[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1416":3,"related-1416":112},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":12,"hotness":13,"is_selected":14,"embedding_ready":15,"score":16,"sources":17,"tags":19,"primary_topic":24,"secondary_topics":30,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":56,"content":12,"score_detail":57,"classification":65},1416,"THE IMPACT OF DIGITAL TECHNOLOGIES ON THE INNOVATIVE DEVELOPMENT OF AGRICULTURAL ENTERPRISES","https:\u002F\u002Fdoi.org\u002F10.32782\u002Finfrastruct90-32","The article examines the impact of digital technologies on the innovative development of agricultural enterprises in the context of economic digitalization and Industry 4.0. Digitalization is identified as a key driver of production efficiency, resource optimization, labor productivity, and competitiveness. The study considers the application of precision agriculture, the Internet of Things (IoT), artificial intelligence, Big Data, GIS, remote sensing, cloud technologies, and automated management systems. The main benefits include improved decision-making, lower production costs, higher productivity, reduced operational risks, and support for sustainable agricultural development. The paper also identifies barriers to digital transformation, such as limited investment, insufficient digital infrastructure, shortages of qualified personnel, and cybersecurity risks. Practical approaches are proposed to improve digital transformation and strengthen the innovative capacity and long-term competitiveness of agricultural enterprises.","本文探讨了在经济数字化与工业4.0背景下，数字技术对农业企业创新发展的影响。数字化被视为提升生产效率、优化资源配置、提高劳动生产率及增强竞争力的关键驱动力。研究涉及精准农业、物联网（IoT）、人工智能、大数据、地理信息系统（GIS）、遥感、云计算及自动化管理系统的应用。主要益处包括改善决策质量、降低生产成本、提高生产力、减少运营风险以及支持农业可持续发展。本文还指出了数字化转型面临的障碍，如投资有限、数字基础设施不足、专业人才短缺及网络安全风险。文章提出了改进数字化转型、增强农业企业创新能力与长期竞争力的实践路径。","Market Infrastructure","2026-09-01T00:00:00Z","论文",null,10,false,true,51,[18],{"name":9,"url":6},[20,21,22,23],"智慧农业","精准农业","数字化转型","农业企业",{"code":25,"slug":26,"name":27,"path":28,"parent_code":29},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],[32,33],"数字化转型 农业企业 智慧农业 精准农业","数字化转型 农业企业","数字化转型农业企业智慧农业精准农业-1416",4,"10.32782\u002Finfrastruct90-32",{"doi":36,"openalex_id":38,"authors":39,"venue":9,"cited_by_count":47,"oa_url":6,"card":48,"direction":54,"ingested_from":55},"W7204884481",[40,42,45],{"name":41,"orcid":12},"Lidiіa Fedoryshynа",{"name":43,"orcid":44},"Yuliia Aleskerova","https:\u002F\u002Forcid.org\u002F0000-0003-3072-4854",{"name":46,"orcid":12},"Lydmyla Drymanova",0,{"tldr":49,"method":50,"finding":51,"direction":52,"opportunity":53},"探讨数字技术对农业企业创新发展的影响，分析效益与障碍并提出改进路径。","综述数字技术应用，分析效益与障碍，提出实践方案。","数字技术提升效率与竞争力，但受投资、设施、人才和网络安全制约。","农业人工智能与决策模型","可深入研究数字技术采纳的障碍机制，构建农业企业数字化转型的评估与促进模型。","智慧农业 \u002F 农业物联网","openalex","2026-09-02T23:30:15.371042Z",{"impact":58,"substance":59,"depth":60,"authority":61,"freshness":62,"relevant":63,"comment":64},12,15,14,8,2,1,"论文系统梳理数字技术对农业企业创新的影响，但缺乏新数据，时效性低，适合行业参考。",{"status":66,"locked":14,"version":67,"method":68,"evidence":69},"classified","2026-10-22.2","rules",{"candidates":70,"agricultural_evidence":15},[71,78,85,93,100,107],{"code":72,"score":73,"supported":15,"title":74,"abstract":75,"tags":77},"I1",9,[],[21,76],"precision agriculture",[22,20,21],{"code":25,"score":73,"supported":15,"title":79,"abstract":80,"tags":84},[],[81,82,83],"物联网","internet of things","IoT",[],{"code":86,"score":87,"supported":15,"title":88,"abstract":89,"tags":92},"I3",6,[],[90,91],"遥感","remote sensing",[],{"code":94,"score":87,"supported":15,"title":95,"abstract":96,"tags":99},"I4",[],[97,98],"地理信息","GIS",[],{"code":101,"score":102,"supported":15,"title":103,"abstract":104,"tags":106},"I2",3,[],[105],"artificial