[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3810":3,"related-3810":89},{"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":9,"secondary_topics":32,"classification":33,"search_phrases":54,"slug":57,"view_count":58,"doi":59,"paper":60,"created_at":88},3810,"Assessment of pressure drop across discs in irrigation filters: New experimental and calculation methodology","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104606","The pressure drop across the discs in disc filters for irrigation systems is a key parameter for assessing hydraulic performance and suggesting design improvements to allow energy savings. However, to date, no clear procedure or method has been reported for obtaining this information. Therefore, an experimental apparatus was developed to test four types of discs from two different manufacturers. This device allowed the measurement of the pressure drop across the discs alone, without any additional element that could mask it. The effects of the disc pack height, compression, and filtration grade were investigated. Furthermore, the discs were imaged using an ultra-high resolution field emission scanning electron microscope to characterise the groove dimensions. These data were employed to calculate the pressure drop using a proposed analytical model based on the Darcy-Weisbach and minor loss equations, assuming two limiting cases for the grooves: open and closed conduits, alongside an additional bound-interpolated weighted model (BIW model) that combined both cases. The results showed an inverse relationship between the groove cross-sectional area and the pressure drop for the tested discs, while regression curves fitted to experimental data for each disc revealed a slightly quadratic behaviour. The analytical model successfully predicted a region containing experimental data. The information obtained may allow the development of improved disc filter designs.","灌溉系统叠片过滤器中叠片间的压降是评估水力性能、提出设计改进以实现节能的关键参数。然而，迄今为止，尚未有明确的程序或方法被报道用于获取该信息。因此，本研究开发了一套实验装置，对来自两个不同制造商的四种叠片进行测试。该装置能够单独测量叠片间的压降，而不受任何可能掩盖该压降的附加元件的影响。研究了叠片组高度、压缩量和过滤等级的影响。此外，利用超高分辨率场发射扫描电子显微镜对叠片进行成像，以表征沟槽尺寸。这些数据被用于通过所提出的解析模型计算压降，该模型基于达西-魏斯巴赫（Darcy-Weisbach）方程和局部损失方程，假设沟槽的两种极限情况：明渠和封闭管道，同时结合了一个额外的边界插值加权模型（BIW模型），该模型综合了上述两种情况。结果表明，对于所测试的叠片，沟槽横截面积与压降之间呈反比关系，而各叠片实验数据的拟合回归曲线则呈现出轻微的二次方特征。该解析模型成功预测了包含实验数据的区域。所获得的信息可为改进叠片过滤器设计提供依据。",null,"Biosystems Engineering","2026-09-29T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,14,9,1,"提出叠片过滤器压降测量装置与解析模型，方法新颖、数据扎实，对灌溉系统节能设计有参考价值，但属细分领域技术论文，产业影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","节水灌溉","灌水器","水力性能","过滤设备",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":53},[39,48],{"code":40,"score":41,"supported":42,"title":43,"abstract":45,"tags":47},"H3",15,true,[44],"irrigation",[46,44],"灌溉",[28],{"code":49,"score":22,"supported":14,"title":50,"abstract":51,"tags":52},"I1",[],[],[27],"农业相关性需要复核",[55,56],"叠片过滤器 压降 试验","智慧农业 水力性能 节水灌溉 过滤设备","叠片过滤器压降试验-3810",2,"10.1016\u002Fj.biosystemseng.2026.104606",{"doi":59,"openalex_id":61,"authors":62,"venue":10,"cited_by_count":80,"oa_url":6,"card":81,"direction":85,"ingested_from":87},"W7214745100",[63,65,68,71,74,77],{"name":64,"orcid":9},"Aniol Castells",{"name":66,"orcid":67},"Miquel Duran–Ros","https:\u002F\u002Forcid.org\u002F0000-0002-3286-8811",{"name":69,"orcid":70},"Gerard Arbat","https:\u002F\u002Forcid.org\u002F0000-0003-2192-0781",{"name":72,"orcid":73},"F. Ramírez de Cartagena","https:\u002F\u002Forcid.org\u002F0000-0001-8844-7461",{"name":75,"orcid":76},"Jaume Puig–Bargués","https:\u002F\u002Forcid.org\u002F0000-0002-6712-269X",{"name":78,"orcid":79},"Toni