AI发情检测、AI健康和分析报告模块。结果表明，在将环境、经济和畜牧管理信息整合到统一数字平台方面取得了进展。然而，仍需进一步验证，以改进传感器数据传输、同步、排放计算、MFCA数据一致性以及AI性能评估。本研究为生态经济决策支持、可持续畜牧管理以及数字畜牧技术的未来商业化提供了基础。","Journal of Applied Business and Technology","2026-09-24T00:00:00Z",62,{"impact":21,"substance":68,"depth":17,"authority":13,"freshness":13,"relevant":22,"comment":69},18,"论文提出融合物联网监测与物料流成本核算的山羊养殖数字平台，方法有创新但尚处原型验证阶段，产业影响有限。",[71],{"name":64,"url":61},[27,28,29,73,74],"农业物联网","畜牧养殖",[76,77],"GEMBALA 山羊养殖 物联网","MFCA 畜牧 环境监测","GEMBALA山羊养殖物联网-3347","10.35145\u002F6e5wnv18",{"doi":79,"openalex_id":81,"authors":82,"venue":64,"cited_by_count":36,"oa_url":61,"card":105,"direction":109,"ingested_from":54},"W7214075234",[83,85,87,89,91,93,96,99,101,103],{"name":84,"orcid":9},"Nicholas Renaldo",{"name":86,"orcid":9},"Sulaiman Musa",{"name":88,"orcid":9},"Jaswar Koto",{"name":90,"orcid":9},"Kristy Veronica",{"name":92,"orcid":9},"Umar Faruq",{"name":94,"orcid":95},"Yulvia Nora Marlim","https:\u002F\u002Forcid.org\u002F0009-0007-8624-5023",{"name":97,"orcid":98},"Rangga Rahmadian Yuliendi","https:\u002F\u002Forcid.org\u002F0000-0003-2288-3580",{"name":100,"orcid":9},"Wilda Susanti",{"name":102,"orcid":9},"Achmad Tavip Junaedi",{"name":104,"orcid":9},"Nabila Wahid",{"tldr":106,"method":107,"finding":108,"direction":109,"opportunity":110},"开发集成物联网监测与物料流成本核算的山羊养殖数字平台GEMBALA。","研发方法，在真实羊场部署物联网传感器并集成MFCA、排放分析与AI模块。","平台初步实现环境、经济与养殖信息整合，但传感器传输与数据一致性仍需验证。","智慧农业 \u002F 农业物联网","可延伸研究物联网数据与MFCA实时耦合的算法优化及AI模块的长期性能验证。","2026-09-24T23:30:09.863218Z",{"id":113,"title":114,"url":115,"summary":116,"summary_zh":9,"content":117,"source_name":118,"source_url":9,"published_at":11,"category":119,"cover_url":9,"hotness":13,"is_selected":14,"score":120,"score_detail":121,"sources":126,"tags":128,"search_phrases":131,"slug":134,"view_count":36,"doi":9,"paper":9,"created_at":135},3215,"秋分逢丰收节 机器人成主角！浙江田野正被AI\"接管\"——浙江农科院数字农业研究所研发AI眼镜+害虫识别小程序","https:\u002F\u002Fwww.cztv.com\u002FnewsDetail\u002F904714","9-22 新蓝网专题报道：在湖州德清县农博家庭农场，种植大户王菊仙戴上一副AI眼镜，对着诱杀害虫的黄板轻轻一扫，\"镜片上、手机端，种类、数量、位置等数据瞬间显现\"。这套由浙江省农科院数字农业研究所研发的设备，正将虫害防控从\"事后补救\"推向\"提前预警\"。在湖州吴兴丰盛湾水产种业，\"云眸\"沼虾养殖AI系统能在3-5秒内捕捉沼虾触须末端细微影像，自动生成比对图谱，一旦发现活动异常即刻标记预警，自动投料机器人与水下传感器联动精准计算投喂量，饲料利用率提升12%-15%、巡塘人力节省六成、养殖效益整体提高10%以上。在杭州余杭区径山镇，无人驾驶拖拉机搭载北斗导航系统自主作业。在杭州临平区田立方未来农场，450亩无人智慧农场示范区配套200余个田间传感器和4个物联网微基站，可根据土壤饱和度和实时水位自动确定浇灌量，一亩地一季油菜花可节水约1000吨。浙江省农业农村厅数据显示，截至目前浙江已累计建成数字农业工厂729家、未来农场63家。","![Image 2](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002FloginBg-OXMHhVd9.png)\n\n验证码登录\n\n获取验证码\n\n 一键登录 \n\n- [x]  \n\n登录代表同意 《用户协议》及 《隐私政策》\n\n扫码登录\n\n![Image 3](https:\u002F\u002Fwww.cztv.com\u002FnewsDetail\u002F904714)\n\n鼠标悬浮刷新二维码\n\n打开Z视介扫码登录\n\n![Image 4](blob:http:\u002F\u002Flocalhost\u002F08f361b6a75bd12fbead3ead9d0ae42d)\n\n[![Image 5](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Flogo-DNzgtwnp.png)](https:\u002F\u002Fwww.cztv.com\u002F)\n\n[首页](https:\u002F\u002Fwww.cztv.com\u002F)\n\n[新闻](https:\u002F\u002Fwww.cztv.com\u002Fheadlines)\n\n[文化](https:\u002F\u002Fwww.cztv.com\u002Fculture)\n\n 