[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5381":3,"related-5381":118},{"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":32,"secondary_topics":38,"classification":45,"search_phrases":99,"slug":102,"view_count":103,"doi":104,"paper":105,"created_at":117},5381,"Climate-Smart Floriculture: Strategies for Sustainable Flower Production Under a Changing Climate","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23467834","Floriculture is a high-value horticultural sector comprising cut flowers, potted flowering plants, foliage plants, ornamental bulbs and nursery plants. The sector is increasingly exposed to climate variability through rising temperature, drought, irregular rainfall, extreme weather events, elevated atmospheric CO₂ and changing pest and disease pressure. These stresses can influence vegetative growth, flowering, flower quality, production scheduling and postharvest performance, thereby affecting the economic value of ornamental crops. Climate-smart floriculture provides an integrated framework for improving resilience while maintaining productivity and resource-use efficiency. Key strategies include climate-resilient cultivar selection, efficient irrigation, soil-moisture conservation, protected cultivation, microclimate regulation, integrated pest management and precision technologies. Genetic improvement offers a long-term approach for developing cultivars capable of maintaining desirable ornamental traits under environmental stress, whereas protected cultivation can buffer crops against external climatic fluctuations. Efficient irrigation, rainwater harvesting and improved soil management can reduce vulnerability to water scarcity, while integrated pest management can address climate-associated changes in pest and disease pressure. Precision agriculture, controlled-environment agriculture and machine-learning-based decision systems offer additional opportunities for real-time monitoring and resource optimisation. However, technological adoption is influenced by investment costs, knowledge availability, extension services, market access and supportive policies. This article discusses the major climate-related risks to floriculture and evaluates integrated strategies for developing productive, resource-efficient and climate-resilient flower production systems.","花卉产业是一个高附加值的园艺部门，包括切花、盆栽开花植物、观叶植物、观赏球根植物和苗木。该产业日益受到气候变率的威胁，表现为气温升高、干旱、降雨不规律、极端天气事件、大气CO₂浓度升高以及病虫害压力变化。这些胁迫可影响营养生长、开花、花卉品质、生产调度和采后性能，进而影响观赏作物的经济价值。气候智慧型花卉产业提供了一个综合框架，可在保持生产力和资源利用效率的同时提高韧性。关键策略包括气候韧性品种选择、高效灌溉、土壤水分保持、设施栽培、微气候调控、病虫害综合治理和精准技术。遗传改良为培育能够在环境胁迫下保持理想观赏性状的品种提供了长期途径，而设施栽培可缓冲作物免受外部气候波动的影响。高效灌溉、雨水收集和改善的土壤管理可降低对水资源短缺的脆弱性，而病虫害综合治理可应对气候相关的病虫害压力变化。精准农业、受控环境农业和基于机器学习的决策系统为实时监测和资源优化提供了额外机会。然而，技术采用受到投资成本、知识可得性、推广服务、市场准入和支持性政策的影响。本文讨论了花卉产业面临的主要气候相关风险，并评估了发展高产、资源高效和气候韧性花卉生产系统的综合策略。