Pujol","https:\u002F\u002Forcid.org\u002F0000-0001-7013-2867",0,{"tldr":82,"method":83,"finding":84,"direction":85,"opportunity":86},"提出测量与计算灌溉叠片过滤器叠片压降的新实验装置与解析模型。","搭建实验台测四类叠片压降，用SEM表征沟槽，基于达西-魏斯巴赫与局部损失建模。","沟槽截面积与压降呈反比，解析模型能预测实验数据所在区域。","智慧农业 \u002F 农业物联网","可结合物联网实时监测压降，开发低能耗自适应叠片过滤器设计与堵塞预警模型。","openalex","2026-09-30T23:30:05.559936Z",{"total":90,"page":22,"page_size":90,"items":91},6,[92,172,280,366,403,483],{"id":93,"title":94,"url":95,"summary":96,"summary_zh":97,"content":9,"source_name":98,"source_url":95,"published_at":99,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":100,"score_detail":101,"sources":106,"tags":108,"primary_topic":112,"secondary_topics":118,"classification":123,"search_phrases":148,"slug":151,"view_count":80,"doi":152,"paper":153,"created_at":171},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,{"impact":17,"substance":19,"depth":102,"authority":103,"freshness":104,"relevant":22,"comment":105},16,13,8,"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。",[107],{"name":98,"url":95},[27,109,110,28,111],"物联网","精准农业","LoRa",{"code":113,"slug":114,"name":115,"path":116,"parent_code":117},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[119],{"code":40,"slug":44,"name":120,"path":121,"parent_code":122},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":124,"locked":14,"version":35,"method":36,"evidence":125},"classified",{"candidates":126,"agricultural_evidence":42},[127,133,137,144],{"code":113,"score":19,"supported":42,"title":128,"abstract":130,"tags":132},[129],"IoT",[109,131,129],"internet of things",[109],{"code":40,"score":41,"supported":42,"title":134,"abstract":135,"tags":136},[44],[46,44],[28],{"code":138,"score":90,"supported":42,"title":139,"abstract":140,"tags":143},"H2",[],[141,142],"土壤","soil",[],{"code":49,"score":58,"supported":14,"title":145,"abstract":146,"tags":147},[],[],[27,110],[149,150],"ESP32 LoRa 智能灌溉","SX1278 精准灌溉","ESP32LoRa智能灌溉-5378","10.3390\u002Felectronics15204831",{"doi":152,"openalex_id":154,"authors":155,"venue":98,"cited_by_count":80,"oa_url":95,"card":166,"direction":85,"ingested_from":87},"W7222959710",[156,158,160,163],{"name":157,"orcid":9},"Hanfoug Salah",{"name":159,"orcid":9},"Riad Saidi",{"name":161,"orcid":162},"Abdelaziz Aouiche","https:\u002F\u002Forcid.org\u002F0000-0003-1378-7941",{"name":164,"orcid":165},"Yazeed Alkhrijah","https:\u002F\u002Forcid.org\u002F0000-0001-7352-003X",{"tldr":167,"method":168,"finding":169,"direction":85,"opportunity":170},"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":173,"title":174,"url":175,"summary":176,"summary_zh":177,"content":9,"source_name":178,"source_url":175,"published_at":179,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":180,"score_detail":181,"sources":185,"tags":187,"primary_topic":191,"secondary_topics":192,"classification":199,"search_phrases":250,"slug":253,"view_count":80,"doi":254,"paper":255,"created_at":279},4989,"Advances in micro-irrigation and supplementary irrigation in millets of dryland ecosystems","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.16312","Dryland ecosystems cover approximately 41 % of the Earth's terrestrial surface and support nearly two billion people. This review synthesises recent advances in micro-irrigation and supplementary irrigation strategies for enhancing millet productivity and resilience in dryland ecosystems. Drylands are characterised by low and erratic rainfall, high evapotranspiration, poor soil fertility and frequent drought stress, making agricultural production vulnerable to climate variability and water scarcity. Millets are drought-tolerant, climate-resilient cereals cultivated in dryland agroecosystems. Despite their