[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3358":3,"related-3358":68},{"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":67},3358,"One Toolchain, Six Domains: A Multiple-Case, Document-Based Study of Rapid IoT Prototypes Built in a One-Week Immersive Course on a Master’s Program in Applied Artificial Intelligence","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202609.2011.v1","This paper reports a document-based, multiple-case study of six Internet-of-Things (IoT) prototypes designed and simulated during a one-week immersive course, “IoT for Data Intelligence,” delivered in July 2026 within the professional Master in Applied Artificial Intelligence (Maestría en Inteligencia Artificial Aplicada, MNA) at Tecnológico de Monterrey. Six teams followed the same five-day toolchain IoT theory; Oracle Application Express (APEX), SQL, and REST service design; MIT App Inventor; ESP32\u002FWokwi simulation; and generative-AI integration and produced Wokwi-simulated prototypes spanning industrial energy monitoring, agricultural hazard response, residential automation, cardiovascular telemonitoring, industrial waste reduction, and precision agriculture. A fixed coding framework was applied across architecture, AI-integration pattern, platform-level failure modes, security debt, and Sustainable Development Goal alignment, distinguishing findings that the course structure itself prescribes from findings the teams introduced independently. The six cases converged on a shared five-layer architecture and, in a pattern only partly prescribed by the course, on keeping generative AI in an advisory or fail-safe-wrapped role. Deposited results were also compared, for illustrative purposes only, against the course’s internal competency rubric. An observed proposal from a Pontifical Catholic University of Chile’s collaboration is discussed as an informal reference point rather than as evidence for generalization. This paper discusses the implications and limits of this small, single-institution, single-cohort, simulation-only case set.","本文报告了一项基于文档的多案例研究，研究对象为六项物联网（Internet of Things, IoT）原型，这些原型是在2026年7月于蒙特雷理工学院（Tecnológico de Monterrey）应用人工智能专业硕士（Maestría en Inteligencia Artificial Aplicada, MNA）项目内开设的一周沉浸式课程“面向数据智能的物联网”（IoT for Data Intelligence）中设计与仿真的。六个团队遵循了相同的五日工具链——物联网理论；Oracle Application Express（APEX）、SQL与REST服务设计；MIT App Inventor；ESP32\u002FWokwi仿真；以及生成式AI集成——并产出了基于Wokwi仿真的原型，涵盖工业能源监测、农业灾害响应、住宅自动化、心血管远程监护、工业减废和精准农业。研究采用固定编码框架，从架构、AI集成模式、平台级失效模式、安全债务和可持续发展目标对齐五个维度进行分析，并区分了课程结构本身所规定的发现与各团队独立引入的发现。六个案例收敛于一个共享的五层架构，并在一种仅部分由课程规定的模式中，将生成式AI保持在顾问性或故障安全包裹的角色中。所提交的成果还仅出于示例目的与课程内部能力量规进行了比较。智利天主教大学一项合作中提出的方案作为非正式参照点加以讨论，而非作为可推广的证据。本文讨论了这一小型、单一机构、单一批次、仅仿真案例集的启示与局限。",null,"Preprints.org","2026-09-23T00:00:00Z","论文",10,false,50,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},6,16,15,4,9,1,"单校单期小样本的预印本教学案例研究，含农业物联网原型与生成式AI集成经验，但样本与仿真局限明显，公共价值有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","农业物联网","精准农业","农业教育",[33,34],"Tecnológico de Monterrey 物联网 课程","ESP32 Wokwi 农业物联网 原型","TecnológicodeMonterrey物联网课程-3358",0,"10.20944\u002Fpreprints202609.2011.v1",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":59,"direction":65,"ingested_from":66},"W7214071608",[41,44,47,50,53,56],{"name":42,"orcid":43},"Antonio Carlos Bento","https:\u002F\u002Forcid.org\u002F0000-0001-8264-4771",{"name":45,"orcid":46},"Alexandro Ortiz","https:\u002F\u002Forcid.org\u002F0000-0002-3945-6908",{"name":48,"orcid":49},"Grettel