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-10-19T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,20,17,13,9,1,"系统综述花卉业气候风险与综合应对策略，具行业参考价值，但属综述性论文、非突破性成果。",[25],{"name":10,"url":6},[27,28,29,30,31],"精准农业","病虫害防控","气候智慧农业","花卉产业","设施园艺",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"B4","flowers","花卉与观赏植物","\u002Ftopic\u002Fhorticulture\u002Fflowers","B",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":52},[51,59,65,73,79,87,94],{"code":33,"score":17,"supported":52,"title":53,"abstract":55,"tags":58},true,[54],"floriculture",[56,57,54],"花卉","ornamental",[30],{"code":40,"score":21,"supported":52,"title":60,"abstract":61,"tags":64},[],[62,63,41],"灌溉","水资源",[],{"code":66,"score":67,"supported":52,"title":68,"abstract":69,"tags":72},"H7",7,[],[70,71],"干旱","drought",[29],{"code":74,"score":67,"supported":52,"title":75,"abstract":76,"tags":78},"I1",[],[27,77],"precision agriculture",[27],{"code":80,"score":81,"supported":52,"title":82,"abstract":83,"tags":86},"H2",6,[],[84,85],"土壤","soil",[],{"code":88,"score":89,"supported":52,"title":90,"abstract":91,"tags":93},"I2",3,[],[92],"机器学习",[],{"code":95,"score":22,"supported":14,"title":96,"abstract":97,"tags":98},"B7",[],[],[31],[100,101],"花卉 可持续生产 气候变化","气候智慧农业 病虫害防控 精准农业 花卉产业","花卉可持续生产气候变化-5381",0,"10.5281\u002Fzenodo.23467834",{"doi":104,"openalex_id":106,"authors":107,"venue":10,"cited_by_count":103,"oa_url":6,"card":110,"direction":114,"ingested_from":116},"W7222973900",[108],{"name":109,"orcid":9},"Shriya Gupta1 and Harish Kumar2",{"tldr":111,"method":112,"finding":113,"direction":114,"opportunity":115},"综述气候智慧型花卉生产策略，评估气候变化风险与综合适应措施。","文献综述，整合抗逆品种、灌溉、保护地栽培、IPM与精准技术。","综合策略可提升花卉气候韧性，但技术采纳受成本、知识与政策制约。","智慧农业 \u002F 农业物联网","花卉领域精准农业与机器学习决策应用少，可研究低成本智能环境调控与品种-技术协同。","openalex","2026-10-20T23:30:08.681802Z",{"total":81,"page":22,"page_size":81,"items":119},[120,227,293,334,388,454],{"id":121,"title":122,"url":123,"summary":124,"summary_zh":125,"content":9,"source_name":126,"source_url":123,"published_at":127,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":128,"score_detail":129,"sources":132,"tags":134,"primary_topic":139,"secondary_topics":144,"classification":151,"search_phrases":202,"slug":205,"view_count":103,"doi":206,"paper":207,"created_at":226},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)","2026-10-21T00:00:00Z",80,{"impact":17,"substance":130,"depth":17,"authority":20,"freshness":21,"relevant":22,"comment":131},22,"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[133],{"name":126,"url":123},[135,136,137,27,138],"智慧农业","农业人工智能","物联网","气候韧性",{"code":88,"slug":140,"name":141,"path":142,"parent_code":143},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[145,150],{"code":146,"slug":147,"name":148,"path":149,"parent_code":143},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":152},{"candidates":153,"agricultural_evidence":52},[154,164,169,176,180,186,190,196],{"code":88,"score":17,"supported":52,"title":155,"abstract":157,"tags":163},[156],"artificial intelligence",[158,92,159,160,161,162],"深度学习","计算机视觉","machine learning","deep learning","computer vision",[136],{"code":74,"score":165,"supported":52,"title":166,"abstract":167,"tags":168},16,[77],[27,77],[135,27],{"code":146,"score":13,"supported":52,"title":170,"abstract":171,"tags":175},[],[137,172,173,174],"边缘计算","IoT","edge computing",[137],{"code":40,"score":21,"supported":52,"title":177,"abstract":178,"tags":179},[],[62,63,41],[],{"code":66,"score":67,"supported":52,"title":181,"abstract":182,"tags":185},[],[183,184],"气候变化","climate