resilience, millet productivity remains constrained by increasing rainfall variability, prolonged intra-seasonal dry spells and rising atmospheric aridity associated with climate change. Evidence from semi-arid and arid regions indicates that micro-irrigation systems, including drip, subsurface drip and micro-sprinkler irrigation, significantly improve yield and biomass accumulation compared to conventional surface irrigation methods. These irrigation systems also enhance water use efficiency (WUE) and nutrient use efficiency (NUE), making them highly suitable for millet production in dryland ecosystems. The integration of fertigation further improves nutrient synchronisation with crop demand, reduces leaching losses and supports soil fertility. The review also highlights optimised irrigation scheduling based on evapotranspiration, soil moisture thresholds, deficit irrigation principles and sensor-based automation. Smart irrigation technologies incorporating soil moisture sensors and Internet of Things (IoT)-based control systems show strong potential to enhance precision water management and reduce water consumption by up to 30–35 %. Their adoption, supported by technological innovation, policy frameworks and location-specific research, will promote sustainable water management and resource-use efficiency in millet-based dryland ecosystems. Future research should focus on optimising irrigation scheduling, smart technologies and cost-effective solutions for water-limited environments.","旱地生态系统约占地球陆地表面的41%，支撑着近20亿人口。本文综述了微灌与补充灌溉策略在提升旱地生态系统中小米生产力与恢复力方面的最新进展。旱地降水少且不稳定、蒸散量大、土壤肥力低、干旱胁迫频繁，使农业生产易受气候变率和缺水的影响。小米是种植于旱地农业生态系统中的耐旱、气候适应型谷物。尽管具有恢复力，小米生产力仍受降雨变率增大、季节性持续干旱期延长以及气候变化相关的大气干燥度上升的制约。来自半干旱和干旱地区的证据表明，与传统地面灌溉方式相比，微灌系统（包括滴灌、地下滴灌和微喷灌）显著提高了产量和生物量积累。这些灌溉系统还提高了水分利用效率（WUE）和养分利用效率（NUE），使其非常适合旱地生态系统中的小米生产。水肥一体化（fertigation）的整合进一步改善了养分与作物需求的同步性，减少了淋失损失，并有助于维持土壤肥力。本文还重点介绍了基于蒸散量、土壤水分阈值、亏缺灌溉原则和基于传感器的自动化的优化灌溉制度。结合土壤水分传感器和基于物联网（IoT）控制系统的智能灌溉技术，在提升精准水分管理和减少高达30%–35%的用水量方面展现出巨大潜力。在技术创新、政策框架和因地制宜研究的支持下，这些技术的采用将促进以小米为基础的旱地生态系统中的可持续水分管理和资源利用效率。未来研究应聚焦于优化灌溉制度、智能技术以及面向水资源受限环境的成本效益解决方案。","Plant Science Today","2026-10-14T00:00:00Z",76,{"impact":19,"substance":182,"depth":183,"authority":103,"freshness":104,"relevant":22,"comment":184},20,17,"系统综述微灌与补充灌溉提升旱地谷子产量与水肥效率，含物联网智能灌溉节水30-35%等实质结论，对旱作智慧农业有参考价值。",[186],{"name":178,"url":175},[27,188,189,28,190],"农业物联网","旱作农业","谷子高粱",{"code":40,"slug":44,"name":120,"path":121,"parent_code":122},[193,194],{"code":113,"slug":114,"name":115,"path":116,"parent_code":117},{"code":195,"slug":196,"name":197,"path":198,"parent_code":122},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":124,"locked":14,"version":35,"method":36,"evidence":200},{"candidates":201,"agricultural_evidence":42},[202,209,213,222,229,233,240,246],{"code":40,"score":203,"supported":42,"title":204,"abstract":205,"tags":208},19,[44],[46,206,44,207],"水资源","evapotranspiration",[189,28],{"code":113,"score":13,"supported":42,"title":210,"abstract":211,"tags":212},[],[109,131,129],[188],{"code":195,"score":21,"supported":42,"title":214,"abstract":215,"tags":221},[],[216,217,218,219,220],"气候变化","气候适应","干旱","climate change","drought",[],{"code":223,"score":90,"supported":42,"title":224,"abstract":225,"tags":228},"F3",[],[226,227],"水肥一体化","fertigation",[],{"code":138,"score":90,"supported":42,"title":230,"abstract":231,"tags":232},[],[141,142],[],{"code":234,"score":235,"supported":42,"title":236,"abstract":237,"tags":239},"A5",3,[],[238],"millet",[],{"code":241,"score":235,"supported":42,"title":242,"abstract":243,"tags":245},"H6",[],[244],"农业生态",[],{"code":49,"score":22,"supported":14,"title":247,"abstract":248,"tags":249},[],[],[27],[251,252],"微灌 