Barceló-Alonso","https:\u002F\u002Forcid.org\u002F0009-0004-3373-6441",{"name":51,"orcid":52},"Jose Reinaldo Silva","https:\u002F\u002Forcid.org\u002F0000-0003-2796-1613",{"name":54,"orcid":55},"Luis E. Falcón-Morales","https:\u002F\u002Forcid.org\u002F0000-0001-8760-5640",{"name":57,"orcid":58},"Sérgio Camacho-León","https:\u002F\u002Forcid.org\u002F0000-0002-5996-9997",{"tldr":60,"method":61,"finding":62,"direction":63,"opportunity":64},"基于六组一周IoT课程原型文档，分析其架构、AI集成与安全模式。","文档多案例研究，固定编码框架，Wokwi仿真与生成式AI集成。","六案例收敛于五层架构，生成式AI多限于建议或故障保护角色。","其他","可探究仿真原型向真实农田部署时，安全债务与AI角色如何演变。","智慧农业 \u002F 农业物联网","openalex","2026-09-24T23:30:13.353443Z",{"total":17,"page":22,"page_size":17,"items":69},[70,105,141,176,228,263],{"id":71,"title":72,"url":73,"summary":74,"summary_zh":75,"content":9,"source_name":76,"source_url":73,"published_at":77,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":78,"score_detail":79,"sources":84,"tags":86,"search_phrases":88,"slug":91,"view_count":36,"doi":92,"paper":93,"created_at":104},3161,"Artificial Intelligence in Plant Disease Detection: An Introduction to Intelligent and Automated Crop Health Monitoring","https:\u002F\u002Fdoi.org\u002F10.59256\u002Fijire.20260705005","Plant diseases are a major challenge in modern agriculture, as they can significantly reduce crop yield, crop quality, and economic productivity. Traditional plant disease detection methods mainly depend on visual inspection and expert knowledge, which can be time-consuming, subjective, and difficult to apply across large agricultural fields. The rapid advancement of Artificial Intelligence (AI), particularly Machine Learning (ML), Deep Learning (DL), and Computer Vision, has created new opportunities for automated and efficient crop disease detection and crop health monitoring. AI-based plant disease detection systems can analyze plant and leaf images to identify disease-related characteristics such as leaf discoloration, spots, lesions, texture variations, and abnormal growth patterns. Advanced techniques, including Convolutional Neural Networks (CNNs), transfer learning, image processing, image segmentation, and object detection, can be used for plant disease classification and identification of affected regions with high accuracy. This chapter introduces the fundamental concepts of AI-based plant disease detection, covering image acquisition, image preprocessing, feature extraction, model development, disease classification, and performance evaluation. It also examines the applications of AI in precision agriculture, smart agriculture, mobile-based plant disease diagnosis, drone-assisted crop monitoring, IoT-enabled farming, and edge-based agricultural systems. Furthermore, the chapter discusses important challenges such as limited and imbalanced datasets, environmental variations, similar disease symptoms, model generalization, computational requirements, and the need for explainable AI in agricultural applications. Finally, emerging trends and future opportunities are discussed, with emphasis on integrating AI with IoT, remote sensing, agricultural robotics, and multimodal agricultural data. The chapter provides a foundation for understanding how Artificial Intelligence for plant disease detection can support early disease identification, reduce crop losses, optimize agricultural resources, and contribute to sustainable and intelligent farming practices.","植物病害是现代农业面临的一项重大挑战，因为它们会显著降低作物产量、作物品质和经济生产力。