intelligence",[],{"code":108,"score":63,"supported":14,"title":109,"abstract":110,"tags":111},"G2",[],[],[23],{"total":87,"page":63,"page_size":87,"items":113},[114,167,220,265,306,352],{"id":115,"title":116,"url":117,"summary":118,"summary_zh":119,"source_name":120,"source_url":121,"published_at":122,"category":11,"cover_url":12,"hotness":13,"is_selected":14,"embedding_ready":15,"score":123,"sources":124,"tags":126,"primary_topic":130,"secondary_topics":131,"search_phrases":132,"slug":135,"view_count":47,"doi":136,"paper":137,"created_at":166},5604,"INOVASI DIGITAL PENGELOLAAN HAMA DAN PENYAKIT PADI: IMPLEMENTASI DIGIFARMING PADA KOMUNITAS PETANI MILENIAL WIJA PAGGALUNG, KECAMATAN BARANTI, KABUPATEN SIDRAP","https:\u002F\u002Fdoi.org\u002F10.20956\u002Fhbhbp615","Pengelolaan organisme pengganggu tanaman (OPT) pada budidaya padi masih menghadapi berbagai kendala, terutama karena sebagian besar petani masih mengandalkan pengamatan konvensional yang berpotensi menyebabkan keterlambatan dalam mendeteksi serangan hama dan penyakit. Kondisi tersebut juga ditemukan pada Komunitas Petani Milenial Wija Paggalung di Kecamatan Baranti, Kabupaten Sidenreng Rappang. Kegiatan pengabdian ini bertujuan untuk mengimplementasikan teknologi DiGiFarming sebagai media monitoring kondisi lingkungan berbasis sensor serta meningkatkan kapasitas petani dalam memanfaatkan teknologi digital untuk pengelolaan hama dan penyakit padi. Metode pelaksanaan dilakukan melalui pendekatan partisipatif yang meliputi koordinasi dan identifikasi kebutuhan mitra, sosialisasi dan demonstrasi teknologi, implementasi perangkat DiGiFarming, pendampingan penggunaan sistem, serta evaluasi kegiatan. Hasil kegiatan menunjukkan bahwa teknologi DiGiFarming berhasil diimplementasikan pada lahan mitra dan mampu menyediakan informasi kondisi lingkungan secara real-time melalui dashboard monitoring digital. Kegiatan sosialisasi, demonstrasi, dan pendampingan mendapatkan respons positif dari peserta serta meningkatkan pemahaman petani mengenai konsep pertanian presisi, fungsi sensor lingkungan, dan pemanfaatan data digital dalam mendukung pengambilan keputusan budidaya. Selain itu, peserta menunjukkan peningkatan literasi digital dan ketertarikan terhadap penerapan teknologi pertanian berbasis Internet of Things (IoT). Implementasi DiGiFarming berpotensi menjadi salah satu strategi dalam mendukung transformasi pertanian menuju sistem budidaya yang lebih modern, adaptif, efisien, dan berkelanjutan. Kata kunci: DiGiFarming, Internet of Things (IoT), Organisme pengganggu tanaman, Pertanian presisi. ABSTRACT The management of plant pests and diseases in rice cultivation still faces various challenges, primarily because most farmers still rely on conventional observation methods, which can lead to delays in detecting pest and disease outbreaks. This situation is also evident among the Wija Paggalung Millennial Farmers’ Community in Baranti Subdistrict, Sidenreng Rappang Regency. This community service initiative aims to implement DiGiFarming technology as a sensor-based tool for monitoring environmental conditions and to enhance farmers’ capacity in utilizing digital technology for rice pest and disease management. The implementation method employed a participatory approach, which included coordinating with and identifying partners’ needs, conducting technology outreach and demonstrations, deploying DiGiFarming devices, providing guidance on system usage, and evaluating the activities. The results of the project show that DiGiFarming technology was successfully implemented on partner farms and is capable of providing real-time environmental data through a digital