[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2395":3,"related-2395":95},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":79,"slug":82,"view_count":83,"doi":8,"paper":84,"created_at":94},2395,"[预印本]Simulating the Marginal Green Contribution of AI Modules in a Smart-Agriculture Platform: Evidence from Two Monte Carlo Experiments","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.06740","arXiv 2609.06740（2026-09-10）。基于海南智慧农业平台两项蒙特卡洛实验：实验1显示AI模式下20%农药减量概率从近零升至20.7%（基线）至49%（诊断精度0.95、采纳率0.85），15%化肥减量概率从近零升至52.0%；实验2显示AI灌溉调度增加5.0个百分点节水至16.0%，稻田CH4减排30.5%。研究指出农户采纳是主要瓶颈。",null,"arXiv | 2026-09-10","2026-09-10T00:00:00Z","论文",10,false,78,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,22,12,8,1,"预印本以蒙特卡洛模拟量化AI模块在农药化肥减量与稻田减排上的边际绿色贡献，数据与结论有新意，但尚待同行评议，属细分领域前沿进展。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","精准农业","节水灌溉","农业减排",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":46},[43,50,56,63,69,74],{"code":44,"score":45,"supported":46,"title":47,"abstract":48,"tags":49},"I1",5,true,[],[25],[25,27],{"code":31,"score":51,"supported":46,"title":52,"abstract":53,"tags":55},4,[],[54],"灌溉",[28],{"code":57,"score":58,"supported":46,"title":59,"abstract":60,"tags":62},"A1",3,[],[61],"稻田",[],{"code":64,"score":58,"supported":46,"title":65,"abstract":66,"tags":68},"F4",[],[67],"农药",[],{"code":70,"score":20,"supported":13,"title":71,"abstract":72,"tags":73},"H4",[],[],[29],{"code":75,"score":20,"supported":13,"title":76,"abstract":77,"tags":78},"I2",[],[],[26],[80,81],"农业人工智能 农业减排 智慧农业 精准农业","农业人工智能 农业减排","农业人工智能农业减排智慧农业精准农业-2395",2,{"doi":8,"openalex_id":8,"authors":85,"venue":8,"cited_by_count":86,"oa_url":8,"card":87,"direction":91,"ingested_from":93},[],0,{"tldr":88,"method":89,"finding":90,"direction":91,"opportunity":92},"用蒙特卡洛实验模拟智慧农业平台中AI模块对农药化肥减量与节水减排的边际绿色贡献。","海南智慧农业平台数据，两项蒙特卡洛实验，含诊断精度与采纳率情景。","AI使农药减量20%概率升至20.7%-49%，化肥减量15%概率达52%，稻田CH4减排30.5%","农业绿色发展与碳","可将农户采纳行为内生化，研究采纳率提升机制与AI模块绿色效益的经济激励设计。","agent","2026-09-14T00:06:32.920282Z",{"total":96,"page":20,"page_size":96,"items":97},6,[98,214,277,346,429,513],{"id":99,"title":100,"url":101,"summary":102,"summary_zh":103,"content":8,"source_name":104,"source_url":101,"published_at":105,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":106,"score_detail":107,"sources":111,"tags":113,"primary_topic":116,"secondary_topics":121,"classification":128,"search_phrases":187,"slug":190,"view_count":86,"doi":191,"paper":192,"created_at":213},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":16,"substance":17,"depth":16,"authority":108,"freshness":109,"relevant":20,"comment":110},13,9,"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。",[112],{"name":104,"url":101},[25,26,114,27,115],"物联网","气候韧性",{"code":75,"slug":117,"name":118,"path":119,"parent_code":120},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[122,127],{"code":123,"slug":124,"name":125,"path":126,"parent_code":120},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":129},{"candidates":130,"agricultural_evidence":46},[131,142,148,155,160,168,175,181],{"code":75,"score":16,"supported":46,"title":132,"abstract":134,"tags":141},[133],"artificial intelligence",[135,136,137,138,139,140],"深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[26],{"code":44,"score":143,"supported":46,"title":144,"abstract":146,"tags":147},16,[145],"precision agriculture",[27,145],[25,27],{"code":123,"score":12,"supported":46,"title":149,"abstract":150,"tags":154},[],[114,151,152,153],"边缘计算","IoT","edge