电视 \n\n 广播 \n\n[专区](https:\u002F\u002Fwww.cztv.com\u002Fzone)\n\n![Image 6](blob:http:\u002F\u002Flocalhost\u002F0062bfa43494c79fb356b384bfc51bc8)\n\n![Image 7: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Faibtn_icon-Dqb11ejT.png)\n\n![Image 8](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002FQRcode1-C1Z3XA1v.png)\n\n![Image 9: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Fdownload_lxw-BTIOt7tT.png)更多精彩 中国蓝新闻\n\n![Image 10](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002FQRcode2-CU2IzmPJ.png)\n\n![Image 11: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Fdownload_zsj-C7ZTF9AF.png)更多精彩 下载Z视介\n\n[![Image 12](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Fdownload_more_btn-DzzjA4z1.png)](https:\u002F\u002Fzmtv.cztv.com\u002Fcmsh5-share\u002Fprod\u002FcommonDownload\u002Findex.html)\n\n登录\n\n![Image 13: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002FcreateCenter_btn-CP1dJyCT.png)\n\n![Image 14: 1](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Faiblue-BYHqI38h.png)\n\nNaN-NaN-NaN NaN:NaN\n\n编辑：\n\n作者：\n\n[](javascript:; \"分享到微信\") \n\n![Image 15](https:\u002F\u002Fwww.cztv.com\u002Fassets\u002Flogo-DNzgtwnp.png)\n\nCopyright 2009-2024 cztv.com [浙ICP备05052141号-1](https:\u002F\u002Fbeian.miit.gov.cn\u002F#\u002FIntegrated\u002Findex) | [浙公网安备 33010602002235号](http:\u002F\u002Fwww.beian.gov.cn\u002Fportal\u002FregisterSystemInfo?recordcode=33010602002235) | 信息网络传播视听节目许可证号：1107197\n\n[互联网新闻信息服务许可证号：33120170002](https:\u002F\u002Fwww.cztv.com\u002Fxuke) | 网络文化经营许可证号：浙网文(2023)1495-044号\n\n 地址：浙江省杭州市莫干山路111号 邮政编码：310005 邮箱：cztv@zmg.com.cn\n\n 网上有害信息举报专区 12321网络不良与垃圾信息举报受理中心 网络违法犯罪举报网站 网络举报APP下载 \n\n 违法和不良信息公开举报电话：12377、0571-81089789 有害信息举报邮箱：jubao@12377.cn 涉未成年人有害信息举报电话: 0571-81089789 \n\n[点播](https:\u002F\u002Fwww.cztv.com\u002Fvideo)\n\n[直播](https:\u002F\u002Fwww.cztv.com\u002FliveTV)\n\n[点播](https:\u002F\u002Fwww.cztv.com\u002Fradio)\n\n[直播](https:\u002F\u002Fwww.cztv.com\u002FliveRadio)","新蓝网","报道",60,{"impact":17,"substance":122,"depth":123,"authority":124,"freshness":124,"relevant":22,"comment":125},14,12,9,"省级官媒报道浙江农科院数字农业研究所AI眼镜与害虫识别小程序落地，属智慧农业细分进展，时效性强但正文信息量有限。",[127],{"name":118,"url":115},[27,28,29,129,130],"智能农机","害虫识别",[132,133],"浙江农科院 数字农业研究所 AI眼镜","浙江 害虫识别 小程序","浙江农科院数字农业研究所AI眼镜-3215","2026-09-23T00:04:29.218317Z",{"id":137,"title":138,"url":139,"summary":140,"summary_zh":141,"content":9,"source_name":142,"source_url":139,"published_at":143,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":144,"score_detail":145,"sources":149,"tags":151,"search_phrases":154,"slug":157,"view_count":36,"doi":158,"paper":159,"created_at":175},2542,"Discrete Element Method-Based Modeling and Application of Agricultural Materials: A Review","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16181958","Discrete element method (DEM) has become an essential tool for analyzing particle-scale behavior and complex interactions in agricultural materials and