monitoring dashboard. Outreach activities, demonstrations, and mentoring received positive feedback from participants and enhanced farmers’ understanding of precision agriculture concepts, the functions of environmental sensors, and the use of digital data to support crop management decisions. In addition, participants demonstrated improved digital literacy and interest in the application of Internet of Things (IoT)-based agricultural technology. The implementation of DiGiFarming has the potential to become a key strategy in supporting agricultural transformation toward a more modern, adaptive, efficient, and sustainable farming system. Keywords: DiGiFarming, Internet of Things (IoT), plant pests, precision agriculture.","水稻种植中有害生物（OPT）的治理仍面临诸多障碍，主要原因是大多数农民仍依赖传统观察方式，可能导致对病虫害侵袭的检测滞后。这一问题同样存在于西登伦·拉庞县巴兰蒂区的维贾·帕加隆千禧农民社区。本项社会服务活动旨在推广应用DiGiFarming技术，作为基于传感器的环境状况监测媒介，并提升农民利用数字技术进行水稻病虫害治理的能力。实施方法采用参与式 approach，包括协调与识别合作方需求、技术宣传与演示、DiGiFarming设备部署、系统使用指导以及活动评估。活动结果表明，DiGiFarming技术已成功在合作方田块中部署，并能够通过数字监测仪表板实时提供环境状况信息。宣传、演示和指导活动获得了参与者的积极反响，并提升了农民对精准农业概念、环境传感器功能以及利用数字数据支持种植决策的认识。此外，参与者的数字素养有所提高，对基于物联网（IoT）的农业技术应用表现出兴趣。DiGiFarming的推广应用有望成为支持农业转型、迈向更现代化、适应性更强、高效且可持续种植体系的一项策略。关键词：DiGiFarming，物联网（IoT），有害生物，精准农业。","Jurnal Dinamika Pengabdian (JDP)","https:\u002F\u002Fjournal.unhas.ac.id\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F52272","2026-10-21T00:00:00Z",54,[125],{"name":120,"url":121},[127,20,128,21,129],"数字农业","农业物联网","水稻病虫害",{"code":25,"slug":26,"name":27,"path":28,"parent_code":29},[],[133,134],"DiGiFarming 水稻 病虫害监测","Wija Paggalung 农民社区 物联网","DiGiFarming水稻病虫害监测-5604","10.20956\u002Fhbhbp615",{"doi":136,"openalex_id":138,"authors":139,"venue":120,"cited_by_count":47,"oa_url":121,"card":161,"direction":54,"ingested_from":55},"W7223186424",[140,143,145,148,150,152,154,156,159],{"name":141,"orcid":142},"Muhammad Junaid","https:\u002F\u002Forcid.org\u002F0000-0002-5578-1129",{"name":144,"orcid":12},"M Bayu Mario",{"name":146,"orcid":147},"E. Brugman","https:\u002F\u002Forcid.org\u002F0000-0002-6935-6385",{"name":149,"orcid":12},"- Andi Nasruddin",{"name":151,"orcid":12},"Erwin Erwin",{"name":153,"orcid":12},"SATRIANI GASSING",{"name":155,"orcid":12},"Muhammad Wirahadikusuma Azis",{"name":157,"orcid":158},"Wardani Syahrani","https:\u002F\u002Forcid.org\u002F0009-0008-2227-2767",{"name":160,"orcid":12},"Nurfalah Asa’Diyah",{"tldr":162,"method":163,"finding":164,"direction":54,"opportunity":165},"在印尼南苏拉威西稻农社区实施DiGiFarming传感器监测系统，提升农户病虫害数字化管理能力。","参与式社区服务：需求识别、技术示范、IoT传感器部署、使用陪伴与评估。","系统成功实时监测田间环境，农户数字素养与精准农业认知显著提升。","可延伸研究：传感器预警数据与病虫害预测模型耦合，量化其对减损增产的实际效果。","2026-10-22T23:30:26.534879Z",{"id":168,"title":169,"url":170,"summary":171,"summary_zh":172,"source_name":173,"source_url":170,"published_at":122,"category":11,"cover_url":12,"hotness":13,"is_selected":14,"embedding_ready":15,"score":174,"sources":175,"tags":177,"primary_topic":180,"secondary_topics":184,"search_phrases":186,"slug":189,"view_count":47,"doi":190,"paper":191,"created_at":219},5603,"AI-Enabled Smart and Precision Agriculture: Smart Sensors, IoT, Deep Learning, Computer Vision, and Decision Support: A Comprehensive Survey","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202610.2112.v1","Artificial intelligence (AI) is increasingly integrated with smart sensors, Internet of Things (IoT) networks, machine learning, deep learning, and computer vision to support site-specific agricultural management. However, strong performance on computational benchmarks does not necessarily translate into reliable decisions, effective field interventions, or measurable improvements in agricultural productivity and resource efficiency. This comprehensive narrative survey critically