computing",[114],{"code":31,"score":109,"supported":46,"title":156,"abstract":157,"tags":159},[],[54,158,32],"水资源",[],{"code":161,"score":162,"supported":46,"title":163,"abstract":164,"tags":167},"H7",7,[],[165,166],"气候变化","climate change",[115],{"code":169,"score":96,"supported":46,"title":170,"abstract":171,"tags":174},"H2",[],[172,173],"土壤","soil",[],{"code":176,"score":58,"supported":46,"title":177,"abstract":178,"tags":180},"A7",[],[179],"间作",[],{"code":182,"score":58,"supported":46,"title":183,"abstract":184,"tags":186},"F3",[],[185],"养分管理",[],[188,189],"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":191,"openalex_id":193,"authors":194,"venue":104,"cited_by_count":86,"oa_url":101,"card":205,"direction":211,"ingested_from":212},"W7223121456",[195,198,200,202],{"name":196,"orcid":197},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":199,"orcid":8},"Oluwadare OYEBODE",{"name":201,"orcid":8},"Iretiayo Adelaiye",{"name":203,"orcid":204},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":206,"method":207,"finding":208,"direction":209,"opportunity":210},"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。","智慧农业 \u002F 农业物联网","openalex","2026-10-22T11:20:18.151376Z",{"id":215,"title":216,"url":217,"summary":218,"summary_zh":219,"content":8,"source_name":220,"source_url":217,"published_at":221,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":222,"score_detail":223,"sources":225,"tags":227,"primary_topic":229,"secondary_topics":230,"classification":232,"search_phrases":253,"slug":256,"view_count":86,"doi":257,"paper":258,"created_at":276},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":18,"substance":16,"depth":143,"authority":108,"freshness":19,"relevant":20,"comment":224},"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。",[226],{"name":220,"url":217},[25,114,27,28,228],"LoRa",{"code":123,"slug":124,"name":125,"path":126,"parent_code":120},[231],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":233},{"candidates":234,"agricultural_evidence":46},[235,240,245,249],{"code":123,"score":16,"supported":46,"title":236,"abstract":237,"tags":239},[152],[114,238,152],"internet of things",[114],{"code":31,"score":241,"supported":46,"title":242,"abstract":243,"tags":244},15,[32],[54,32],[28],{"code":169,"score":96,"supported":46,"title":246,"abstract":247,"tags":248},[],[172,173],[],{"code":44,"score":83,"supported":13,"title":250,"abstract":251,"tags":252},[],[],[25,27],[254,255],"ESP32 LoRa 智能灌溉","SX1278 精准灌溉","ESP32LoRa智能灌溉-5378","10.3390\u002Felectronics15204831",{"doi":257,"openalex_id":259,"authors":260,"venue":220,"cited_by_count":86,"oa_url":217,"card":271,"direction":211,"ingested_from":212},"W7222959710",[261,263,265,268],{"name":262,"orcid":8},"Hanfoug Salah",{"name":264,"orcid":8},"Riad Saidi",{"name":266,"orcid":267},"Abdelaziz Aouiche","https:\u002F\u002Forcid.org\u002F0000-0003-1378-7941",{"name":269,"orcid":270},"Yazeed Alkhrijah","https:\u002F\u002Forcid.org\u002F0000-0001-7352-003X",{"tldr":272,"method":273,"finding":274,"direction":211,"opportunity":275},"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":278,"title":279,"url":280,"summary":281,"summary_zh":282,"content":8,"source_name":283,"source_url":280,"published_at":284,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":285,"score_detail":286,"sources":290,"tags":292,"primary_topic":295,"secondary_topics":296,"classification":298,"search_phrases":329,"slug":332,"view_count":86,"doi":333,"paper":334,"created_at":345},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