machinery. This review synthesizes recent advances in DEM-based modeling and applications in agricultural engineering. It systematically examines particle modeling strategies, contact model development, and parameter calibration frameworks for representative materials such as soil, seeds, fertilizers, stalks, and roots. The evolution from simple spherical approximations to multi-sphere, bonded, and 3D scan-based reconstructions is highlighted, along with the shift from classical Hertz–Mindlin models to cohesive, elasto-plastic, and fracture-capable formulations. Calibration methods are traced from empirical assignment to systematic frameworks that integrate design of experiments, response surface methodology, and machine-learning-assisted inverse analysis. These advances in fidelity and accuracy have enabled extensive DEM applications in tillage, seeding, fertilization, and harvesting, with emphasis on equipment optimization, mechanism analysis, and performance prediction. Despite the progress, challenges remain in model standardization, parameter transferability, computational cost, and multiphysics coupling. Future work points to unified modeling frameworks, standardized databases, real-time simulation, and integration with artificial intelligence to support digital agriculture and intelligent machinery. This review aims to serve as a reference for high-fidelity DEM modeling and for advancing the digital transformation of agricultural engineering.","离散元法（DEM）已成为分析农业物料与机械中颗粒尺度行为及复杂相互作用的重要工具。本文综述了基于DEM的建模与农业工程应用的最新进展，系统考察了土壤、种子、肥料、茎秆和根系等代表性物料的颗粒建模策略、接触模型开发及参数标定框架。重点阐述了从简单球形近似到多球体、粘结型及基于三维扫描的重构方法的演变，以及从经典Hertz–Mindlin模型向黏聚、弹塑性及可断裂本构模型的转变。标定方法从经验赋值发展为集成试验设计、响应面法和机器学习辅助反分析的系统框架。这些在保真度和精度方面的进步使得DEM在耕整、播种、施肥和收获等领域得到广泛应用，重点涉及装备优化、机理分析和性能预测。尽管取得了进展，但在模型标准化、参数可迁移性、计算成本和多物理场耦合方面仍存在挑战。未来工作指向统一建模框架、标准化数据库、实时仿真以及与人工智能的融合，以支撑数字农业和智能机械发展。本文旨在为高保真DEM建模及推进农业工程数字化转型提供参考。","Agriculture","2026-09-12T00:00:00Z",76,{"impact":68,"substance":146,"depth":68,"authority":147,"freshness":20,"relevant":22,"comment":148},21,13,"系统综述离散元法在农业物料建模与装备优化中的进展，方法学价值高，对智能农机与数字农业有参考意义。",[150],{"name":142,"url":139},[27,28,29,152,153],"农业机械","离散元仿真",[155,156],"农业人工智能 离散元仿真 农业机械 数字农业","农业人工智能 离散元仿真","农业人工智能离散元仿真农业机械数字农业-2542","10.3390\u002Fagriculture16181958",{"doi":158,"openalex_id":160,"authors":161,"venue":142,"cited_by_count":36,"oa_url":139,"card":169,"direction":173,"ingested_from":54},"W7213152558",[162,164,166],{"name":163,"orcid":9},"Xingchi Zhou",{"name":165,"orcid":9},"Yanbin Liu",{"name":167,"orcid":168},"Zhenwei Liang","https:\u002F\u002Forcid.org\u002F0000-0001-6501-4364",{"tldr":170,"method":171,"finding":172,"direction":173,"opportunity":174},"综述农业物料离散元建模的粒子模型、接触模型与参数标定进展及应用。","综述离散元粒子建模、接触模型、标定方法及在耕播收中的应用。","建模从球形向多球\u002F键合\u002F3D重建演进，标定转向机器学习辅助反演。","农业人工智能与决策模型","可探索DEM与AI实时仿真、多物理场耦合及标准化参数数据库构建。","2026-09-15T23:30:36.643241Z",{"id":177,"title":178,"url":179,"summary":180,"summary_zh":181,"content":9,"source_name":182,"source_url":179,"published_at":183,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":144,"score_detail":184,"sources":186,"tags":188,"search_phrases":191,"slug":194,"view_count":36,"doi":195,"paper":196,"created_at":208},2046,"AI-Powered