change",[138],{"code":80,"score":81,"supported":52,"title":187,"abstract":188,"tags":189},[],[84,85],[],{"code":191,"score":89,"supported":52,"title":192,"abstract":193,"tags":195},"A7",[],[194],"间作",[],{"code":197,"score":89,"supported":52,"title":198,"abstract":199,"tags":201},"F3",[],[200],"养分管理",[],[203,204],"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":206,"openalex_id":208,"authors":209,"venue":126,"cited_by_count":103,"oa_url":123,"card":220,"direction":114,"ingested_from":116},"W7223121456",[210,213,215,217],{"name":211,"orcid":212},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":214,"orcid":9},"Oluwadare OYEBODE",{"name":216,"orcid":9},"Iretiayo Adelaiye",{"name":218,"orcid":219},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":221,"method":222,"finding":223,"direction":224,"opportunity":225},"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。","农业人工智能与决策模型","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":228,"title":229,"url":230,"summary":231,"summary_zh":232,"content":9,"source_name":233,"source_url":230,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":234,"score_detail":235,"sources":239,"tags":241,"primary_topic":244,"secondary_topics":245,"classification":247,"search_phrases":269,"slug":272,"view_count":103,"doi":273,"paper":274,"created_at":292},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",67,{"impact":236,"substance":17,"depth":165,"authority":20,"freshness":237,"relevant":22,"comment":238},12,8,"面向无网络覆盖地区的ESP32+LoRa智能灌溉系统，含实地链路测试数据，方法有参考价值但属细分技术进展，未达每日精选门槛。",[240],{"name":233,"url":230},[135,137,27,242,243],"节水灌溉","LoRa",{"code":146,"slug":147,"name":148,"path":149,"parent_code":143},[246],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":248},{"candidates":249,"agricultural_evidence":52},[250,255,260,264],{"code":146,"score":17,"supported":52,"title":251,"abstract":252,"tags":254},[173],[137,253,173],"internet of things",[137],{"code":40,"score":256,"supported":52,"title":257,"abstract":258,"tags":259},15,[41],[62,41],[242],{"code":80,"score":81,"supported":52,"title":261,"abstract":262,"tags":263},[],[84,85],[],{"code":74,"score":265,"supported":14,"title":266,"abstract":267,"tags":268},2,[],[],[135,27],[270,271],"ESP32 LoRa 智能灌溉","SX1278 精准灌溉","ESP32LoRa智能灌溉-5378","10.3390\u002Felectronics15204831",{"doi":273,"openalex_id":275,"authors":276,"venue":233,"cited_by_count":103,"oa_url":230,"card":287,"direction":114,"ingested_from":116},"W7222959710",[277,279,281,284],{"name":278,"orcid":9},"Hanfoug Salah",{"name":280,"orcid":9},"Riad Saidi",{"name":282,"orcid":283},"Abdelaziz Aouiche","https:\u002F\u002Forcid.org\u002F0000-0003-1378-7941",{"name":285,"orcid":286},"Yazeed Alkhrijah","https:\u002F\u002Forcid.org\u002F0000-0001-7352-003X",{"tldr":288,"method":289,"finding":290,"direction":114,"opportunity":291},"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":294,"title":295,"url":296,"summary":297,"summary_zh":9,"content":298,"source_name":299,"source_url":9,"published_at":300,"category":301,"cover_url":9,"hotness":13,"is_selected":14,"score":302,"score_detail":303,"sources":306,"tags":308,"primary_topic":313,"secondary_topics":314,"classification":315,"search_phrases":329,"slug":332,"view_count":103,"doi":9,"paper":9,"created_at":333},5355,"云南农科院花卉所自育长征主题月季三款新品种首次公开亮相","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7696362334037590528","2026 年是中国工农红军长征胜利 90 周年,云南省农业科学院花卉研究所自主培育的长征主题月季在总台央视《江山万里新长征》节目中首次亮相。