synthesizes advances in agricultural sensing, IoT architectures, data processing, multimodal sensor fusion, predictive modeling, computer vision, decision optimization, and automated actuation. Particular emphasis is placed on the relationships between measurement reliability, model generalization, decision feasibility, intervention accuracy, and verified agricultural outcomes. The reviewed literature demonstrates promising capabilities in crop monitoring, yield prediction, disease and weed recognition, precision irrigation, and selected automated operations. Nevertheless, heterogeneous evaluation protocols, limited independent field validation, sensor calibration challenges, distribution shifts, interoperability barriers, and deployment costs constrain operational reliability and generalizability. To address these interconnected challenges, the survey proposes a literature-informed reference architecture linking agricultural observations, data integration, AI inference, constrained decision-making, physical interventions, and outcome feedback, supported by uncertainty assessment, human oversight, and cross-layer assurance. The proposed architecture is conceptual and has not been empirically validated. Future research priorities include reproducible field evaluations, representative agricultural datasets, uncertainty-aware and explainable AI, resource-efficient computing, interoperable infrastructures, and economically inclusive deployment. The review concludes that the practical value of AI-enabled precision agriculture should be evaluated through reliable interventions, sustainable resource utilization, and demonstrated agricultural outcomes rather than predictive performance alone.","人工智能（AI）正日益与智能传感器、物联网（IoT）网络、机器学习、深度学习及计算机视觉相融合，以支撑因地制宜的精准农业管理。然而，在计算基准上的优异表现并不必然转化为可靠的决策、有效的田间干预，或农业生产率与资源效率方面可衡量的提升。本综述性研究系统梳理并批判性综合了农业传感、物联网架构、数据处理、多模态传感器融合、预测建模、计算机视觉、决策优化及自动执行等方面的进展，重点探讨测量可靠性、模型泛化能力、决策可行性、干预精度与经核实的农业成效之间的关系。所综述文献表明，上述技术在作物监测、产量预测、病害与杂草识别、精准灌溉及部分自动化作业中展现出良好前景。然而，评估方案异质、独立田间验证有限、传感器校准难题、分布偏移、互操作性障碍及部署成本等因素制约了运行可靠性与泛化能力。为应对这些相互关联的挑战，本综述提出一种基于文献的参考架构，将农业观测、数据集成、AI推理、受约束决策、物理干预与成效反馈相衔接，并以不确定性评估、人工监督与跨层保障为支撑。该架构为概念性框架，尚未经实证验证。未来研究重点包括可复现的田间评估、具有代表性的农业数据集、不确定性感知与可解释AI、资源高效计算、可互操作基础设施及经济包容性部署。本综述认为，AI赋能精准农业的实际价值应通过可靠的干预措施、可持续的资源利用及经证实的农业成效来评估，而非仅凭预测性能。","Preprints.org",70,[176],{"name":173,"url":170},[20,178,179,81,21],"农业人工智能","计算机视觉",{"code":101,"slug":181,"name":182,"path":183,"parent_code":29},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[185],{"code":25,"slug":26,"name":27,"path":28,"parent_code":29},[187,188],"农业人工智能 计算机视觉 智慧农业 精准农业","农业人工智能 计算机视觉","农业人工智能计算机视觉智慧农业精准农业-5603","10.20944\u002Fpreprints202610.2112.v1",{"doi":190,"openalex_id":192,"authors":193,"venue":173,"cited_by_count":47,"oa_url":170,"card":214,"direction":54,"ingested_from":55},"W7223175252",[194,197,200,203,205,207,210,212],{"name":195,"orcid":196},"IBRAHIM ADABARA","https:\u002F\u002Forcid.org\u002F0000-0001-8949-7540",{"name":198,"orcid":199},"Bashir Olaniyi Sadiq","https:\u002F\u002Forcid.org\u002F0000-0001-7868-4076",{"name":201,"orcid":202},"Aliyu Nuhu Shuaibu","https:\u002F\u002Forcid.org\u002F0000-0002-5171-675X",{"name":204,"orcid":12},"Yale Ibrahim Danjuma",{"name":206,"orcid":12},"Ainamukama Anold",{"name":208,"orcid":209},"Mutebi Joe","https:\u002F\u002Forcid.org\u002F0000-0002-2757-3875",{"name":211,"orcid":12},"Elhadi Abdalla A. Arbab",{"name":213,"orcid":12},"Brian