传统的植物病害检测方法主要依赖视觉检查和专家知识，这种方式耗时、主观性强，且难以在大规模农田中应用。人工智能（AI）的快速发展，尤其是机器学习（ML）、深度学习（DL）和计算机视觉，为自动化、高效的作物病害检测和作物健康监测创造了新的机遇。基于AI的植物病害检测系统可以分析植物和叶片图像，以识别与病害相关的特征，如叶片变色、斑点、病斑、纹理变化和异常生长模式。包括卷积神经网络（CNN）、迁移学习、图像处理、图像分割和目标检测在内的先进技术，可用于植物病害分类和受影响区域的高精度识别。本章介绍了基于AI的植物病害检测的基本概念，涵盖图像采集、图像预处理、特征提取、模型开发、病害分类和性能评估。本章还探讨了AI在精准农业、智慧农业、基于移动端的植物病害诊断、无人机辅助作物监测、物联网（IoT）赋能农业和边缘农业系统中的应用。此外，本章讨论了重要挑战，如数据集有限且不平衡、环境变化、相似病害症状、模型泛化、计算需求，以及农业应用中可解释AI的需求。最后，讨论了新兴趋势和未来机遇，重点强调将AI与物联网、遥感、农业机器人和多模态农业数据相结合。本章为理解人工智能用于植物病害检测如何支持早期病害识别、减少作物损失、优化农业资源，并促进可持续和智能农业实践提供了基础。","International Journal of Innovative Research in Engineering","2026-09-21T00:00:00Z",59,{"impact":80,"substance":81,"depth":19,"authority":13,"freshness":82,"relevant":22,"comment":83},12,14,8,"系统综述AI在植物病害检测中的应用，内容全面但属教科书式介绍，方法新颖性与数据规模有限，可作为智慧农业主题聚合素材。",[85],{"name":76,"url":73},[27,28,29,30,87],"植物病害检测",[89,90],"AI 植物病害检测","无人机 作物健康监测","AI植物病害检测-3161","10.59256\u002Fijire.20260705005",{"doi":92,"openalex_id":94,"authors":95,"venue":76,"cited_by_count":36,"oa_url":9,"card":98,"direction":65,"ingested_from":66},"W7213950095",[96],{"name":97,"orcid":9},"Jamuna Ratcha",{"tldr":99,"method":100,"finding":101,"direction":102,"opportunity":103},"综述AI在植物病害检测中的应用，涵盖图像采集到模型评估全流程及未来趋势。","综述CNN、迁移学习、图像分割与目标检测在叶片病害识别中的应用。","AI可高精度识别病害，但受限于数据集不足、环境变化与模型泛化能力。","农业人工智能与决策模型","可探索多模态数据融合与可解释AI，提升复杂田间环境下病害检测的泛化能力。","2026-09-22T23:30:11.209653Z",{"id":106,"title":107,"url":108,"summary":109,"summary_zh":110,"content":9,"source_name":111,"source_url":108,"published_at":112,"category":12,"cover_url":9,"hotness":113,"is_selected":14,"score":114,"score_detail":115,"sources":119,"tags":123,"search_phrases":125,"slug":128,"view_count":36,"doi":129,"paper":130,"created_at":140},2866,"Upcoming Technologies for Agriculture: Innovations Shaping the Future of Farming","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22816586","Agriculture has always been a cornerstone of human civilization, providing food, raw materials, and employment. However, with the global population projected to reach nearly 10 billion by 2050, the demand for food production is expected to increase substantially (Godfray et al., 2010). Traditional farming methods are increasingly challenged by climate change, resource limitations, and environmental concerns. To address these challenges, upcoming technologies in agriculture promise to revolutionize farming practices by enhancing productivity, sustainability, and resilience. This article reviews key emerging technologies, including precision agriculture, artificial intelligence (AI), gene editing, drone and robotic systems, Internet of Things (IoT) sensors, and sustainable farming innovations. The integration of these technologies is expected to transform agriculture into a more efficient, data-driven, and environmentally friendly sector.","农业一直是人类文明的基石，为人类提供食物、原材料和就业机会。然而，随着全球人口预计到2050年将接近100亿，粮食生产需求预计将大幅增加（Godfray等，2010）。传统耕作方式日益受到气候变化、资源限制和环境问题的挑战。为应对这些挑战，农业领域的新兴技术有望通过提高生产力、可持续性和韧性来彻底变革耕作方式。本文综述了关键新兴技术，包括精准农业、人工智能（AI）、基因编辑、无人机与机器人系统、物联网（IoT）传感器以及可持续农业创新。这些技术的融合有望将农业转变为一个更高效、数据驱动且环境友好的产业。