[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4913":3,"related-4913":95},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":11,"published_at":12,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":16,"score_detail":17,"sources":25,"tags":27,"primary_topic":33,"secondary_topics":39,"classification":40,"search_phrases":69,"slug":72,"view_count":23,"doi":73,"paper":74,"created_at":94},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。",null,"Lincoln (University of Nebraska)","https:\u002F\u002Fdigitalcommons.unl.edu\u002Fageconworkpap\u002F49","2026-10-13T00:00:00Z","论文",10,false,75,{"impact":18,"substance":19,"depth":20,"authority":21,"freshness":22,"relevant":23,"comment":24},16,21,17,13,8,1,"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。",[26],{"name":10,"url":11},[28,29,30,31,32],"智慧农业","精准农业","农业技术采纳","灌溉农业","玉米大豆",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":41,"locked":15,"version":42,"method":43,"evidence":44},"classified","2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":48},[46,53,59,64],{"code":47,"score":18,"supported":48,"title":49,"abstract":51,"tags":52},"I1",true,[50],"precision agriculture",[29,50],[28,29],{"code":34,"score":54,"supported":48,"title":55,"abstract":56,"tags":58},7,[],[57,35],"灌溉",[31],{"code":60,"score":23,"supported":15,"title":61,"abstract":62,"tags":63},"A3",[],[],[32],{"code":65,"score":23,"supported":15,"title":66,"abstract":67,"tags":68},"G1",[],[],[30],[70,71],"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":73,"openalex_id":75,"authors":76,"venue":10,"cited_by_count":84,"oa_url":85,"card":86,"direction":92,"ingested_from":93},"W2326051080",[77,79,81],{"name":78,"orcid":9},"Michael H Castle",{"name":80,"orcid":9},"Bradley Lubben",{"name":82,"orcid":83},"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":87,"method":88,"finding":89,"direction":90,"opportunity":91},"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。","农业人工智能与决策模型","openalex","2026-10-14T23:30:55.751323Z",{"total":96,"page":23,"page_size":96,"items":97},6,[98,214,280,347,439,500],{"id":99,"title":100,"url":101,"summary":102,"summary_zh":103,"content":9,"source_name":104,"source_url":101,"published_at":105,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":106,"score_detail":107,"sources":112,"tags":114,"primary_topic":118,"secondary_topics":124,"classification":131,"search_phrases":188,"slug":191,"view_count":192,"doi":193,"paper":194,"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":108,"substance":109,"depth":108,"authority":21,"freshness":110,"relevant":23,"comment":111},18,22,9,"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。",[113],{"name":104,"url":101},[28,115,116,29,117],"农业人工智能","物联网","气候韧性",{"code":119,"slug":120,"name":121,"path":122,"parent_code":123},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[125,130],{"code":126,"slug":127,"name":128,"path":129,"parent_code":123},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":41,"locked":15,"version":42,"method":43,"evidence":132},{"candidates":133,"agricultural_evidence":48},[134,145,149,156,161,168,175,182],{"code":119,"score":108,"supported":48,"title":135,"abstract":137,"tags":144},[136],"artificial intelligence",[138,139,140,141,142,143],"深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[115],{"code":47,"score":18,"supported":48,"title":146,"abstract":147,"tags":148},[50],[29,50],[28,29],{"code":126,"score":14,"supported":48,"title":150,"abstract":151,"tags":155},[],[116,152,153,154],"边缘计算","IoT","edge computing",[116],{"code":34,"score":110,"supported":48,"title":157,"abstract":158,"tags":160},[],[57,159,35],"水资源",[],{"code":162,"score":54,"supported":48,"title":163,"abstract":164,"tags":167},"H7",[],[165,166],"气候变化","climate change",[117],{"code":169,"score":96,"supported":48,"title":170,"abstract":171,"tags":174},"H2",[],[172,173],"土壤","soil",[],{"code":176,"score":177,"supported":48,"title":178,"abstract":179,"tags":181},"A7",3,[],[180],"间作",[],{"code":183,"score":177,"supported":48,"title":184,"abstract":185,"tags":187},"F3",[],[186],"养分管理",[],[189,190],"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",0,"10.53982\u002Fajerd.2026.01.14-j",{"doi":193,"openalex_id":195,"authors":196,"venue":104,"cited_by_count":192,"oa_url":101,"card":207,"direction":212,"ingested_from":93},"W7223121456",[197,200,202,204],{"name":198,"orcid":199},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":201,"orcid":9},"Oluwadare OYEBODE",{"name":203,"orcid":9},"Iretiayo