第一款金沙万里金色如革命星火熠熠生辉,寓意红军巧渡金沙江;第二款石鼓春江花开之时追忆江畔烽火岁月;第三款云崖暖取自金沙水拍云崖暖。云南农科院计划最快在 2027 年 7 月至 8 月向种植户提供主题月季种苗,明年年底有望上市。","## 云花“金沙万里”“石鼓春江”“云崖暖”亮相！\n\n2026-10-14 11:37·[蒙自发布](https:\u002F\u002Fwww.toutiao.com\u002Fc\u002Fuser\u002Ftoken\u002FMS4wLjABAAAASV-_T0D7FJqyV-1UFbuR6NHj-Hh-R4cc-XyrHfqH0VM\u002F?source=tuwen_detail)\n\n2026年，是中国工农红军长征胜利90周年。金秋时节，繁花盛放。一批来自彩云之南、承载红色底蕴的国产月季新品种，在总台央视《江山万里新长征》节目中首次亮相，以鲜花致敬长征精神，以匠心礼赞山河盛世。\n\n在位于云南昆明的斗南花卉市场，由云南省农业科学院花卉研究所自主培育的“长征主题月季”依次排开。\n\n第一款“金沙万里”，“金沙”即金沙江，“万里”既指长征万里，也指金沙江奔流万里，寓意红军巧渡金沙江。金色如革命星火熠熠生辉。\n\n![Image 1](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-axegupay5k\u002Fefb26be552a142fdaa56092ba3443174~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1793069485&x-signature=OjWNa3Sp%2BJjrTDeclCB%2BfhS71v4%3D)\n\n第二款“石鼓春江”，“石鼓”为长江第一湾古渡口。花开之时，追忆江畔烽火岁月，致敬波澜壮阔的峥嵘往事。\n\n![Image 2](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-6w9my0ksvp\u002Fffe0af747ae440f6b085be40adc0c70b~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1793069485&x-signature=KWab5XWy6PZd8e5TsDOpRSMTwGw%3D)\n\n第三款“云崖暖”，取自“金沙水拍云崖暖”，以花朵承载诗词里的革命豪情，花开见诗，以花传志。\n\n![Image 3](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-6w9my0ksvp\u002Ff191765ac56d47518374316b3864dc0c~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1793069485&x-signature=qAg5ywqyHiU2YfS12HT2e2siWoI%3D)\n\n这是“长征主题月季”首次公开亮相。据了解，云南省农科院计划最快在2027年7月至8月向种植户提供主题月季种苗，明年年底有望上市。\n\n这些惊艳绽放的云花新品背后，是久久为功的坚守。云南花卉科研团队经过长期不懈的努力和无数次试验，攻克种源技术难关，解决了品种适应性弱、花期短、观赏性不足等诸多技术难题。\n\n忆往昔，红军不畏艰险长征路，把命运握在自己手里；看今朝，云南把花卉种业的主动权牢牢握在自己手中，走出一条科研攻关的新长征路。\n\n![Image 4](https:\u002F\u002Fp3-sign.toutiaoimg.com\u002Ftos-cn-i-6w9my0ksvp\u002F3b9cf5e9b7e4469b8f17acb909f13dbc~tplv-tt-origin-web:gif.jpeg?_iz=58558&from=article.pc_detail&lk3s=953192f4&x-expires=1793069485&x-signature=iwO4Zl%2FULtsfBEYVLVSMoY5ATdA%3D)\n\n从左到右依次为“宝华”月季、“文秀”月季、“娇龙”月季。\n\n从昆明斗南出发，这些带着“中国芯”的花卉，如今已在各地开花。从依赖进口到自主育种，云南花卉产业的每一次突破，都是国产花卉自主创新这条长征路上的坚实足迹。\n\n**以鲜花致敬英雄**\n\n**致敬的是峥嵘岁月**\n\n**更是这份把命运**\n\n**把握在自己手里的底气与信念**\n\n* * *\n\n来源：云南发布、总台央视、云南广播电视台\n\n编辑：李倩（实习）、肖金兰","今日头条(云南发布) 2026-10-14","2026-10-14T00:00:00Z","报道",61,{"impact":17,"substance":165,"depth":236,"authority":13,"freshness":304,"relevant":22,"comment":305},5,"省级科研机构自主培育的长征主题月季新品种首次亮相，兼具种业自主创新与红色文化价值，但时效略滞后、产业影响限于花卉细分领域。",[307],{"name":299,"url":296},[309,30,310,311,312],"种业振兴","月季育种","自主品种","云南花卉",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":316},{"candidates":317,"agricultural_evidence":52},[318,324],{"code":33,"score":319,"supported":52,"title":320,"abstract":322,"tags":323},23,[56,321],"月季",[56,321],[30],{"code":325,"score":22,"supported":14,"title":326,"abstract":327,"tags":328},"F1",[],[],[309],[330,331],"云南农科院 花卉所 长征主题月季","金沙万里 石鼓春江 云崖暖 月季","云南农科院花卉所长征主题月季-5355","2026-10-20T02:47:44.766519Z",{"id":335,"title":336,"url":337,"summary":338,"summary_zh":9,"content":339,"source_name":340,"source_url":9,"published_at":341,"category":301,"cover_url":9,"hotness":342,"is_selected":14,"score":343,"score_detail":344,"sources":347,"tags":352,"primary_topic":357,"secondary_topics":358,"classification":365,"search_phrases":383,"slug":386,"view_count":22,"doi":9,"paper":9,"created_at":387},5050,"河北省农林科学院棉花所主持制定的团标《切花月季基质栽培生产技术规程》通过专家审定","http:\u002F\u002Fhebnky.com\u002Fxueshuhuodongxinxi.aspx?xshdid=1474","10月9日,河北省林木种苗协会组织专家对河北省农林科学院棉花所主持起草的团体标准《切花月季基质栽培生产技术规程》(送审稿)进行审查。