Crop Protection: Transforming Pest Surveillance and Decision-Making Through Digital Agriculture","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjsrr\u002F2026\u002Fv32i94503","Crop protection is increasingly challenged by changing pest population dynamics, pesticide resistance and the need for more sustainable agricultural production. Artificial intelligence (AI) and digital agriculture offer complementary tools for improving pest surveillance, forecasting and decision-making through automated detection, continuous monitoring and more precise interventions. This review synthesises the applications of machine learning, deep learning, computer vision, Internet of Things-enabled sensing, smart traps, remote sensing, unmanned aerial vehicles, robotics, predictive analytics and intelligent decision support systems in pest management. These approaches support pest identification, population monitoring, outbreak prediction, risk assessment and site-specific management, thereby strengthening the information base required for integrated pest management. The review also examines emerging technologies, including generative AI, foundation models, digital twins, explainable AI, edge intelligence and autonomous agricultural platforms, in relation to future crop protection systems. Despite rapid technological progress, practical deployment remains constrained by limited high-quality datasets, variable field conditions, model generalisation, interoperability, digital infrastructure, cost and farmer adoption. Effective implementation therefore requires transparent, robust and scalable systems that are validated under diverse agricultural conditions and aligned with ecological pest management principles. Overall, integrating AI-enabled surveillance and decision support with integrated pest management can support more timely, adaptive and resource-efficient crop protection while reducing unnecessary interventions.","作物保护正日益受到害虫种群动态变化、农药抗性以及更可持续农业生产需求的挑战。人工智能（AI）与数字农业通过自动检测、持续监测和更精准的干预措施，为改善害虫监测、预测和决策提供了互补性工具。本文综述了机器学习、深度学习、计算机视觉、物联网传感、智能诱捕器、遥感、无人机、机器人技术、预测分析和智能决策支持系统在害虫管理中的应用。这些方法支持害虫识别、种群监测、暴发预测、风险评估和位点特异性管理，从而强化了有害生物综合治理所需的信息基础。本文还探讨了新兴技术，包括生成式人工智能、基础模型、数字孪生、可解释人工智能、边缘智能和自主农业平台，及其在未来作物保护系统中的前景。尽管技术进展迅速，实际部署仍受限于高质量数据集不足、田间条件多变、模型泛化能力、互操作性、数字基础设施、成本以及农民采纳程度等因素。因此，有效实施需要透明、稳健且可扩展的系统，这些系统应在多样化农业条件下得到验证，并与生态害虫管理原则相一致。