Mugisha",{"tldr":215,"method":216,"finding":217,"direction":54,"opportunity":218},"综述AI赋能精准农业的传感、物联网、深度学习与决策支持，并提出参考架构。","叙述性综述，整合传感器、IoT、多模态融合、计算机视觉与决策优化文献。","基准性能不等于田间可靠决策，评估协议异质、独立验证不足制约落地。","可研究不确定性感知与可解释AI、可复现田间评估及跨层保障架构的实证验证。","2026-10-22T23:30:19.310160Z",{"id":221,"title":222,"url":223,"summary":224,"summary_zh":225,"source_name":226,"source_url":223,"published_at":122,"category":11,"cover_url":12,"hotness":13,"is_selected":14,"embedding_ready":15,"score":227,"sources":228,"tags":230,"primary_topic":232,"secondary_topics":233,"search_phrases":241,"slug":244,"view_count":47,"doi":245,"paper":246,"created_at":264},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)",80,[229],{"name":226,"url":223},[20,178,81,21,231],"气候韧性",{"code":101,"slug":181,"name":182,"path":183,"parent_code":29},[234,235],{"code":25,"slug":26,"name":27,"path":28,"parent_code":29},{"code":236,"slug":237,"name":238,"path":239,"parent_code":240},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[242,243],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":245,"openalex_id":247,"authors":248,"venue":226,"cited_by_count":47,"oa_url":223,"card":259,"direction":54,"ingested_from":55},"W7223121456",[249,252,254,256],{"name":250,"orcid":251},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":253,"orcid":12},"Oluwadare OYEBODE",{"name":255,"orcid":12},"Iretiayo Adelaiye",{"name":257,"orcid":258},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":260,"method":261,"finding":262,"direction":52,"opportunity":263},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z",{"id":266,"title":267,"url":268,"summary":269,"summary_zh":270,"source_name":271,"source_url":268,"published_at":272,"category":11,"cover_url":12,"hotness":13,"is_selected":14,"embedding_ready":15,"score":273,"sources":274,"tags":276,"primary_topic":279,"secondary_topics":280,"search_phrases":282,"slug":285,"view_count":47,"doi":286,"paper":287,"created_at":305},5378,"IoT-Enabled Smart Irrigation Using ESP32 and LoRa for Precision Water Management in Connectivity-Limited Regions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Felectronics15204831","In this paper, a new method for irrigation systems in agriculture has been reported. This work aims to design and implement a smart irrigation system based on the ESP32 platform and Internet of Things (IoT) technologies for optimizing water usage management. With the rapid growth of Internet of Things (IoT) technologies, where devices are connected and can be controlled through the internet, it is now feasible to design smart irrigation systems based on real-time environmental data to deliver optimal water use with minimal human intervention. In Sahara areas, where communication networks and internet connections are lacking, alternative communication protocols are necessary for data transfer. LoRa technology based on the SX1278 transceiver provides long-range, low-power communication that can achieve multi-kilometer ranges (up to 10–15 km in rural areas) under ideal conditions: line-of-sight, high antenna height, low data rate, and open rural environments between distributed sensor nodes and a central gateway. However, this theoretical range cannot be assumed in practice. Field tests under standard farm deployment conditions (ground-level placement and vegetation interference) showed that the link was lost at approximately 120 m, which validates the system at the scale of a field plot. This project used Arduino (version 2.3.7) software to program the NodeMCU (ESP32) board to link the board with the Blynk cloud application. The project is used to monitor the temperature, humidity, and soil moisture and do the command for the irrigation system, with master and slave cards using LoRa technology as a communication tool to replace communication systems like WiFi or cellular networks in deserted or remote areas, which might not offer sufficient coverage or could be too expensive to build. To address this challenge, this paper presents the design and implementation of a smart irrigation system based on the ESP32 microcontroller and LoRa (SX1278) technology, with the long-term objective of helping in the modernization of traditional agriculture and environmental sustainability. Extending the coverage to larger fields and Sahara areas would require elevated antennas, optimized LoRa parameters (spreading factor and bandwidth), multi-hop relay nodes, or multiple gateways; these strategies are proposed as future work.","本文报道了一种农业灌溉系统的新方法。