":18,"substance":287,"depth":288,"authority":108,"freshness":109,"relevant":20,"comment":289},21,17,"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。",[291],{"name":283,"url":280},[25,26,293,27,294],"产量预测","灌溉决策",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[297],{"code":75,"slug":117,"name":118,"path":119,"parent_code":120},{"status":38,"locked":13,"version":39,"method":40,"evidence":299},{"candidates":300,"agricultural_evidence":46},[301,305,310,314,318,324],{"code":31,"score":16,"supported":46,"title":302,"abstract":303,"tags":304},[32],[54,158,32],[294],{"code":75,"score":12,"supported":46,"title":306,"abstract":307,"tags":309},[],[136,138,308],"decision support",[26],{"code":44,"score":19,"supported":46,"title":311,"abstract":312,"tags":313},[],[27,145],[25,27],{"code":169,"score":58,"supported":46,"title":315,"abstract":316,"tags":317},[],[172],[],{"code":319,"score":58,"supported":46,"title":320,"abstract":321,"tags":323},"H8",[],[322],"可持续农业",[],{"code":325,"score":20,"supported":13,"title":326,"abstract":327,"tags":328},"I3",[],[],[293],[330,331],"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":333,"openalex_id":335,"authors":336,"venue":283,"cited_by_count":86,"oa_url":280,"card":340,"direction":211,"ingested_from":212},"W7221521521",[337],{"name":338,"orcid":339},"Artughrul Gayibov","https:\u002F\u002Forcid.org\u002F0009-0009-7349-0286",{"tldr":341,"method":342,"finding":343,"direction":209,"opportunity":344},"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",{"id":347,"title":348,"url":349,"summary":350,"summary_zh":351,"content":8,"source_name":352,"source_url":349,"published_at":353,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":354,"score_detail":355,"sources":357,"tags":359,"primary_topic":362,"secondary_topics":363,"classification":364,"search_phrases":409,"slug":412,"view_count":86,"doi":413,"paper":414,"created_at":428},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":18,"substance":16,"depth":143,"authority":108,"freshness":109,"relevant":20,"comment":356},"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。",[358],{"name":352,"url":349},[25,26,28,360,361],"光伏农业","ESG报告",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":365},{"candidates":366,"agricultural_evidence":46},[367,371,377,384,391,395,401,405],{"code":31,"score":16,"supported":46,"title":368,"abstract":369,"tags":370},[32],[54,158,32],[28],{"code":372,"score":19,"supported":46,"title":373,"abstract":375,"tags":376},"D",[374],"aquaculture",[],[],{"code":378,"score":162,"supported":46,"title":379,"abstract":380,"tags":383},"B7",[],[381,382],"温室","greenhouse",[360],{"code":385,"score":96,"supported":46,"title":386,"abstract":387,"tags":390},"B1",[],[388,389],"蔬菜","vegetable",[],{"code":169,"score":96,"supported":46,"title":392,"abstract":393,"tags":394},[],[172,173],[],{"code":161,"score":96,"supported":46,"title":396,"abstract":397,"tags":400},[],[398,399],"干旱","drought",[],{"code":75,"score":51,"supported":46,"title":402,"abstract":403,"tags":404},[],[136],[26],{"code":44,"score":20,"supported":13,"title":406,"abstract":407,"tags":408},[],[],[25],[410,411],"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":413,"openalex_id":415,"authors":416,"venue":352,"cited_by_count":86,"oa_url":349,"card":423,"direction":211,"ingested_from":212},"W7221439427",[417,419,421],{"name":418,"orcid":8},"Hoko Energy and Projects",{"name":420,"orcid":8},"Siyabulela Hoko",{"name":422,"orcid":8},"Asithandile