Adelaiye",{"name":205,"orcid":206},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":208,"method":209,"finding":210,"direction":92,"opportunity":211},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:18.151376Z",{"id":215,"title":216,"url":217,"summary":218,"summary_zh":219,"content":9,"source_name":220,"source_url":217,"published_at":221,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":222,"score_detail":223,"sources":226,"tags":228,"primary_topic":231,"secondary_topics":232,"classification":234,"search_phrases":256,"slug":259,"view_count":192,"doi":260,"paper":261,"created_at":279},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":224,"substance":108,"depth":18,"authority":21,"freshness":22,"relevant":23,"comment":225},12,"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。",[227],{"name":220,"url":217},[28,116,29,229,230],"节水灌溉","LoRa",{"code":126,"slug":127,"name":128,"path":129,"parent_code":123},[233],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":41,"locked":15,"version":42,"method":43,"evidence":235},{"candidates":236,"agricultural_evidence":48},[237,242,247,251],{"code":126,"score":108,"supported":48,"title":238,"abstract":239,"tags":241},[153],[116,240,153],"internet of things",[116],{"code":34,"score":243,"supported":48,"title":244,"abstract":245,"tags":246},15,[35],[57,35],[229],{"code":169,"score":96,"supported":48,"title":248,"abstract":249,"tags":250},[],[172,173],[],{"code":47,"score":252,"supported":15,"title":253,"abstract":254,"tags":255},2,[],[],[28,29],[257,258],"ESP32 LoRa 智能灌溉","SX1278 精准灌溉","ESP32LoRa智能灌溉-5378","10.3390\u002Felectronics15204831",{"doi":260,"openalex_id":262,"authors":263,"venue":220,"cited_by_count":192,"oa_url":217,"card":274,"direction":212,"ingested_from":93},"W7222959710",[264,266,268,271],{"name":265,"orcid":9},"Hanfoug Salah",{"name":267,"orcid":9},"Riad Saidi",{"name":269,"orcid":270},"Abdelaziz Aouiche","https:\u002F\u002Forcid.org\u002F0000-0003-1378-7941",{"name":272,"orcid":273},"Yazeed Alkhrijah","https:\u002F\u002Forcid.org\u002F0000-0001-7352-003X",{"tldr":275,"method":276,"finding":277,"direction":212,"opportunity":278},"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":281,"title":282,"url":283,"summary":284,"summary_zh":285,"content":9,"source_name":286,"source_url":283,"published_at":287,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":288,"score_detail":289,"sources":291,"tags":293,"primary_topic":296,"secondary_topics":297,"classification":299,"search_phrases":330,"slug":333,"view_count":192,"doi":334,"paper":335,"created_at":346},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":224,"substance":19,"depth":20,"authority":21,"freshness":110,"relevant":23,"comment":290},"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。",[292],{"name":286,"url":283},[28,115,294,29,295],"产量预测","灌溉决策",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[298],{"code":119,"slug":120,"name":121,"path":122,"parent_code":123},{"status":41,"locked":15,"version":42,"method":43,"evidence":300},{"candidates":301,"agricultural_evidence":48},[302,306,311,315,319,325],{"code":34,"score":108,"supported":48,"title":303,"abstract":304,"tags":305},[35],[57,159,35],[295],{"code":119,"score":14,"supported":48,"title":307,"abstract":308,"tags":310},[],[139,141,309],"decision support",[115],{"code":47,"score":22,"supported":48,"title":312,"abstract":313,"tags":314},[],[29,50],[28,29],{"code":169,"score":177,"supported":48,"title":316,"abstract":317,"tags":318},[],[172],[],{"code":320,"score":177,"supported":48,"title":321,"abstract":322,"tags":324},"H8",[],[323],"可持续农业",[],{"code":326,"score":23,"supported":15,"title":327,"abstract":328,"tags":329},"I3",[],[],[294],[331,332],"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":334,"openalex_id":336,"authors":337,"venue":286,"cited_by_count":192,"oa_url":283,"card":341,"direction":212,"ingested_from":93},"W7221521521",[338],{"name":339,"orcid":340},"Artughrul Gayibov","https:\u002F\u002Forcid.org\u002F0009-0009-7349-0286",{"tldr":342,"method":343,"finding":344,"direction":92,"opportunity":345},"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":348,"title":349,"url":350,"summary":351,"summary_zh":352,"content":9,"source_name":353,"source_url":350,"published_at":354,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":222,"score_detail":355,"sources":358,"tags":360,"primary_topic":364,"secondary_topics":365,"classification":366,"search_phrases":401,"slug":404,"view_count":252,"doi":405,"paper":406,"created_at":438},4602,"Phenology-driven multisensor