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-17T00:00:00Z",25,68,{"impact":116,"substance":81,"depth":19,"authority":117,"freshness":82,"relevant":22,"comment":118},18,13,"综述性论文系统梳理精准农业、AI、基因编辑等前沿技术，时效性尚可，但缺乏新数据与独家结论，适合作为主题聚合素材而非每日精选头条。",[120,121],{"name":111,"url":108},{"name":111,"url":122},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22816587",[27,28,29,30,124],"基因编辑",[126,127],"精准农业 人工智能 无人机","农业物联网 传感器 机器人","精准农业人工智能无人机-2866","10.5281\u002Fzenodo.22816586",{"doi":129,"openalex_id":131,"authors":132,"venue":111,"cited_by_count":36,"oa_url":108,"card":135,"direction":65,"ingested_from":66},"W7213515489",[133],{"name":134,"orcid":9},"Zorawar Singh",{"tldr":136,"method":137,"finding":138,"direction":65,"opportunity":139},"综述精准农业、AI、基因编辑、无人机、物联网等新兴技术如何重塑未来农业。","文献综述，整合精准农业、AI、基因编辑、无人机、IoT等关键技术。","技术融合将推动农业向高效、数据驱动和环境友好方向转型。","可聚焦多技术集成落地中的成本、数据标准与农户采纳障碍等实证研究空白。","2026-09-18T23:30:14.975692Z",{"id":142,"title":143,"url":144,"summary":145,"summary_zh":146,"content":9,"source_name":111,"source_url":144,"published_at":147,"category":12,"cover_url":9,"hotness":113,"is_selected":14,"score":148,"score_detail":149,"sources":152,"tags":156,"search_phrases":160,"slug":163,"view_count":36,"doi":164,"paper":165,"created_at":175},3357,"AI-Driven Precision Agriculture and Crop Resilience: Integrating Artificial Intelligence, IoT and Remote Sensing for Climate-Resilient Indian Agriculture: A Vision for Viksit Bharat 2047","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914538","Abstract Agriculture is central to India's economic development, food security, rural employment, and the achievement of the Viksit Bharat@2047 vision. However, Indian agriculture faces increasingly complex challenges, including climate variability, water scarcity, soil degradation, pest and disease outbreaks, fragmented landholdings, market uncertainty, and unequal access to agricultural knowledge. These challenges require a transition from conventional, input-intensive agriculture towards data-driven, resource-efficient, climate-resilient and farmer-centric production systems. Agriculture in India is increasingly affected by climate variability, water scarcity, soil degradation, pest and disease outbreaks, and unpredictable weather conditions. These challenges threaten crop productivity and food security, particularly among small and marginal farmers. Artificial Intelligence (AI), Internet of Things (IoT), remote sensing, and machine learning offer new opportunities to transform conventional agricultural practices into data-driven precision agriculture systems. This paper presents a conceptual framework for AI-driven precision agriculture aimed at improving crop resilience under changing climatic conditions. Artificial Intelligence (AI), combined with precision agriculture, Internet of Things (IoT), remote sensing, satellite imagery, drones, machine learning, robotics and digital public infrastructure, offers significant opportunities to transform Indian agriculture. AI can support crop and yield prediction, disease and pest identification, weather-based advisories, irrigation optimisation, soil management, crop insurance, market intelligence and early-warning systems. The paper also discusses challenges related to digital inclusion, data governance, affordability, AI reliability, farmer skills, privacy and institutional coordination. It argues that India's objective should not simply be the digitisation of agriculture, but the creation of an intelligent, inclusive and resilient agricultural ecosystem in which technology augments farmer knowledge and decision-making. By 2047, India can aspire to establish globally competitive agriculture that produces more with fewer resources, withstands climate shocks, generates higher and more stable farm incomes, and ensures sustainable food and nutritional security.","摘要 农业对印度的经济发展、粮食安全、农村就业以及“发达印度@2047”愿景的实现至关重要。