补充灌溉 旱地 谷子","智慧灌溉 水分利用效率 谷子","微灌补充灌溉旱地谷子-4989","10.14719\u002Fpst.16312",{"doi":254,"openalex_id":256,"authors":257,"venue":178,"cited_by_count":80,"oa_url":273,"card":274,"direction":85,"ingested_from":87},"W7221796668",[258,261,264,267,270],{"name":259,"orcid":260},"M Megha","https:\u002F\u002Forcid.org\u002F0009-0006-7303-6946",{"name":262,"orcid":263},"N Vadivel","https:\u002F\u002Forcid.org\u002F0000-0002-5301-5324",{"name":265,"orcid":266},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":268,"orcid":269},"P. Ayyadurai","https:\u002F\u002Forcid.org\u002F0000-0003-1889-6047",{"name":271,"orcid":272},"PD Rathinasamy","https:\u002F\u002Forcid.org\u002F0000-0001-8793-3513","https:\u002F\u002Fhorizonepublishing.com\u002Fjournals\u002Findex.php\u002FPST\u002Farticle\u002Fdownload\u002F16312\u002F16748",{"tldr":275,"method":276,"finding":277,"direction":85,"opportunity":278},"综述旱地微灌与补充灌溉提升小米产量与水分利用效率的研究进展。","综述滴灌、地下滴灌、微喷及水肥一体化、传感器与IoT智能灌溉技术。","微灌显著提高小米产量、WUE和NUE，智能灌溉可节水30–35%。","可研究低成本IoT灌溉决策模型在小米旱地的本地化适配与长期效应。","2026-10-15T23:30:08.971713Z",{"id":281,"title":282,"url":283,"summary":284,"summary_zh":285,"content":9,"source_name":286,"source_url":283,"published_at":287,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":288,"score_detail":289,"sources":291,"tags":293,"primary_topic":297,"secondary_topics":298,"classification":299,"search_phrases":346,"slug":349,"view_count":80,"doi":350,"paper":351,"created_at":365},4740,"EcoGrow Smart Irrigation System (EGSIS) A solar-powered, AI-enabled irrigation and water management system with live monitoring, alarms and ESG reporting for climate-smart food production, water resilience, land-based aquaculture and the circular economy","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23294278","The EcoGrow Smart Irrigation System (EGSIS) is a low-cost, solar-powered irrigation controller and water management system designed for small-scale vegetable production. It replaces calendar-based or manual watering with irrigation that responds to the actual moisture condition of the soil. Water is applied only when the crop needs it, and the entire system runs on solar energy stored in a long-life LiFePO4 battery. Agriculture is both a major user of freshwater and a significant source of greenhouse gas emissions, while small-scale growers in South Africa face unreliable electricity, rising energy costs, drought risk and limited access to technology. EGSIS addresses these pressures with a compact, affordable and measurable solution that links renewable energy, automation and intelligent water use. EGSIS also includes an AI and software layer. Machine-learning models adapt irrigation to different crops, growth stages and weather, while a web and mobile app gives growers live data on tank levels, solar generation and soil moisture, a dashboard of key performance indicators, and triggers and alarms for faults. The same data feeds automated ESG (environmental, social and governance) reports that growers can use to attract funding and investment.","EcoGrow智能灌溉系统（EGSIS）是一款低成本、太阳能驱动的灌溉控制器与水资源管理系统，专为小规模蔬菜生产而设计。它取代了基于日历或人工浇水的模式，转而根据土壤实际湿度状况进行灌溉。只有在作物需要时才供水，整个系统依靠储存在长寿命磷酸铁锂（LiFePO4）电池中的太阳能运行。农业既是淡水的主要使用者，也是温室气体排放的重要来源，而南非的小规模种植者面临着电力不可靠、能源成本上涨、干旱风险以及技术获取渠道有限等问题。EGSIS通过一种紧凑、经济且可量化的解决方案来应对这些压力，将可再生能源、自动化和智能用水连接起来。EGSIS还包含人工智能与软件层。机器学习模型可根据不同作物、生长阶段和天气调整灌溉，而网页和移动应用则为种植者提供水箱水位、太阳能发电量和土壤湿度的实时数据、关键绩效指标仪表板，以及故障触发器和警报。同一套数据还可用于生成自动化ESG（环境、社会和治理）报告，种植者可借此吸引资金和投资。