编制小组就规程编制的背景、技术内容、指标依据及征求意见处理情况进行汇报,与会专家一致同意通过审查,认为标准贴合河北切花月季生产实际,具备较强指导性和实用性,将为河北省花卉产业绿色高质量发展提供技术支撑。","2026年10月9日，河北省林木种苗协会邀请河北农业大学、河北省林业和草原科学研究院、河北省林草种苗与经济林花卉中心、河北林业生态建设投资有限公司、石家庄市风景园林学会等单位的专家，组成标准审定委员会，对河北省林木种苗协会提出、棉花所主持起草的团体标准《切花月季基质栽培生产技术规程》（送审稿）进行了审查。\n\n谷瑞丰博士代表编制小组汇报了编制工作，详细介绍了规程编制的背景、技术内容、指标依据以及征求意见的处理情况等核心内容。与会专家对标准编制说明及送审稿全文进行逐条审阅，依据标准编制的相关规定和审查原则，提出了专业修改建议。经专家充分的讨论和综合评议，一致认为，标准编制依据充分，技术成熟先进，条文内容科学规范，贴合我省切花月季生产实际，具备较强的指导性和实用性，一致同意该送审稿通过审查。建议编制组严格依据审查意见，按时完成标准内容的完善工作，及时报送标准提出单位发布实施。\n\n本标准的实施，将规范我省切花月季基质栽培生产，提高切花产量和品质，为河北省花卉产业绿色高质量发展提供技术支撑。","河北省农林科学院 棉花研究所 2026年10月12日","2026-10-12T06:08:00Z",25,66,{"impact":165,"substance":17,"depth":345,"authority":236,"freshness":81,"relevant":22,"comment":346},14,"省级团体标准通过审定，属细分行业实质进展，但影响范围限于河北花卉产业，时效性一般，可作备选。",[348,349],{"name":340,"url":337},{"name":350,"url":351},"河北省农林科学院 2026-10-12","http:\u002F\u002Fhebnky.com\u002FXueshuhuodong.aspx",[353,354,355,30,356],"行业标准","河北省农科院","切花月季","基质栽培",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[359],{"code":360,"slug":361,"name":362,"path":363,"parent_code":364},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",{"status":46,"locked":14,"version":47,"method":48,"evidence":366},{"candidates":367,"agricultural_evidence":52},[368,372,378],{"code":33,"score":256,"supported":52,"title":369,"abstract":370,"tags":371},[321],[56,321],[30],{"code":360,"score":373,"supported":52,"title":374,"abstract":376,"tags":377},11,[375],"棉花",[375],[],{"code":379,"score":22,"supported":14,"title":380,"abstract":381,"tags":382},"J1",[],[],[353],[384,385],"河北农科院 切花月季 基质栽培","切花月季 生产技术规程 团体标准","河北农科院切花月季基质栽培-5050","2026-10-16T00:13:11.953840Z",{"id":389,"title":390,"url":391,"summary":392,"summary_zh":393,"content":9,"source_name":394,"source_url":395,"published_at":396,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":397,"score_detail":398,"sources":401,"tags":403,"primary_topic":407,"secondary_topics":408,"classification":409,"search_phrases":430,"slug":433,"view_count":22,"doi":434,"paper":435,"created_at":453},4913,"Factors Influencing the Adoption of Precision Agriculture Technologies by Nebraska Producers","https:\u002F\u002Fdoi.org\u002F10.22004\u002Fag.econ.413436","An ever-increasing world population and increasingly-volatile commodity prices have charged producers with the task of becoming more efficient. To combat this, precision agriculture technologies aimed at increasing production efficiency are continually being developed, but their adoption is not yet widespread. A survey regarding the usage of these technologies was distributed to a sample of row crop producers across the state of Nebraska and a Poisson regression was used to determine the factors influencing adoption. Results of the study indicate that larger, more tech-savvy producers and those using irrigation are more likely to adopt a higher number of precision agriculture technologies, while operator age and gross farm income were found to be noninfluential factors.","世界人口不断增长以及大宗商品价格日益波动，使生产者肩负起提高效率的任务。