本工作旨在设计和实现一种基于ESP32平台和物联网（Internet of Things, IoT）技术的智能灌溉系统，以优化用水管理。随着物联网技术的快速发展，设备得以互联并可通过互联网进行控制，如今基于实时环境数据设计智能灌溉系统，以在最少人工干预下实现最优用水已成为可能。在撒哈拉地区，由于缺乏通信网络和互联网连接，数据传输需要采用替代通信协议。基于SX1278收发器的LoRa技术可提供远距离、低功耗通信，在理想条件下（视距传输、天线高度较高、低数据速率以及分布式传感器节点与中心网关之间开阔的农村环境），其通信距离可达数公里（农村地区可达10–15 km）。然而，这一理论距离在实际中并不能假定成立。在标准农田部署条件下（地面放置和植被干扰）进行的现场测试表明，链路在约120 m处即发生中断，这验证了系统在田块尺度上的可行性。本项目使用Arduino（版本2.3.7）软件对NodeMCU（ESP32）开发板进行编程，将开发板与Blynk云应用相连接。该项目用于监测温度、湿度和土壤水分，并向灌溉系统发送指令，主从卡采用LoRa技术作为通信工具，以替代WiFi或蜂窝网络等通信系统——这些系统在荒漠或偏远地区可能覆盖不足，或建设成本过高。为应对这一挑战，本文提出了基于ESP32微控制器和LoRa（SX1278）技术的智能灌溉系统的设计与实现，其长期目标是助力传统农业的现代化和环境可持续性。要将覆盖范围扩展至更大田块和撒哈拉地区，需要架高天线、优化LoRa参数（扩频因子和带宽）、采用多跳中继节点或部署多个网关；这些策略被提出作为未来工作。","Electronics","2026-10-19T00:00:00Z",67,[275],{"name":271,"url":268},[20,81,21,277,278],"节水灌溉","LoRa",{"code":25,"slug":26,"name":27,"path":28,"parent_code":29},[281],{"code":236,"slug":237,"name":238,"path":239,"parent_code":240},[283,284],"ESP32 LoRa 智能灌溉","SX1278 精准灌溉","ESP32LoRa智能灌溉-5378","10.3390\u002Felectronics15204831",{"doi":286,"openalex_id":288,"authors":289,"venue":271,"cited_by_count":47,"oa_url":268,"card":300,"direction":54,"ingested_from":55},"W7222959710",[290,292,294,297],{"name":291,"orcid":12},"Hanfoug Salah",{"name":293,"orcid":12},"Riad Saidi",{"name":295,"orcid":296},"Abdelaziz Aouiche","https:\u002F\u002Forcid.org\u002F0000-0003-1378-7941",{"name":298,"orcid":299},"Yazeed Alkhrijah","https:\u002F\u002Forcid.org\u002F0000-0001-7352-003X",{"tldr":301,"method":302,"finding":303,"direction":54,"opportunity":304},"基于ESP32与LoRa构建智能灌溉系统，在无网络覆盖区实现精准用水管理。","ESP32采集温湿度和土壤水分，经LoRa(SX1278)传输至网关并接入Bly","实际农田条件下LoRa链路仅约120米即中断，远低于理论公里级。","可研究多跳中继、天线高度与扩频参数优化，提升偏远农田LoRa组网覆盖与可靠性。","2026-10-20T23:30:08.464791Z",{"id":307,"title":308,"url":309,"summary":310,"summary_zh":311,"source_name":312,"source_url":309,"published_at":272,"category":11,"cover_url":12,"hotness":13,"is_selected":14,"embedding_ready":15,"score":313,"sources":314,"tags":316,"primary_topic":320,"secondary_topics":326,"search_phrases":333,"slug":336,"view_count":63,"doi":337,"paper":338,"created_at":351},5308,"Geopolitics and food security: A comprehensive review of emerging risks and responses","https:\u002F\u002Fdoi.org\u002F10.51200\u002Fjsffs.v2i2.7666","This review paper examines the relationship between geopolitics and food security through a Systematic Literature Review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework. A total of 15 peer-reviewed journal articles published between 2015 and 2026 were reviewed to identify emerging geopolitical risks and responses affecting global food systems. The study synthesizes literature on armed conflicts, trade restrictions, climate change, supply chain disruptions, energy market instability, and technological transformation in agriculture. The findings identified four major themes. First, geopolitical conflicts and protectionist trade policies significantly disrupt global food supply chains and increase food price volatility. Second, climate-related shock such as droughts, floods, and extreme weather events intensifies food insecurity, particularly in import-dependent and developing economies. Third, weaknesses in