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-11T00:00:00Z",68,{"impact":17,"substance":19,"depth":102,"authority":103,"freshness":21,"relevant":22,"comment":290},"面向小农户的低成本太阳能AI灌溉系统，方法具体、数据可测，但属细分技术方案，公共影响有限。",[292],{"name":286,"url":283},[27,294,28,295,296],"农业人工智能","光伏农业","ESG报告",{"code":40,"slug":44,"name":120,"path":121,"parent_code":122},[],{"status":124,"locked":14,"version":35,"method":36,"evidence":300},{"candidates":301,"agricultural_evidence":42},[302,306,312,320,327,331,335,342],{"code":40,"score":19,"supported":42,"title":303,"abstract":304,"tags":305},[44],[46,206,44],[28],{"code":307,"score":104,"supported":42,"title":308,"abstract":310,"tags":311},"D",[309],"aquaculture",[],[],{"code":313,"score":314,"supported":42,"title":315,"abstract":316,"tags":319},"B7",7,[],[317,318],"温室","greenhouse",[295],{"code":321,"score":90,"supported":42,"title":322,"abstract":323,"tags":326},"B1",[],[324,325],"蔬菜","vegetable",[],{"code":138,"score":90,"supported":42,"title":328,"abstract":329,"tags":330},[],[141,142],[],{"code":195,"score":90,"supported":42,"title":332,"abstract":333,"tags":334},[],[218,220],[],{"code":336,"score":337,"supported":42,"title":338,"abstract":339,"tags":341},"I2",4,[],[340],"机器学习",[294],{"code":49,"score":22,"supported":14,"title":343,"abstract":344,"tags":345},[],[],[27],[347,348],"EGSIS 太阳能 灌溉","农业人工智能 光伏农业 智慧农业 节水灌溉","EGSIS太阳能灌溉-4740","10.5281\u002Fzenodo.23294278",{"doi":350,"openalex_id":352,"authors":353,"venue":286,"cited_by_count":80,"oa_url":283,"card":360,"direction":85,"ingested_from":87},"W7221439427",[354,356,358],{"name":355,"orcid":9},"Hoko Energy and Projects",{"name":357,"orcid":9},"Siyabulela Hoko",{"name":359,"orcid":9},"Asithandile Mgweba",{"tldr":361,"method":362,"finding":363,"direction":85,"opportunity":364},"开发太阳能AI灌溉系统EGSIS，按土壤湿度精准灌溉并生成ESG报告。","太阳能供电、LiFePO4电池、机器学习模型、Web\u002F移动应用实时监控。","低成本系统可减少用水、适应小农需求，并自动生成ESG报告助力融资。","可研究AI灌溉模型在不同作物和气候下的泛化能力，以及ESG报告对小农融资的实际影响。","2026-10-12T23:30:07.706557Z",{"id":367,"title":368,"url":369,"summary":370,"summary_zh":9,"content":371,"source_name":372,"source_url":9,"published_at":373,"category":374,"cover_url":9,"hotness":13,"is_selected":14,"score":375,"score_detail":376,"sources":378,"tags":380,"primary_topic":9,"secondary_topics":384,"classification":385,"search_phrases":398,"slug":401,"view_count":22,"doi":9,"paper":9,"created_at":402},3709,"中国农科院第八届农科开放日启动 36个院所560余项成果亮相","https:\u002F\u002Fwww.farmer.com.cn\u002F2026\u002F09\u002F24\u002Fwap_991029575.html","9月19日中国农业科学院第八届农科开放日系列活动启动，主题为农业科技赋能点亮美好生活。36个院属研究所联动开展活动，560余项最新农业科技成果集中亮相。自2019年首届至今已累计吸引4400万公众线上线下参与。","本次开放日深耕科普育人初心，聚焦青少年科学素养培育，摒弃单一宣讲模式，打造课堂宣讲、实操体验、趣味游戏、标本观摩相结合的多元科普场景，让青少年在沉浸式体验中探秘农科奥秘，播种科学梦想。\n\n植物保护研究所专场活动人气爆棚，近2000名群众、600余个家庭走进院区，开启植保科学探索之旅。活动设置知识小课堂、科研微体验、趣味游戏三大板块，内容丰富、趣味十足。科研专家围绕草原鼠害绿色防控、外来入侵物种识别、转基因生物技术等热点内容开展科普宣讲，破除大众对植保防控的片面认知。实操区域内，农残检测、病毒试剂盒实验、虫情雷达监测演示、农田植物攻防科普等场景轮番上演，让抽象的植保知识变得直观鲜活。无人机模拟操作、昆虫吊飞实验、果实甜度检测等互动项目，让青少年在动手实践中体悟智慧农业魅力。\n\n农田灌溉研究所走进河南商丘开展科普公益行，打造户外沉浸式科学课堂。活动依托野外科研平台，开设实验观摩、趣味实验、精准农业科普三大板块，学生们实地参观智能温室、科研试验设施，动手操作叶绿素分离、酸碱度检测、显微观察等科学实验，近距离学习节水灌溉、精密播种、智慧农业等前沿技术，彻底打破对传统农耕的刻板印象。活动暖心吸纳特殊青少年群体参与，以温情科普传递科学温度，让每一位青少年都能感受农科魅力。\n\n饲料研究所、生物技术研究所、水稻研究所等纷纷创新科普形式，通过多样化活动将专业农科知识融入趣味互动中。一系列接地气、有新意的科普活动，让青少年跳出课本局限，真切读懂粮食安全、生态保护、种业振兴的深刻内涵，在心中厚植知农、爱农、强农的情怀种子。","中国农网","2026-09-24T00:00:00Z","报道",64,{"impact":19,"substance":20,"depth":17,"authority":20,"freshness":90,"relevant":22,"comment":377},"国家级科研机构开放日科普活动，形式新颖、覆盖面广，但属常规年度活动，信息增量有限，可作为科普类资讯收录。",[379],{"name":372,"url":369},[27,381,382,28,383],"植物保护","农业科普","农科开放日",[],{"status":386,"locked":14,"version":35,"method":36,"evidence":387},"unclassified",{"candidates":388,"agricultural_evidence":42,"reason":397},[389,393],{"code":40,"score":22,"supported":14,"title":390,"abstract":391,"tags":392},[],[],[28],{"code":49,"score":22,"supported":14,"title":394,"abstract":395,"tags":396},[],[],[27],"缺少足够的主题证据",[399,400],"中国农科院 