总体而言，将人工智能驱动的监测与决策支持同有害生物综合治理相结合，可支持更及时、更具适应性和资源效率更高的作物保护，同时减少不必要的干预。","Journal of Scientific Research and Reports","2026-09-09T00:00:00Z",{"impact":68,"substance":18,"depth":19,"authority":147,"freshness":21,"relevant":22,"comment":185},"系统综述AI与数字农业在病虫害监测预警与决策支持中的应用与瓶颈，信息密度高、结论可靠，对智慧植保方向有参考价值，但属综述类论文而非突破性成果，适合进入精选。",[187],{"name":182,"url":179},[27,28,29,189,190],"病虫害监测","精准植保",[192,193],"农业人工智能 病虫害监测 数字农业 智慧农业","农业人工智能 病虫害监测","农业人工智能病虫害监测数字农业智慧农业-2046","10.9734\u002Fjsrr\u002F2026\u002Fv32i94503",{"doi":195,"openalex_id":197,"authors":198,"venue":182,"cited_by_count":36,"oa_url":179,"card":203,"direction":109,"ingested_from":54},"W7212081296",[199,201],{"name":200,"orcid":9},"R. Saritha",{"name":202,"orcid":9},"M. Swathi",{"tldr":204,"method":205,"finding":206,"direction":173,"opportunity":207},"综述AI与数字农业在害虫监测、预测及决策支持中的应用与挑战。","综述机器学习、深度学习、计算机视觉、物联网、遥感、无人机与决策支持系统。","AI可提升害虫识别、暴发预测和精准管理，但受数据、泛化、成本与采纳制约。","可探索生成式AI、数字孪生与边缘智能在田间害虫管理中的可解释、可扩展落地验证。","2026-09-10T23:30:09.692422Z",{"id":210,"title":211,"url":212,"summary":213,"summary_zh":214,"content":9,"source_name":215,"source_url":212,"published_at":216,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":217,"score_detail":218,"sources":221,"tags":223,"search_phrases":227,"slug":230,"view_count":36,"doi":231,"paper":232,"created_at":251},3368,"A PCA-based deep feature optimization framework for explainable orange fruit disease classification","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12870-026-09984-8","Accurate classification of orange fruit diseases is important for precision agriculture and yield protection. This study develops and rigorously benchmarks a hybrid deep-feature framework for classifying Black Spot, Canker, Fresh, and Greening oranges (1,090 images), combining deep feature extraction, PCA-based dimensionality reduction, and classical machine-learning classification. Eight backbones (seven CNNs and a Vision Transformer, ViT) and four classifiers (32 configurations in total) were evaluated under 5 × 5 repeated stratified cross-validation, with PCA fitted exclusively on training-fold features in every iteration to eliminate data leakage. The proposed ViT + PCA+SVM configuration achieved the highest mean accuracy, 99.12%±0.71%, significantly outperforming every CNN-based backbone, including DenseNet201 + PCA + SVM (98.48%±0.81%, p \u003C 0.001). A dedicated variance-retention sensitivity analysis justifies the 98% threshold used throughout, and ablation experiments confirm that PCA substantially reduces feature dimensionality (by ~ 55.7% for ViT and ~ 76.6% for DenseNet201) without a significant loss of accuracy for either backbone. Explainability analysis — occlusion sensitivity and SHAP for the proposed ViT model, and Grad-CAM and SHAP for the DenseNet201 comparison model — shows that both configurations base predictions on biologically relevant, disease-affected regions of the fruit rather than spurious cues. These results identify ViT + PCA+SVM as the most accurate configuration evaluated, with DenseNet201 + PCA + SVM as a closely competitive, more compact convolutional alternative for intelligent orchard disease-monitoring systems.","橙类果实病害的准确分类对精准农业和产量保护具有重要意义。本研究开发并严格基准测试了一种混合深度特征框架，用于对黑斑病、溃疡病、新鲜和黄龙病橙类（1，090张图像）进行分类，该框架结合了深度特征提取、基于PCA的降维和经典机器学习分类。在5×5重复分层交叉验证下评估了八种骨干网络（七种CNN和一种视觉Transformer，ViT）和四种分类器（共32种配置），每次迭代中PCA仅在训练折特征上拟合以消除数据泄漏。