Mgweba",{"tldr":424,"method":425,"finding":426,"direction":211,"opportunity":427},"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":430,"title":431,"url":432,"summary":433,"summary_zh":434,"content":8,"source_name":435,"source_url":432,"published_at":436,"category":11,"cover_url":8,"hotness":12,"is_selected":46,"score":437,"score_detail":438,"sources":441,"tags":443,"primary_topic":446,"secondary_topics":451,"classification":453,"search_phrases":485,"slug":488,"view_count":20,"doi":489,"paper":490,"created_at":512},4371,"Intelligent greenhouse irrigation control via multilayer perceptron neural network climate prediction integrated with fuzzy logic control","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compeleceng.2026.111587","Conventional irrigation methods rely on indoor sensor feedback, which fails to anticipate rapid microclimate fluctuations and often leads to over-irrigation or water stress. Additionally, traditional controllers used in the watering process lack the ability to adjust water flow rate dynamically, resulting in inefficient water usage and energy waste. Intelligent Greenhouse Irrigation Management (IGIM) remains a critical challenge for achieving sustainable, high-yield greenhouse agriculture. Precise irrigation management is especially important for crops like tomatoes, where both timing and quantity directly influence productivity and energy consumption. To address these challenges, this paper proposes a hybrid intelligent system that integrates a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) with an Adaptive Fuzzy Logic Controller (AFLC). The MLP-ANN forecasts key indoor variables such as temperature, humidity, and soil moisture, achieving R² values of 0.999. The Root Mean Square Errors of 0.266 °C, 0.254%, and 0.808%, respectively. These predictions directly feed into the AFLC, which employs an adaptive rule base to dynamically adjust irrigation flow rates in real time (0–18 LPH), significantly enhancing water-use efficiency and reducing waste to save energy consumption in the watering process. The key novelty of this work is the direct integration of MLP-predicted microclimate variables into the antecedent of adaptive fuzzy rules, enabling the controller to anticipate and respond to climate variations before they affect crop conditions. Unlike conventional reactive or static rule-based systems, the proposed MLP-AFLC framework creates a closed-loop coupling between forecasting and actuation, allowing predictive, rather than threshold-based, irrigation decisions. By integrating predictive intelligence with adaptive control, the proposed MLP-AFLC framework provides a robust, autonomous, and sustainable solution for greenhouse management, advancing precision agriculture in highly dynamic environments.","传统灌溉方法依赖室内传感器反馈，无法预判微气候的快速波动，常导致过度灌溉或水分胁迫。此外，灌溉过程中使用的传统控制器缺乏动态调节流速的能力，造成水资源利用效率低下和能源浪费。智能温室灌溉管理（IGIM）仍是实现可持续、高产温室农业的关键挑战。对于番茄等作物而言，精准灌溉管理尤为重要，因为灌溉时机和水量直接影响产量和能耗。为解决这些挑战，本文提出了一种将多层感知器人工神经网络（MLP-ANN）与自适应模糊逻辑控制器（AFLC）相集成的混合智能系统。MLP-ANN预测温度、湿度和土壤湿度等关键室内变量，R²值达到0.999，均方根误差分别为0.266 °C、0.254%和0.808%。这些预测结果直接输入AFLC，后者采用自适应规则库实时动态调节灌溉流速（0–18 LPH），显著提高水资源利用效率并减少浪费，从而在灌溉过程中节约能耗。本工作的关键创新在于将MLP预测的微气候变量直接集成到自适应模糊规则的前件中，使控制器能够在气候变化影响作物状况之前进行预判和响应。与传统反应式或静态规则系统不同，所提出的MLP-AFLC框架在预测与执行之间建立了闭环耦合，实现了基于预测而非基于阈值的灌溉决策。