然而，印度农业面临日益复杂的挑战，包括气候变异性、水资源短缺、土壤退化、病虫害暴发、土地持有碎片化、市场不确定性以及农业知识获取不平等。这些挑战要求从传统的投入密集型农业向数据驱动、资源高效、气候韧性且以农民为中心的生产体系转型。印度农业日益受到气候变异性、水资源短缺、土壤退化、病虫害暴发及不可预测天气条件的影响。这些挑战威胁着作物生产力和粮食安全，尤其是对小农和边缘农民而言。人工智能（AI）、物联网（IoT）、遥感和机器学习为将传统农业实践转变为数据驱动的精准农业系统提供了新机遇。本文提出了一个AI驱动的精准农业概念框架，旨在改善气候变化条件下作物的韧性。人工智能（AI）与精准农业、物联网（IoT）、遥感、卫星影像、无人机、机器学习、机器人技术及数字公共基础设施相结合，为改造印度农业提供了重大机遇。AI可支持作物与产量预测、病虫害识别、基于天气的农事建议、灌溉优化、土壤管理、作物保险、市场情报及预警系统。本文还讨论了与数字包容、数据治理、可负担性、AI可靠性、农民技能、隐私及机构协调相关的挑战。文章认为，印度的目标不应仅仅是农业数字化，而应是创建一个智能、包容且有韧性的农业生态系统，使技术增强农民的知识与决策能力。到2047年，印度有望建立具有全球竞争力的农业，以更少资源生产更多产品，抵御气候冲击，创造更高且更稳定的农业收入，并确保可持续的粮食与营养安全。","2026-09-30T00:00:00Z",69,{"impact":150,"substance":116,"depth":18,"authority":117,"freshness":36,"relevant":22,"comment":151},22,"概念性框架论文，系统梳理AI、IoT与遥感在印度气候韧性农业中的应用与挑战，有参考价值但无实证数据，且发布日期在未来、时效性缺失，暂不宜进入每日精选。",[153,154],{"name":111,"url":144},{"name":111,"url":155},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22914539",[157,27,28,29,158,159],"数字乡村","气候韧性","遥感监测",[161,162],"印度 精准农业 AI","农业人工智能 农业物联网 数字乡村 智慧农业","印度精准农业AI-3357","10.5281\u002Fzenodo.22914538",{"doi":164,"openalex_id":166,"authors":167,"venue":111,"cited_by_count":36,"oa_url":144,"card":170,"direction":65,"ingested_from":66},"W7214083098",[168],{"name":169,"orcid":9},"Twinkal Prakash Sawant",{"tldr":171,"method":172,"finding":173,"direction":65,"opportunity":174},"提出AI+物联网+遥感驱动的精准农业概念框架，提升印度气候韧性作物生产。","概念框架分析，整合AI、IoT、遥感、卫星、无人机、机器学习与数字公共基础设施。","印度农业应构建智能、包容、有韧性的生态系统，而非仅数字化，以应对气候与资源挑战。","可实证检验小农户场景下AI+IoT+遥感集成对作物韧性与收入的实际效果及数字包容机制。","2026-09-24T23:30:13.211525Z",{"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":184,"score_detail":185,"sources":187,"tags":189,"search_phrases":192,"slug":195,"view_count":36,"doi":196,"paper":197,"created_at":227},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":82,"substance":116,"depth":18,"authority":13,"freshness":13,"relevant":22,"comment":186},"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。",[188],{"name":182,"url":179},[190,27,28,29,191],"数字农业","畜牧养殖",[193,194],"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":196,"openalex_id":198,"authors":199,"venue":182,"cited_by_count":36,"oa_url":179,"card":222,"direction":65,"ingested_from":66},"W7214075234",[200,202,204,206,208,210,213,216,218,220],{"name":201,"orcid":9},"Nicholas Renaldo",{"name":203,"orcid":9},"Sulaiman Musa",{"name":205,"orcid":9},"Jaswar Koto",{"name":207,"orcid":9},"Kristy Veronica",{"name":209,"orcid":9},"Umar Faruq",{"name":211,"orcid":212},"Yulvia Nora Marlim","https:\u002F\u002Forcid.org\u002F0009-0007-8624-5023",{"name":214,"orcid":215},"Rangga Rahmadian Yuliendi","https:\u002F\u002Forcid.org\u002F0000-0003-2288-3580",{"name":217,"orcid":9},"Wilda Susanti",{"name":219,"orcid":9},"Achmad Tavip Junaedi",{"name":221,"orcid":9},"Nabila Wahid",{"tldr":223,"method":224,"finding":225,"direction":65,"opportunity":226},"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。","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":229,"title":230,"url":231,"summary":232,"summary_zh":233,"content":9,"source_name":111,"source_url":231,"published_at":234,"category":12,"cover_url":9,"hotness":113,"is_selected":14,"score":235,"score_detail":236,"sources":240,"tags":244,"search_phrases":247,"slug":250,"view_count":36,"doi":251,"paper":252,"created_at":262},3282,"Design and Implementation of an Ensemble Learning Based Decision Support Model for Crop Selection in Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22895811","Soil composition governs which crop can be grown profitably in a given field, and the relationship between soil variables and crop suitability is nonlinear, interacting and therefore poorly served by heuristic rules. This paper presents SCR-XGB, a five-layer framework that couples a disciplined data-conditioning stage with a regularised gradient-boosted tree ensemble for crop recommendation from soil and climatic parameters. The acquisition layer collects nitrogen, phosphorus and potassium concentration together with temperature, humidity, soil pH and rainfall; the conditioning layer imputes missing values and removes outliers by an interquartile filter; the feature engineering layer derives nutrient ratios, normalises and standardises the numeric fields and encodes the crop label; the ensemble layer fits an additive sequence of regression trees under a regularised objective with shrinkage and column subsampling; and the recommendation layer issues a ranked crop list with per-crop confidence. Four algorithms are specified in full, covering conditioning, feature construction, boosted training and inference, and a complexity analysis is given for each stage. Evaluated on a public corpus of soil and climate records against five baseline learners trained over the identical feature matrix, the proposed framework attains 99.31% accuracy, 100% precision, 99% recall and an F1-score of 99%, ahead of naive Bayes and random forest at 99.09%, support vector machine at 97.95%, logistic regression at 95.22% and a single decision tree at 90.00%. The 9.31 percentage point margin over the single tree, set against the 0.22 point margin over the strongest baseline, quantifies the benefit of boosting and shows where the remaining headroom on this task actually lies.","土壤组成决定了特定田块适宜种植何种作物才能获得经济效益，而土壤变量与作物适宜性之间的关系是非线性的、相互作用的，因此启发式规则难以有效处理这一问题。本文提出SCR-XGB，一个五层框架，将规范化的数据调理阶段与正则化梯度提升树集成相结合，用于基于土壤和气候参数的作物推荐。采集层收集氮、磷、钾浓度以及温度、湿度、土壤pH值和降雨量；调理层通过四分位距滤波器插补缺失值并剔除异常值；特征工程层推导养分比率，对数值字段进行归一化和标准化，并对作物标签进行编码；集成层在带有收缩和列子采样的正则化目标函数下拟合加性回归树序列；推荐层输出带有每种作物置信度的排序作物列表。本文完整给出了四种算法，涵盖调理、特征构建、提升训练和推理，并对每个阶段进行了复杂度分析。在公开的土壤和气候记录语料库上，与在相同特征矩阵上训练的五个基线学习器进行对比评估，所提框架达到了99.31%的准确率、100%的精确率、99%的召回率和99%的F1分数，优于朴素贝叶斯和随机森林的99.09%、支持向量机的97.95%、逻辑回归的95.22%以及单棵决策树的90.00%。相较于单棵决策树9.31个百分点的优势，与相较于最强基线0.22个百分点的优势相比，量化了提升方法的收益，并揭示了该任务上剩余提升空间的实际所在。","2026-09-22T00:00:00Z",71,{"impact":80,"substance":237,"depth":238,"authority":80,"freshness":21,"relevant":22,"comment":239},21,17,"方法完整、对比基线充分，但属常规机器学习应用论文，公共价值有限，可作主题聚合素材而非每日精选。",[241,242],{"name":111,"url":231},{"name":111,"url":243},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22895812",[27,28,30,245,246],"作物推荐","土壤数据",[248,249],"SCR-XGB 作物推荐 土壤","集成学习 精准农业 选种","SCR-XGB作物推荐土壤-3282","10.5281\u002Fzenodo.22895811",{"doi":251,"openalex_id":253,"authors":254,"venue":111,"cited_by_count":36,"oa_url":231,"card":257,"direction":102,"ingested_from":66},"W7214043002",[255],{"name":256,"orcid":9},"Prof. Nagendra Patel Sahil Verma",{"tldr":258,"method":259,"finding":260,"direction":102,"opportunity":261},"提出SCR-XGB五层框架，用梯度提升树集成从土壤和气候参数推荐作物。","基于土壤气候数据，采用正则化梯度提升树集成，含缺失值插补、异常值过滤和特征工程。","模型准确率达99.31%，优于朴素贝叶斯、随机森林等基线，比单决策树提升9.31个百分点。","可探索将模型部署到田间实时决策，并融合遥感与物联网数据提升泛化能力。","2026-09-23T23:30:35.342497Z",{"id":264,"title":265,"url":266,"summary":267,"summary_zh":268,"content":9,"source_name":111,"source_url":266,"published_at":234,"category":12,"cover_url":9,"hotness":113,"is_selected":14,"score":269,"score_detail":270,"sources":272,"tags":276,"search_phrases":278,"slug":281,"view_count":36,"doi":282,"paper":283,"created_at":292},3271,"A