为应对这一挑战，旨在提高生产效率的精准农业（precision agriculture）技术不断被开发出来，但其采用尚未普及。本研究向内布拉斯加州大田作物生产者样本发放了关于这些技术使用情况的调查问卷，并采用泊松回归（Poisson regression）确定影响采用的因素。研究结果表明，规模更大、更精通技术的生产者以及使用灌溉的生产者更可能采用更多数量的精准农业技术，而经营者年龄和农场总收入则被发现在影响因素中不起作用。","Lincoln (University of Nebraska)","https:\u002F\u002Fdigitalcommons.unl.edu\u002Fageconworkpap\u002F49","2026-10-13T00:00:00Z",75,{"impact":165,"substance":399,"depth":19,"authority":20,"freshness":237,"relevant":22,"comment":400},21,"基于内布拉斯加州种植者调查的实证研究，用泊松回归揭示规模、技术素养与灌溉对精准农业技术采纳的影响，对国内智慧农业推广有参考价值。",[402],{"name":394,"url":395},[135,27,404,405,406],"农业技术采纳","灌溉农业","玉米大豆",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":410},{"candidates":411,"agricultural_evidence":52},[412,416,420,425],{"code":74,"score":165,"supported":52,"title":413,"abstract":414,"tags":415},[77],[27,77],[135,27],{"code":40,"score":67,"supported":52,"title":417,"abstract":418,"tags":419},[],[62,41],[405],{"code":421,"score":22,"supported":14,"title":422,"abstract":423,"tags":424},"A3",[],[],[406],{"code":426,"score":22,"supported":14,"title":427,"abstract":428,"tags":429},"G1",[],[],[404],[431,432],"内布拉斯加 精准农业 采纳","精准农业技术 采纳因素","内布拉斯加精准农业采纳-4913","10.22004\u002Fag.econ.413436",{"doi":434,"openalex_id":436,"authors":437,"venue":394,"cited_by_count":445,"oa_url":446,"card":447,"direction":224,"ingested_from":116},"W2326051080",[438,440,442],{"name":439,"orcid":9},"Michael H Castle",{"name":441,"orcid":9},"Bradley Lubben",{"name":443,"orcid":444},"Joe D. Luck","https:\u002F\u002Forcid.org\u002F0000-0002-8278-9812",32,"http:\u002F\u002Fdigitalcommons.unl.edu\u002Fcgi\u002Fviewcontent.cgi?article=1050&context=ageconworkpap",{"tldr":448,"method":449,"finding":450,"direction":451,"opportunity":452},"基于内布拉斯加州调查，用泊松回归分析影响精准农业技术采纳的因素。","对行作物生产者问卷调查，采用泊松回归模型分析采纳数量。","规模大、技术熟练及有灌溉的生产者更易采纳更多精准农业技术，年龄和收入无显著影响。","数字乡村与农业信息化","可探究小规模或非灌溉农户采纳障碍，以及技术熟练度的中介机制与干预策略。","2026-10-14T23:30:55.751323Z",{"id":455,"title":456,"url":457,"summary":458,"summary_zh":459,"content":9,"source_name":460,"source_url":457,"published_at":461,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":462,"score_detail":463,"sources":465,"tags":467,"primary_topic":470,"secondary_topics":471,"classification":473,"search_phrases":504,"slug":507,"view_count":103,"doi":508,"paper":509,"created_at":520},4801,"CROPINTEL DATASET: A COMPREHENSIVE AGROENVIRONMENTAL RESOURCE FOR CROP CLASSIFICATION, IRRIGATION PLANNING, AND YIELD ANALYSIS","https:\u002F\u002Fdoi.org\u002F10.30546\u002F09090.2025.210.005","The growing demand for sustainable agricultural intensification necessitates advanced tools for optimizing crop selection and resource management.This study presents an integrated analytical framework using machine learning to derive agro-environmental intelligence from the comprehensive CropIntel dataset.We evaluated a suite of over fifteen machine learning algorithms for three critical precision agriculture tasks: crop