通过将预测智能与自适应控制相结合，所提出的MLP-AFLC框架为温室管理提供了一种稳健、自主且可持续的解决方案，推动了高度动态环境下的精准农业发展。","Computers & Electrical Engineering","2026-10-06T00:00:00Z",81,{"impact":16,"substance":17,"depth":16,"authority":439,"freshness":109,"relevant":20,"comment":440},14,"提出MLP-ANN与自适应模糊控制融合的温室预测性灌溉框架，R²达0.999，方法新颖且数据可靠，对精准农业节水节能有实质参考价值。",[442],{"name":435,"url":432},[25,26,444,27,445],"智能灌溉","温室种植",{"code":378,"slug":447,"name":448,"path":449,"parent_code":450},"protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[452],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":454},{"candidates":455,"agricultural_evidence":46},[456,460,464,468,472,477,481],{"code":31,"score":16,"supported":46,"title":457,"abstract":458,"tags":459},[32],[54,158,32],[444],{"code":378,"score":241,"supported":46,"title":461,"abstract":462,"tags":463},[382],[381,382],[445],{"code":44,"score":19,"supported":46,"title":465,"abstract":466,"tags":467},[],[27,145],[25,27],{"code":169,"score":96,"supported":46,"title":469,"abstract":470,"tags":471},[],[172,173],[],{"code":385,"score":58,"supported":46,"title":473,"abstract":474,"tags":476},[],[475],"番茄",[],{"code":161,"score":58,"supported":46,"title":478,"abstract":479,"tags":480},[],[165],[],{"code":75,"score":20,"supported":13,"title":482,"abstract":483,"tags":484},[],[],[26],[486,487],"MLP-AFLC 温室灌溉","神经网络 模糊控制 灌溉","MLP-AFLC温室灌溉-4371","10.1016\u002Fj.compeleceng.2026.111587",{"doi":489,"openalex_id":491,"authors":492,"venue":435,"cited_by_count":86,"oa_url":432,"card":507,"direction":209,"ingested_from":212},"W7220746994",[493,496,499,501,504],{"name":494,"orcid":495},"Jamel Riahi","https:\u002F\u002Forcid.org\u002F0009-0006-1434-4367",{"name":497,"orcid":498},"Hamza Nasri","https:\u002F\u002Forcid.org\u002F0009-0008-7343-4199",{"name":500,"orcid":8},"Abdelkader Mami",{"name":502,"orcid":503},"Mario Carpentieri","https:\u002F\u002Forcid.org\u002F0000-0001-5165-5873",{"name":505,"orcid":506},"Silvano Vergura","https:\u002F\u002Forcid.org\u002F0000-0002-0826-9237",{"tldr":508,"method":509,"finding":510,"direction":211,"opportunity":511},"提出MLP-ANN预测温室微气候并驱动自适应模糊控制器，实现预测性智能灌溉。","MLP神经网络预测温湿度与土壤水分，结合自适应模糊逻辑控制流量。","预测R²达0.999，模糊控制器动态调节0-18 LPH流量，显著节水节能。","可探索多作物多环境迁移学习与边缘部署，降低预测模型算力需求。","2026-10-07T23:30:43.848308Z",{"id":514,"title":515,"url":516,"summary":517,"summary_zh":518,"content":8,"source_name":519,"source_url":520,"published_at":221,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":521,"score_detail":522,"sources":524,"tags":526,"primary_topic":527,"secondary_topics":528,"classification":529,"search_phrases":556,"slug":559,"view_count":83,"doi":560,"paper":561,"created_at":573},4209,"AI-Driven Smart Irrigation System Using Edge-Based Embedded Controllers","https:\u002F\u002Fdoi.org\u002F10.31838\u002Fece\u002F03.02.04","This study brings out a new AI-based smart irrigation device aimed at solving the increasing problem of water shortage and poor agricultural procedures using emulated edge-based controllers. This combination of environmental sensing, lightweight machine learning algorithms and actuation on microcontrollers enables the system to do real time, adaptive irrigation scheduling. Their implementation of important parts like Raspberry Pi 4 as an AI inference module and an STM32 microcontroller as an actuator controller accelerates the responsiveness, increases security, and saves energy consumption since there is no need in cloud computing. Using the local climatic and soil moisture data the machine learning model identifies optimum irrigation time and duration looking at various parameters such as temperature, humidity, rain and crop-specific thresholds. The whole decision-making process is automated and carried out at the edge that makes it