global logistics and energy dependence expose food systems to external shocks and market instability. Fourth, technological innovations, including smart farming, precision agriculture, and digital monitoring systems, play an increasingly important role in strengthening agricultural productivity, improving resource efficiency, and enhancing resilience against geopolitical and environmental uncertainties. The policy implications of this study include strengthening regional food reserves, diversifying import sources, investing in climate-resilient agriculture, promoting the adoption of smart farming technologies, and enhancing institutional cooperation between governments and international organizations. The relevance of smart farming is increasingly evident, as precision agriculture, Internet of Things (IoT)-based monitoring systems, and data-driven farming technologies play a critical role in improving productivity, reducing resource inefficiency, and enhancing food system resilience under geopolitical stress. The study contributes to a broader understanding of how geopolitical dynamics shape food security and provides recommendations for building sustainable, resilient, and technology-driven food systems in an increasingly uncertain global environment.","本文通过遵循系统评价与荟萃分析首选报告条目（Preferred Reporting Items for Systematic Reviews and Meta-Analyses）框架的系统文献综述，考察地缘政治与粮食安全之间的关系。共 review 了2015年至2026年间发表的15篇同行评审期刊论文，以识别影响全球粮食系统的新兴地缘政治风险与应对措施。本研究综合了关于武装冲突、贸易限制、气候变化、供应链中断、能源市场不稳定以及农业技术转型的文献。研究结果识别出四大主题。第一，地缘政治冲突和保护主义贸易政策显著扰乱全球粮食供应链并加剧粮食价格波动。第二，干旱、洪水和极端天气事件等气候相关冲击加剧粮食不安全，尤其是在依赖进口的经济体和发展中经济体中。第三，全球物流的薄弱环节和能源依赖使粮食系统暴露于外部冲击和市场不稳定之中。第四，包括智慧农业、精准农业和数字监测系统在内的技术创新，在增强农业生产力、提高资源效率以及增强应对地缘政治和环境不确定性的韧性方面发挥着日益重要的作用。本研究的政策启示包括加强区域粮食储备、多元化进口来源、投资气候韧性农业、促进智慧农业技术的采用，以及加强政府与国际组织之间的制度合作。智慧农业的相关性日益明显，因为精准农业、基于物联网（IoT）的监测系统和数据驱动农业技术在提高生产力、减少资源低效以及增强地缘政治压力下粮食系统韧性方面发挥着关键作用。本研究有助于更广泛地理解地缘政治动态如何塑造粮食安全，并为在日益不确定的全球环境中构建可持续、有韧性且技术驱动的粮食系统提供建议。","Journal of Smart Farming and Food Security",77,[315],{"name":312,"url":309},[20,317,318,21,319],"农业供应链","粮食安全","地缘政治",{"code":321,"slug":322,"name":323,"path":324,"parent_code":325},"A8","yield-food-security","单产提升与粮食安全","\u002Ftopic\u002Ffield-crops\u002Fyield-food-security","A",[327,332],{"code":328,"slug":329,"name":330,"path":331,"parent_code":240},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"code":25,"slug":26,"name":27,"path":28,"parent_code":29},[334,335],"地缘政治 粮食安全","智慧农业 粮食系统韧性","地缘政治粮食安全-5308","10.51200\u002Fjsffs.v2i2.7666",{"doi":337,"openalex_id":339,"authors":340,"venue":312,"cited_by_count":47,"oa_url":309,"card":346,"direction":54,"ingested_from":55},"W7222902031",[341,343],{"name":342,"orcid":12},"Parameswaran Mahendran",{"name":344,"orcid":345},"Wong Sing Yun","https:\u002F\u002Forcid.org\u002F0000-0001-5776-8852",{"tldr":347,"method":348,"finding":349,"direction":54,"opportunity":350},"系统综述地缘政治风险对粮食安全的影响及应对，强调智慧农业的韧性作用。","按PRISMA框架系统综述2015-2026年15篇同行评议文献。","冲突、贸易限制、气候与能源冲击威胁粮食安全，智慧农业可提升韧性与效率。","可量化研究物联网与精准农业在地缘政治冲击下对粮食系统韧性的实证贡献。","2026-10-19T23:30:07.850994Z",{"id":353,"title":354,"url":355,"summary":356,"summary_zh":357,"source_name":358,"source_url":355,"published_at":359,"category":11,"cover_url":12,"hotness":13,"is_selected":14,"embedding_ready":15,"score":360,"sources":361,"tags":363,"primary_topic":366,"secondary_topics":372,"search_phrases":374,"slug":377,"view_count":47,"doi":378,"paper":379,"created_at":398},5237,"Integrasi Framework Laravel dan Android pada Sistem IoT untuk Monitoring Parameter Fisika-Kimia di Greenhouse","https:\u002F\u002Fdoi.org\u002F10.36257\u002Fteknika.v20i3.12040","Pengembangan teknologi pertanian presisi (precision agriculture) pada ekosistem greenhouse menggunakan sistem