classification, seasonal irrigation requirement prediction, and yield potential forecasting.The results demonstrate the exceptional capability of tree-based models in this domain.Notably, Decision Tree, Bagging, and Gaussian Naïve Bayes classifiers achieved near-perfect accuracy (Acc≈99.7%) in identifying suitable crops based on climatic and edaphic conditions.For regression tasks, LightGBM and Histogram Gradient Boosting models proved most effective, explaining approximately 84% of the variance in irrigation needs (R²≈0.84)and about 70% of the variance in yield potential (R²≈0.696).These findings underscore the potential of machine learning to create powerful decision support systems that can guide crop selection, optimize water allocation, and provide reliable yield forecasts.This research contributes to a holistic, data-driven methodology that can enhance the efficiency and sustainability of agricultural practices.","对可持续农业集约化的需求日益增长，亟需先进工具来优化作物选择与资源管理。本研究提出一个集成的分析框架，利用机器学习从综合性的CropIntel数据集中提取农业环境智能信息。我们针对三个关键精准农业任务评估了超过十五种机器学习算法：作物分类、季节性灌溉需求预测和产量潜力预报。结果表明，基于树的模型在该领域具有卓越能力。值得注意的是，决策树、装袋法和高斯朴素贝叶斯分类器在基于气候和土壤条件识别适宜作物方面达到了近乎完美的准确率（Acc≈99.7%）。在回归任务中，LightGBM和直方图梯度提升模型最为有效，分别解释了约84%的灌溉需求方差（R²≈0.84）和约70%的产量潜力方差（R²≈0.696）。这些发现凸显了机器学习在构建强大决策支持系统方面的潜力，可指导作物选择、优化水资源分配并提供可靠的产量预报。本研究为一种整体性、数据驱动的方法论做出了贡献，有助于提升农业实践的效率与可持续性。","JOURNAL OF BAKU ENGINEERING UNIVERSITY- MATHEMATICS AND COMPUTER SCIENCE","2026-10-12T00:00:00Z",72,{"impact":236,"substance":399,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":464},"构建CropIntel农业环境数据集并系统评测15种以上机器学习算法，在作物分类、灌溉需求与产量预测上给出可复现结论，方法扎实但属细分领域学术进展，产业影响有限。",[466],{"name":460,"url":457},[135,136,468,27,469],"产量预测","灌溉决策",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[472],{"code":88,"slug":140,"name":141,"path":142,"parent_code":143},{"status":46,"locked":14,"version":47,"method":48,"evidence":474},{"candidates":475,"agricultural_evidence":52},[476,480,485,489,493,499],{"code":40,"score":17,"supported":52,"title":477,"abstract":478,"tags":479},[41],[62,63,41],[469],{"code":88,"score":13,"supported":52,"title":481,"abstract":482,"tags":484},[],[92,160,483],"decision support",[136],{"code":74,"score":237,"supported":52,"title":486,"abstract":487,"tags":488},[],[27,77],[135,27],{"code":80,"score":89,"supported":52,"title":490,"abstract":491,"tags":492},[],[84],[],{"code":494,"score":89,"supported":52,"title":495,"abstract":496,"tags":498},"H8",[],[497],"可持续农业",[],{"code":500,"score":22,"supported":14,"title":501,"abstract":502,"tags":503},"I3",[],[],[468],[505,506],"CropIntel 数据集 作物分类","机器学习 灌溉需求 预测","CropIntel数据集作物分类-4801","10.30546\u002F09090.2025.210.005",{"doi":508,"openalex_id":510,"authors":511,"venue":460,"cited_by_count":103,"oa_url":457,"card":515,"direction":114,"ingested_from":116},"W7221521521",[512],{"name":513,"orcid":514},"Artughrul Gayibov","https:\u002F\u002Forcid.org\u002F0009-0009-7349-0286",{"tldr":516,"method":517,"finding":518,"direction":224,"opportunity":519},"基于CropIntel数据集，用15+种机器学习算法实现作物分类、灌溉需求预测与产量潜力预测。","CropIntel农业环境数据集，对比15+种机器学习算法，含树模型与贝叶斯分类","树模型分类准确率近99.7%，LightGBM等回归模型对灌溉需求R²约0.84、产量约0.70。","可探索多任务联合建模与模型可解释性，并将预测结果嵌入实时灌溉决策系统。","2026-10-13T23:30:08.688669Z"]