所提出的ViT + PCA+SVM配置取得了最高平均准确率，为99.12%±0.71%，显著优于所有基于CNN的骨干网络，包括DenseNet201 + PCA + SVM（98.48%±0.81%，p \u003C 0.001）。专门的方差保留敏感性分析证明了全程使用的98%阈值是合理的，消融实验证实PCA大幅降低了特征维度（ViT约降低55.7%，DenseNet201约降低76.6%），且两种骨干网络均无显著准确率损失。可解释性分析——对所提出的ViT模型采用遮挡敏感性和SHAP，对DenseNet201对比模型采用Grad-CAM和SHAP——表明两种配置均基于果实中生物学相关的病害影响区域而非虚假线索进行预测。这些结果确定ViT + PCA+SVM为所评估的最准确配置，而DenseNet201 + PCA + SVM则是一种竞争力接近且更紧凑的卷积替代方案，可用于智能果园病害监测系统。","BMC Plant Biology","2026-09-23T00:00:00Z",79,{"impact":17,"substance":219,"depth":68,"authority":122,"freshness":124,"relevant":22,"comment":220},22,"方法严谨、数据与消融实验充分，对果园智能病害监测有实用参考价值，但属细分技术论文，产业影响有限。",[222],{"name":215,"url":212},[28,29,224,225,226],"可解释AI","病害识别","柑橘种植",[228,229],"柑橘病害 深度学习 分类","ViT PCA SVM 病害识别","柑橘病害深度学习分类-3368","10.1186\u002Fs12870-026-09984-8",{"doi":231,"openalex_id":233,"authors":234,"venue":215,"cited_by_count":36,"oa_url":212,"card":246,"direction":173,"ingested_from":54},"W7214068709",[235,237,239,241,244],{"name":236,"orcid":9},"Amruta Hingmire",{"name":238,"orcid":9},"Avinash Golande",{"name":240,"orcid":9},"Vinodkumar Bhutnal",{"name":242,"orcid":243},"Sagar Dhanraj Pande","https:\u002F\u002Forcid.org\u002F0000-0003-4506-6997",{"name":245,"orcid":9},"Tanuja Pande",{"tldr":247,"method":248,"finding":249,"direction":173,"opportunity":250},"提出PCA深度特征优化框架，用ViT+SVM分类橙子病害，准确率达99.12%。","8种骨干网络提取特征，PCA降维，4种分类器，5×5交叉验证。","ViT+PCA+SVM最优，PCA降维超55%且精度不降，可解释性验证有效。","可探索轻量化模型在移动端或边缘设备的实时病害检测与多作物泛化。","2026-09-24T23:30:34.116938Z",{"id":253,"title":254,"url":255,"summary":256,"summary_zh":257,"content":9,"source_name":258,"source_url":255,"published_at":216,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":259,"score_detail":260,"sources":262,"tags":264,"search_phrases":267,"slug":270,"view_count":36,"doi":271,"paper":272,"created_at":311},3366,"Brazilian Insect Survey: A Platform for Pest Management","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs13744-026-01426-2","Abstract Modern agriculture faces significant challenges in integrated pest management, where collecting, connecting, and processing monitoring data in real time are essential. This study presents the Brazilian Insect Survey (BIS), a web-based platform designed to centralize and streamline phytosanitary data management, enabling the integration of computer vision, field experimentation, and population modeling within a unified digital ecosystem. The platform is organized into functional modules that support experimental data management (TrapSystem and AgroExperiment), automated insect detection and counting from digital images (InsectCV and AphidCV), and simulation of aphid population dynamics (ABISM). These components operate in synergy, supported by distributed processing infrastructure, to ensure scalable data handling and efficient analytical workflows. Multi-year field applications demonstrate that integrating field data acquisition and automated image analysis with mechanistic population modeling enables timely, model-driven interventions that reduce