农科开放日","农田灌溉研究所 商丘 科普","中国农科院农科开放日-3709","2026-09-29T00:08:22.104325Z",{"id":404,"title":405,"url":406,"summary":407,"summary_zh":408,"content":9,"source_name":409,"source_url":406,"published_at":410,"category":12,"cover_url":9,"hotness":13,"is_selected":42,"score":411,"score_detail":412,"sources":415,"tags":417,"primary_topic":420,"secondary_topics":421,"classification":422,"search_phrases":457,"slug":460,"view_count":90,"doi":461,"paper":462,"created_at":482},3605,"AquaCrop-derived surrogate model for crop yield response to monthly irrigation deficit","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104995","Context: The Upper Yellow River Basin faces acute water competition between cascade hydropower operations and large irrigation districts. Traditional multi-objective reservoir operation leads to distorted energy-food benefit trade-offs from oversimplified linear representation of agricultural objectives, while process-based crop models are infeasible for direct embedding into reservoir operation frameworks requiring long-sequence iterative optimization due to excessive computational cost. Objective: This study aims to develop an AquaCrop-derived and computationally efficient agricultural surrogate model that accurately maps monthly water deficits to nonlinear crop yield responses, thereby providing a refined agricultural loss function for reservoir operation frameworks. Methods: Focusing on spring wheat, spring maize, and sunflower in the Hetao Irrigation District, a locally calibrated AquaCrop model was applied to simulate yield responses under full-gradient deficit irrigation scenarios. On this basis, we developed high-order polynomial ridge regression surrogate models driven by growing-season Standardized Precipitation Evapotranspiration Index and monthly irrigation deficit ratios. Finally, the model performance was validated using an offline dual validation framework covering generalization performance and decision consistency, and critical irrigation months and yield-loss thresholds were further quantified. Results and conclusions: The surrogate model achieved a global coefficient of determination above 0.93 across dual held-out test sets, with median yield-loss deviations from the sample-best AquaCrop scheme of less than 2%, and outperformed benchmark irrigation allocation schemes. However, prediction accuracy declined substantially under severe water stress, especially for spring maize. Furthermore, computational efficiency was improved by over five orders of magnitude compared with the process-based model. Under the locally parameterized AquaCrop framework, this study identified April as the most irrigation-sensitive month for spring wheat, May for spring maize, and June for sunflower. Compared with interval median scheme, the surrogate model-recommended scheme delayed physically feasible yield-loss thresholds by 6.7 to 14.7 percentage points, with bootstrap 95% confidence intervals entirely above zero. Significance: This study highlights that the AquaCrop-derived surrogate model bridges the gap between crop modeling and reservoir operation by mapping monthly water-allocation decisions to nonlinear crop yield responses. Aligned with the monthly decision cycle of reservoir operation and with ultra-high computational efficiency, this model is expected to serve as a crucial methodological component for future multi-objective reservoir operation frameworks.","背景：黄河上游流域面临梯级水电运行与大型灌区之间尖锐的水资源竞争。传统多目标水库调度因农业目标的过度简化线性表征，导致能量-粮食效益权衡关系失真；而基于过程的作物模型由于计算成本过高，无法直接嵌入需要长序列迭代优化的水库调度框架。