Systematic Study of Supervised and Ensemble Learning Approaches for Crop Selection in Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22896343","Selecting the crop best matched to a field’s soil and climate is one of the highest-leverage decisions in agriculture, and one that farmers have traditionally made by intuition or inherited practice. Soil pH together with nitrogen, phosphorus and potassium concentration, and the local temperature, humidity and rainfall regime, jointly determine which crop will flourish and which will fail, and the relationship between those variables and crop performance is neither linear nor independent. Machine learning has therefore become the dominant approach to automated crop recommendation. This paper reviews the field across four technique families — classical supervised learning, ensemble and boosting methods, deep learning and metaheuristic hybrids, and IoT and deployment-oriented systems — and compares twenty-four representative studies published between 2016 and 2026 in terms of method, data source, reported accuracy, advantage and limitation. A generic seven-stage recommendation pipeline is presented and each family is situated within it. The comparison shows that reported accuracy on the standard nutrient-and-climate benchmark has converged in a narrow band between roughly 98 and 99.5 per cent, that boosting and ensemble methods occupy the upper part of that band, and that further gains on the benchmark are no longer the binding constraint on the field. The gaps that remain open are instead the absence of socio-economic and market variables from the decision, the lack of region-specific and long-horizon environmental validation, dataset narrowness and geographic bias, limited interpretability, and the accessibility of these systems to small and resource-poor farmers. These are consolidated into a set of research directions for future work.","选择与田块土壤和气候最匹配的作物是农业中杠杆效应最高的决策之一，而农民传统上依靠直觉或世代相传的经验来做出这一决策。土壤pH值以及氮、磷、钾浓度，加上当地的气温、湿度和降雨状况，共同决定了哪种作物能够茁壮成长、哪种会歉收，而这些变量与作物表现之间的关系既非线性也非相互独立。因此，机器学习已成为自动化作物推荐的主流方法。本文从四个技术族系——经典监督学习、集成与提升方法、深度学习与元启发式混合方法，以及物联网与面向部署的系统——对该领域进行了综述，并从方法、数据来源、报告精度、优势和局限性方面比较了2016年至2026年间发表的二十四项代表性研究。本文提出了一个通用的七阶段推荐流程，并将每个技术族系置于该流程中加以定位。比较结果表明，在标准养分与气候基准上的报告精度已收敛于约98%至99.5%的狭窄区间内，提升与集成方法占据该区间的上端，而在该基准上进一步提升已不再是该领域的约束瓶颈。真正尚未填补的空白在于：决策中缺乏社会经济和市场变量，缺少针对特定区域和长期环境验证，数据集狭窄且存在地理偏差，可解释性有限，以及这些系统对小型和资源匮乏农户的可及性不足。这些空白被归纳为未来工作的一系列研究方向。",78,{"impact":18,"substance":150,"depth":116,"authority":117,"freshness":21,"relevant":22,"comment":271},"系统综述24项研究并指出基准精度已趋饱和，真正瓶颈转向社会经济变量与可解释性，对智慧农业选种方向有参考价值。",[273,274],{"name":111,"url":266},{"name":111,"url":275},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22896344",[27,28,277,30,245],"机器学习",[279,280],"农业人工智能 作物推荐 智慧农业 机器学习","农业人工智能 作物推荐","农业人工智能作物推荐智慧农业机器学习-3271","10.5281\u002Fzenodo.22896343",{"doi":282,"openalex_id":284,"authors":285,"venue":111,"cited_by_count":36,"oa_url":266,"card":287,"direction":65,"ingested_from":66},"W7214002748",[286],{"name":256,"orcid":9},{"tldr":288,"method":289,"finding":290,"direction":102,"opportunity":291},"系统综述2016-2026年24项作物推荐研究，比较四类机器学习方法并指出基准精度已趋饱和。","综述监督学习、集成提升、深度学习与元启发式、物联网部署四类方法及七阶段流程。","标准基准精度收敛于98%-99.5%，提升集成法最优，但精度已非领域瓶颈。","将社会经济与市场变量、区域长期环境验证及可解释性纳入作物推荐，服务小农户。","2026-09-23T23:30:09.369987Z"]