functional even without interruption in areas with poor or no internet connection. This was done by conducting extensive field tests within the semi-arid agricultural areas to prove the efficacy of the recommended solution. The findings show that there is a considerable gain in the efficiency of resources, with up to 38 percent of reduction of water use and 20 percent of increase in the yield of the crops contrasted to the conventional time-based irrigation schemes. Also, the system averaged a latency of only 34millisecs and consumed power at less than 2.5W which became very handy in deployment in rural areas. TinyML and its compatibility with cheap edge hardware are scalable and sustainable, which creates an efficient path to modernize the irrigation infrastructure in the developing world. This article does not only highlight the revolutionary nature of embedded AI in precision agriculture, but it precondition the further innovative development of the field of data-driven farm management and autonomous farm management. The one suggested is one of the options that could be implemented to achieve environmental sustainability, enhance the productivity of agriculture, and make smart and local decisions to use water intelligently.","本研究提出了一种基于人工智能的新型智能灌溉装置，采用模拟边缘控制器，旨在解决日益严重的缺水问题和农业操作不当问题。该系统将环境传感、轻量级机器学习算法和微控制器上的执行器相结合，能够实现实时自适应灌溉调度。其关键部分的实现方式包括将树莓派4（Raspberry Pi 4）作为AI推理模块，将STM32微控制器作为执行器控制器，从而提升了响应速度、增强了安全性并降低了能耗，因为无需依赖云计算。利用当地气候和土壤湿度数据，机器学习模型根据温度、湿度、降雨和作物特定阈值等多种参数，确定最佳灌溉时间和时长。整个决策过程自动化并在边缘端执行，使其即使在互联网连接较差或无互联网连接的地区也能不间断运行。通过在半干旱农业区进行广泛的田间测试，验证了所推荐方案的有效性。研究结果表明，资源效率有显著提升，与传统基于时间的灌溉方案相比，用水量减少了高达38%，作物产量增加了20%。此外，该系统平均延迟仅为34毫秒，功耗低于2.5瓦，在农村地区部署中非常实用。TinyML及其与低成本边缘硬件的兼容性具有可扩展性和可持续性，为发展中国家灌溉基础设施的现代化开辟了一条高效路径。本文不仅强调了嵌入式AI在精准农业中的革命性，还为数据驱动农场管理和自主农场管理领域的进一步创新发展奠定了基础。所提出的方案是实现环境可持续性、提高农业生产力以及做出智能本地化决策以合理用水的一种可行选择。","Progress in Electronics and Communication Engineering","https:\u002F\u002Fecejournals.in\u002Findex.php\u002FPECE\u002Farticle\u002Fview\u002F427",74,{"impact":16,"substance":17,"depth":16,"authority":108,"freshness":58,"relevant":20,"comment":523},"边缘AI灌溉系统实测节水38%、增产20%，方法新颖数据扎实，但论文发表于两周后、时效性偏弱，适合作为技术类精选。",[525],{"name":519,"url":520},[25,26,151,444,27],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":530},{"candidates":531,"agricultural_evidence":46},[532,536,540,544,548,552],{"code":31,"score":241,"supported":46,"title":533,"abstract":534,"tags":535},[32],[54,32],[444],{"code":44,"score":19,"supported":46,"title":537,"abstract":538,"tags":539},[],[27,145],[25,27],{"code":75,"score":162,"supported":46,"title":541,"abstract":542,"tags":543},[],[136,138],[26],{"code":169,"score":96,"supported":46,"title":545,"abstract":546,"tags":547},[],[172,173],[],{"code":161,"score":58,"supported":46,"title":549,"abstract":550,"tags":551},[],[398],[],{"code":123,"score":20,"supported":13,"title":553,"abstract":554,"tags":555},[],[],[151],[557,558],"Raspberry Pi STM32 智能灌溉","TinyML 边缘计算 节水灌溉","RaspberryPiSTM32智能灌溉-4209","10.31838\u002Fece\u002F03.02.04",{"doi":560,"openalex_id":562,"authors":563,"venue":519,"cited_by_count":20,"oa_url":520,"card":568,"direction":211,"ingested_from":212},"W7219822766",[564,566],{"name":565,"orcid":8},"Fahad Al-Jame",{"name":567,"orcid":8},"Perera Manthila",{"tldr":569,"method":570,"finding":571,"direction":211,"opportunity":572},"提出基于边缘嵌入式控制器的AI智能灌溉系统，实现实时自适应灌溉调度。","Raspberry Pi 4做AI推理、STM32做执行控制，结合环境传感与轻量","半干旱区试验显示节水38%、增产20%，延迟34毫秒、功耗低于2.5W。","可探索TinyML模型在多种作物与土壤气候下的泛化能力及低成本边缘节点组网协同。","2026-10-05T23:30:08.862496Z"]