目标：本研究旨在开发一种源自AquaCrop且计算高效的农业代理模型，能够准确刻画月度水分亏缺与作物产量非线性响应之间的映射关系，从而为水库调度框架提供精细化的农业损失函数。方法：以河套灌区的春小麦、春玉米和向日葵为研究对象，采用经本地校准的AquaCrop模型模拟全梯度亏缺灌溉情景下的产量响应。在此基础上，构建了以生长季标准化降水蒸散指数和月度灌溉亏缺比为驱动因子的高阶多项式岭回归代理模型。最后，采用涵盖泛化性能和决策一致性的离线双重验证框架对模型性能进行验证，并进一步量化了关键灌溉月份和产量损失阈值。结果与结论：代理模型在两个独立测试集上的全局决定系数均超过0.93，与样本最优AquaCrop方案相比，产量损失偏差中位数低于2%，且优于基准灌溉配水方案。然而，在重度水分胁迫条件下，预测精度显著下降，春玉米尤为明显。此外，与基于过程的模型相比，计算效率提升了五个数量级以上。在本地参数化的AquaCrop框架下，本研究确定春小麦对灌溉最敏感的月份为4月，春玉米为5月，向日葵为6月。与区间中值方案相比，代理模型推荐方案将物理可行的产量损失阈值推迟了6.7至14.7个百分点，Bootstrap 95%置信区间完全位于零以上。意义：本研究突出表明，源自AquaCrop的代理模型通过映射……弥合了作物建模与水库调度之间的差距。","Agricultural Systems","2026-09-26T00:00:00Z",82,{"impact":19,"substance":413,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":414},23,"将AquaCrop过程模型转化为高效代理模型，为水库调度提供非线性农业损失函数，方法新颖、验证扎实，对灌区水资源协同管理有实用价值。",[416],{"name":409,"url":406},[27,294,418,28,419],"作物模型","河套灌区",{"code":40,"slug":44,"name":120,"path":121,"parent_code":122},[],{"status":124,"locked":14,"version":35,"method":36,"evidence":423},{"candidates":424,"agricultural_evidence":42},[425,429,436,443,449,453],{"code":40,"score":19,"supported":42,"title":426,"abstract":427,"tags":428},[44],[46,206,44,207],[28],{"code":430,"score":90,"supported":42,"title":431,"abstract":432,"tags":435},"A2",[],[433,434],"小麦","wheat",[],{"code":437,"score":90,"supported":42,"title":438,"abstract":439,"tags":442},"A3",[],[440,441],"玉米","maize",[],{"code":444,"score":235,"supported":42,"title":445,"abstract":446,"tags":448},"A4",[],[447],"向日葵",[],{"code":336,"score":58,"supported":14,"title":450,"abstract":451,"tags":452},[],[],[418,294],{"code":49,"score":22,"supported":14,"title":454,"abstract":455,"tags":456},[],[],[27],[458,459],"河套灌区 春小麦 灌溉亏缺","AquaCrop 代理模型 产量响应","河套灌区春小麦灌溉亏缺-3605","10.1016\u002Fj.agsy.2026.104995",{"doi":461,"openalex_id":463,"authors":464,"venue":409,"cited_by_count":80,"oa_url":406,"card":476,"direction":480,"ingested_from":87},"W7214449839",[465,467,470,472,474],{"name":466,"orcid":9},"任泽岭",{"name":468,"orcid":469},"Binquan Li","https:\u002F\u002Forcid.org\u002F0000-0002-9958-0396",{"name":471,"orcid":9},"Jing Liu",{"name":473,"orcid":9},"Kuang Li",{"name":475,"orcid":9},"Zhongmin Liang",{"tldr":477,"method":478,"finding":479,"direction":480,"opportunity":481},"构建AquaCrop衍生的代理模型，快速映射月度灌溉亏缺与作物产量的非线性响应。","局部校准AquaCrop模拟亏缺灌溉，用SPEI与亏缺比驱动高阶多项式岭回归代理","代理模型R²>0.93、计算效率提升五个数量级，但重旱下春玉米精度明显下降。","农业人工智能与决策模型","可探索极端干旱下代理模型精度衰减的校正方法，并耦合水库调度实现能量-粮食协同优化。","2026-09-27T23:30:06.912579Z",{"id":484,"title":485,"url":486,"summary":487,"summary_zh":488,"content":9,"source_name":489,"source_url":486,"published_at":490,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":491,"score_detail":492,"sources":494,"tags":496,"primary_topic":499,"secondary_topics":500,"classification":502,"search_phrases":525,"slug":528,"view_count":337,"doi":529,"paper":530,"created_at":542},3472,"Solar- Powered and Performance Evaluation of a Smart Irrigation System","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijemt.vol.12.no8.2026.pg158.171","Experiment was carried out on how to generate and developed solar powered pumping system for smart irrigation. Solar power energy materials used consist of photovoltaic panel, solar charge controller, batteries, and inverter, distribution pipe, voltmeter, wiring accessories and water pump. The coupled energy materials were set at an angle of 150 South - east. The generated voltage recorded during the day time from the solar energy were recorded in two parts; from solar panel and voltage controller. The two solar panels generated an average solar energy of 24.63v, PV panel and 10.23v charge controller. Within 10 hours set for solar energy assessment from the panels,from 9:00 am to 