hidroponik menghadapi tantangan teknis dalam menjaga stabilitas parameter lingkungan mikro secara simultan. Penelitian ini bertujuan untuk mengintegrasikan framework Laravel dan platform Android ke dalam sistem Internet of Things (IoT) untuk memantau parameter fisika-kimia lingkungan, meliputi suhu udara, kelembaban, intensitas cahaya, dan kadar karbon dioksida (CO2), pada budidaya pakcoy (Brassica rapa L.) di greenhouse hidroponik. Fokus utama penelitian ini terletak pada perancangan arsitektur backend RESTful API menggunakan Laravel untuk mengelola penyimpanan basis data riwayat jangka panjang serta menyinkronkan transmisi data dari multi-sensor (DHT22, LDR, dan sensor gas CO2) yang memiliki karakteristik sampling rate berbeda. Di sisi pengguna, aplikasi Android dan antarmuka web dikembangkan sebagai frontend yang mengonsumsi data melalui serialisasi format JSON menggunakan metode polling HTTP GET real-time, serta menyediakan tiga mode kendali aktuator, yaitu manual, otomatis berbasis ambang batas sensor, dan penjadwalan. Pengujian fungsionalitas dan kinerja sistem dilakukan melalui visualisasi antarmuka pada dasbor situs web neliti-sk.darmajaya.ac.id serta evaluasi transmisi data nirkabel. Infrastruktur backend berbasis Laravel terbukti stabil dalam menangani fluktuasi frekuensi data sensor yang kompleks. Berdasarkan evaluasi log data selama 24 jam pengujian kontinu pada rentang 14 Agustus 2026 (00.00–23.00 WIB), sistem berhasil merekam seluruh 24 titik data per jam (100%) tanpa satupun jam yang terlewat. Integrasi ini menghadirkan solusi pemantauan sekaligus pengendalian pertanian presisi yang andal, terintegrasi, dan memiliki skalabilitas tinggi bagi operator greenhouse, meskipun masih memerlukan mekanisme offline logging untuk mengantisipasi gangguan konektivitas atau pemadaman listrik di masa mendatang.","在温室生态系统中利用水培系统开发精准农业（precision agriculture）技术，面临着同时维持微环境参数稳定性的技术挑战。本研究旨在将Laravel框架和Android平台集成到物联网（IoT）系统中，以监测水培温室中小白菜（Brassica rapa L.）栽培环境的物理化学参数，包括空气温度、湿度、光照强度和二氧化碳（CO2）浓度。本研究的主要重点在于设计基于Laravel的RESTful API后端架构，以管理长期历史数据库的存储，并同步具有不同采样率特征的多传感器（DHT22、LDR和CO2气体传感器）的数据传输。在用户端，开发了Android应用和Web界面作为前端，通过HTTP GET实时轮询方法以JSON格式序列化消费数据，并提供三种执行器控制模式，即手动、基于传感器阈值的自动控制和定时调度。系统功能与性能测试通过neliti-sk.darmajaya.ac.id网站仪表板的界面可视化以及无线数据传输评估进行。基于Laravel的后端基础设施在处理复杂的传感器数据频率波动方面表现出稳定性。根据2026年8月14日（00:00–23:00 WIB）连续24小时测试期间的数据日志评估，系统成功记录了每小时全部24个数据点（100%），无一小时遗漏。该集成提供了一种可靠、集成且具有高可扩展性的精准农业监测与控制解决方案，适用于温室操作人员，但未来仍需离线日志记录机制以应对连接中断或停电情况。","TEKNIKA","2026-10-17T00:00:00Z",56,[362],{"name":358,"url":355},[20,81,21,364,365],"温室大棚","水培蔬菜",{"code":367,"slug":368,"name":369,"path":370,"parent_code":371},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[373],{"code":25,"slug":26,"name":27,"path":28,"parent_code":29},[375,376],"Laravel Android 温室物联网","小白菜 水培 温室监测","LaravelAndroid温室物联网-5237","10.36257\u002Fteknika.v20i3.12040",{"doi":378,"openalex_id":380,"authors":381,"venue":358,"cited_by_count":47,"oa_url":392,"card":393,"direction":54,"ingested_from":55},"W7222850582",[382,384,387,390],{"name":383,"orcid":12},"Ramadanzah Ramadanzah",{"name":385,"orcid":386},"Dodi Yudo Setyawan","https:\u002F\u002Forcid.org\u002F0000-0003-2205-7617",{"name":388,"orcid":389},"Nurfiana Nurfiana","https:\u002F\u002Forcid.org\u002F0000-0002-6149-0918",{"name":391,"orcid":12},"Refi Arioen","https:\u002F\u002Fjurnal.polsri.ac.id\u002Findex.php\u002Fteknika\u002Farticle\u002Fdownload\u002F12040\u002F4368",{"tldr":394,"method":395,"finding":396,"direction":54,"opportunity":397},"集成Laravel与Android的IoT系统，实时监测温室水培小白菜的温湿度、光照和CO2。","用Laravel构建RESTful API后端，Android与Web前端通过J","后端稳定处理异构采样率数据，24小时连续测试每小时数据完整记录率100%。","可研究断网或断电下的离线缓存与数据补传机制，提升温室IoT系统鲁棒性。","2026-10-18T23:30:07.964809Z"]