aphid infestation levels and protect crop yield potential under variable environmental conditions, providing empirical evidence for queryPlease check if the captured keywords are correct.the effectiveness of combining AI-based insect detection with population dynamics modeling in operational integrated pest management. The case studies presented here demonstrate that the BIS platform successfully integrates computer vision, field experimentation, and population modeling within a modular digital ecosystem, highlighting its potential to enhance decision-making and advance data-driven integrated pest management.","摘要 现代农业在有害生物综合治理方面面临重大挑战，其中实时采集、连接和处理监测数据至关重要。本研究提出了巴西昆虫调查平台（Brazilian Insect Survey，BIS），这是一个基于网络的平台，旨在集中化和简化植物检疫数据管理，使计算机视觉、田间试验和种群建模能够整合在一个统一的数字生态系统中。该平台按功能模块组织，支持实验数据管理（TrapSystem和AgroExperiment）、基于数字图像的昆虫自动检测与计数（InsectCV和AphidCV），以及蚜虫种群动态模拟（ABISM）。这些组件在分布式处理基础设施的支持下协同运行，以确保可扩展的数据处理和高效的分析工作流。多年田间应用表明，将田间数据采集和自动图像分析与机制性种群建模相结合，能够实现及时的、模型驱动的干预，从而在多变的环境条件下降低蚜虫侵染水平并保护作物产量潜力，为将基于人工智能的昆虫检测与种群动态建模相结合在实际有害生物综合治理中的有效性提供了经验证据。本文所呈现的案例研究表明，BIS平台成功地将计算机视觉、田间试验和种群建模整合在一个模块化数字生态系统中，凸显了其在增强决策能力和推进数据驱动有害生物综合治理方面的潜力。","Neotropical Entomology",80,{"impact":68,"substance":219,"depth":68,"authority":122,"freshness":21,"relevant":22,"comment":261},"巴西昆虫调查平台将计算机视觉与种群动态模型整合进IPM实践，方法新颖、多年田间验证，对智慧农业病虫害监测有参考价值。",[263],{"name":258,"url":255},[28,29,265,189,266],"计算机视觉","种群模型",[268,269],"Brazil 蚜虫 监测平台","农业人工智能 病虫害监测 计算机视觉 智慧农业","Brazil蚜虫监测平台-3366","10.1007\u002Fs13744-026-01426-2",{"doi":271,"openalex_id":273,"authors":274,"venue":258,"cited_by_count":36,"oa_url":255,"card":306,"direction":52,"ingested_from":54},"W7214074071",[275,278,280,283,286,288,291,294,297,300,303],{"name":276,"orcid":277},"Telmo De Cesaro Júnior","https:\u002F\u002Forcid.org\u002F0000-0002-2886-229X",{"name":279,"orcid":9},"Bárbara Stella Wehrmann",{"name":281,"orcid":282},"Alexandre Tagliari Lazzaretti","https:\u002F\u002Forcid.org\u002F0009-0004-8345-8216",{"name":284,"orcid":285},"Roberto Wiest","https:\u002F\u002Forcid.org\u002F0000-0001-7982-6603",{"name":287,"orcid":9},"Jorge Luis Boeira Bavaresco",{"name":289,"orcid":290},"Brenda Slongo Taca","https:\u002F\u002Forcid.org\u002F0009-0000-6408-5523",{"name":292,"orcid":293},"Nicolas Welfer Kirinus","https:\u002F\u002Forcid.org\u002F0009-0003-5260-3161",{"name":295,"orcid":296},"Crislaine Sartori Suzana Milan","https:\u002F\u002Forcid.org\u002F0000-0003-0562-7286",{"name":298,"orcid":299},"Jayme Garcia Arnal Barbedo","https:\u002F\u002Forcid.org\u002F0000-0002-1156-8270",{"name":301,"orcid":302},"Douglas Lau","https:\u002F\u002Forcid.org\u002F0000-0001-8648-0102",{"name":304,"orcid":305},"Rafael Rieder","https:\u002F\u002Forcid.org\u002F0000-0002-7435-9054",{"tldr":307,"method":308,"finding":309,"direction":109,"opportunity":310},"巴西昆虫调查平台整合计算机视觉、田间试验与种群建模，实现害虫实时监测与治理。","构建BIS网络平台，集成InsectCV\u002FAphidCV图像识别、TrapSys","AI昆虫检测与种群动态建模结合可降低蚜虫侵害并保护作物产量。","可探索多害虫跨区域监测数据标准化与模型迁移，构建开放植保决策生态。","2026-09-24T23:30:25.977846Z"]