6:00pm, PV panels recorded voltage that ranged between 19.3 to 30.2, while Charge controller recorded from 5.1 to 16.0v.The voltage kept increasing from 9:0am until it reached a peak at 3:0pm, and started declining at 4:0pm to the lowest voltage at 6: 00pm, when the sun rays have gone dim.The charge controller allows current into battery and protect and regulate its charge and discharge. The pump efficiency was tested using variation in pumping time (sec), which was set at 10,20, 30,40,50 and 60 seconds. The flow rate (L\u002Fsec) remained constant at 5 L\u002Fsec, because the battery was at full charge state. The pumping unit was coupled to an automated soil moisture-based drip irrigation system, developed using smart irrigation system. Internet of Things (IoT) components used were, Arduino, Veroboard and Timer Switch, Soil Moisture Sensor, Relay Module and Water Pump. The experimental plots were laid out in an improvised green house. Med emitters were used as irrigation drippers. Moisture sensor was set to trigger irrigation when soil moisture was below50%. The, solar energy generated, sustained pumping water required for the Smart irrigation system throughout the irrigation scheduled period to harvest time.","本研究旨在探讨如何构建和开发用于智能灌溉的太阳能驱动抽水系统。所使用的太阳能能源材料包括光伏板、太阳能充电控制器、电池、逆变器、输水管道、电压表、接线附件和水泵。将耦合的能源材料以东南方向150°角安装。白天从太阳能中产生的电压记录分为两部分：来自太阳能板和电压控制器。两块太阳能板的平均太阳能发电量为24.63V，光伏板为24.63V，充电控制器为10.23V。在用于评估太阳能板发电量的10小时时段内（上午9：00至下午6：00），光伏板记录的电压范围为19.3至30.2V，而充电控制器记录的电压范围为5.1至16.0V。电压从上午9：00起持续上升，直至下午3：00达到峰值，下午4：00开始下降，至下午6：00阳光变暗时降至最低电压。充电控制器允许电流进入电池，并保护和调节其充放电。通过改变抽水时间（秒）测试水泵效率，设定时间为10、20、30、40、50和60秒。由于电池处于满充状态，流量（L\u002Fs）保持恒定在5L\u002Fs。抽水装置与基于土壤湿度的自动化滴灌系统耦合，该系统采用智能灌溉系统开发。所使用的物联网（IoT）组件包括Arduino、万用板（Veroboard）和定时开关、土壤湿度传感器、继电器模块和水泵。试验小区布置在简易温室中。采用中流量滴头作为灌溉滴头。湿度传感器设定为当土壤湿度低于50%时触发灌溉。所产生的太阳能在整个灌溉计划期间直至收获期，持续维持智能灌溉系统所需抽水量。","INTERNATIONAL JOURNAL OF ENGINEERING AND MODERN TECHNOLOGY","2026-09-23T00:00:00Z",49,{"impact":104,"substance":20,"depth":103,"authority":90,"freshness":104,"relevant":22,"comment":493},"太阳能驱动智能灌溉系统的实测数据与IoT集成方案有一定参考价值，但期刊层级偏低、影响范围有限，暂不建议进入每日精选。",[495],{"name":489,"url":486},[27,109,28,497,498],"土壤墒情","太阳能灌溉",{"code":40,"slug":44,"name":120,"path":121,"parent_code":122},[501],{"code":113,"slug":114,"name":115,"path":116,"parent_code":117},{"status":124,"locked":14,"version":35,"method":36,"evidence":503},{"candidates":504,"agricultural_evidence":42},[505,509,513,517,521],{"code":40,"score":41,"supported":42,"title":506,"abstract":507,"tags":508},[44],[46,44],[28],{"code":113,"score":13,"supported":42,"title":510,"abstract":511,"tags":512},[],[109,131,129],[109],{"code":138,"score":314,"supported":42,"title":514,"abstract":515,"tags":516},[],[141,142],[497],{"code":313,"score":235,"supported":42,"title":518,"abstract":519,"tags":520},[],[317],[],{"code":49,"score":22,"supported":14,"title":522,"abstract":523,"tags":524},[],[],[27],[526,527],"太阳能 智能灌溉 系统","土壤湿度 滴灌 物联网","太阳能智能灌溉系统-3472","10.56201\u002Fijemt.vol.12.no8.2026.pg158.171",{"doi":529,"openalex_id":531,"authors":532,"venue":489,"cited_by_count":80,"oa_url":536,"card":537,"direction":85,"ingested_from":87},"W7214070923",[533],{"name":534,"orcid":535},"Malum Japhet Flayin","https:\u002F\u002Forcid.org\u002F0000-0002-9400-2546","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJEMT\u002FVOL. 12 NO. 8 2026\u002FSolar- Powered and Performance Evaluation 158-171.pdf",{"tldr":538,"method":539,"finding":540,"direction":85,"opportunity":541},"研发并评估太阳能供电的智能滴灌系统，利用土壤湿度传感器自动触发灌溉。","光伏板、充电控制器、电池、Arduino与土壤湿度传感器，温室试验。","光伏板日均电压24.63V，泵流量稳定5L\u002Fs，系统可全天候维持灌溉至收获。","可研究多传感器融合与能量管理优化，提升阴雨天续航及灌溉决策精度。","2026-09-25T23:30:10.959076Z"]