unsupervised crop mapping in data-scarce irrigated agriculture: a case of Adea District in Ethiopia","https:\u002F\u002Fdoi.org\u002F10.2166\u002Fwpt.2026.492","ABSTRACT Accurate irrigation planning, water allocation, and agricultural productivity assessment are frequently constrained in data-scare regions where reliable ground-based crop information and spatial coverage data are limited. This study addresses these gaps using a phenology-driven, multisensor unsupervised crop-mapping framework implemented within Google Earth Engine (GEE). The approach integrates monthly normalized difference vegetation index (NDVI) profiles from Sentinel-2, Sentinel-1VV backscatter, and digital elevation model–derived terrain slope into unsupervised K-means clustering algorithm. Adea District, Ethiopia, served as a demonstration site to evaluate and interpret the phenological signatures of key irrigated crops. The identified crop types were mapped to estimate spatial coverage and compared with the ground truth observations of irrigated wheat and tomato fields. The approach successfully distinguished irrigated wheat and tomato fields with an overall classification accuracy of 91%, a Kappa coefficient of 0.82, and estimated cultivated areas of 666.93 and 812.35 ha, respectively. Combining phenological profiles with multisensor satellite observations significantly enhance unsupervised classification performance in heterogeneous, smallholder-dominated agricultural landscapes. The cloud-based framework offers a computationally scalable and cost-effective solution for crop mapping, and water productivity monitoring in data-scarce agricultural regions.","在数据稀缺地区，可靠的基于地面作物信息和空间覆盖数据往往有限，精准灌溉规划、水资源分配和农业生产力评估因此常受制约。本研究针对这些不足，采用了一种由物候驱动、多传感器协同的无监督作物制图框架，并在Google Earth Engine（GEE）平台上实现。该方法将Sentinel-2的月度归一化植被指数（NDVI）时序、Sentinel-1 VV后向散射以及数字高程模型衍生的地形坡度整合到无监督K均值聚类算法中。以埃塞俄比亚Adea区为示范区，评估和解读主要灌溉作物的物候特征。对识别出的作物类型进行制图以估算空间覆盖范围，并与灌溉小麦和番茄田的地面实测数据进行对比。该方法成功区分了灌溉小麦和番茄田，总体分类精度达91%，Kappa系数为0.82，估算种植面积分别为666.93公顷和812.35公顷。将物候特征与多传感器卫星观测相结合，可显著提升异质性小农主导农业景观中的无监督分类性能。该基于云的框架为数据稀缺农业地区的作物制图和水分生产力监测提供了一种计算可扩展且成本效益高的解决方案。","Water Practice & Technology","2026-10-09T00:00:00Z",{"impact":22,"substance":356,"depth":20,"authority":21,"freshness":110,"relevant":23,"comment":357},20,"方法新颖、精度可靠，但属区域案例研究，产业影响有限，可作为技术参考入选。",[359],{"name":353,"url":350},[28,361,362,363,31],"遥感","作物识别","Google Earth Engine",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":15,"version":42,"method":43,"evidence":367},{"candidates":368,"agricultural_evidence":48},[369,373,380,387,393,397],{"code":34,"score":14,"supported":48,"title":370,"abstract":371,"tags":372},[],[57,159,35],[31],{"code":374,"score":96,"supported":48,"title":375,"abstract":376,"tags":379},"A2",[],[377,378],"小麦","wheat",[],{"code":381,"score":96,"supported":48,"title":382,"abstract":383,"tags":386},"B1",[],[384,385],"番茄","tomato",[],{"code":388,"score":177,"supported":48,"title":389,"abstract":390,"tags":392},"G2",[],[391],"smallholder",[],{"code":326,"score":252,"supported":15,"title":394,"abstract":395,"tags":396},[],[],[363,361],{"code":47,"score":23,"supported":15,"title":398,"abstract":399,"tags":400},[],[],[28],[402,403],"埃塞俄比亚 Adea 灌溉作物 遥感","Sentinel-2 NDVI 作物分类","埃塞俄比亚Adea灌溉作物遥感-4602","10.2166\u002Fwpt.2026.492",{"doi":405,"openalex_id":407,"authors":408,"venue":353,"cited_by_count":192,"oa_url":350,"card":432,"direction":90,"ingested_from":93},"W7221136167",[409,412,415,418,421,424,427,429],{"name":410,"orcid":411},"Belete Berhanu Kidanewold","https:\u002F\u002Forcid.org\u002F0000-0002-9876-8549",{"name":413,"orcid":414},"Jemal Mohammed Hassen","https:\u002F\u002Forcid.org\u002F0000-0002-7417-173X",{"name":416,"orcid":417},"Rahel Sintayehu Tessema","https:\u002F\u002Forcid.org\u002F0009-0006-2798-1752",{"name":419,"orcid":420},"Saba Kidane","https:\u002F\u002Forcid.org\u002F0009-0002-7403-9939",{"name":422,"orcid":423},"Mulugeta Melese","https:\u002F\u002Forcid.org\u002F0009-0008-3539-910X",{"name":425,"orcid":426},"Tadesse Shimels","https:\u002F\u002Forcid.org\u002F0009-0009-3351-5952",{"name":428,"orcid":9},"Mussie Alemayehu",{"name":430,"orcid":431},"Daneal Fekersillassie","https:\u002F\u002Forcid.org\u002F0009-0006-6194-0903",{"tldr":433,"method":434,"finding":435,"direction":436,"opportunity":437},"基于物候与多传感器数据，在埃塞俄比亚缺数据灌区实现无监督作物制图。","GEE平台融合Sentinel-2 NDVI、Sentinel-1 VV及DEM","成功区分灌溉小麦和番茄，总体精度91%，Kappa 0.82，面积分别为666.93和812.35公","农业遥感与作物表型","可探索物候特征与多源遥感在缺数据小农灌区的迁移学习及作物水生产力监测。","2026-10-10T23:30:36.165502Z",{"id":440,"title":441,"url":442,"summary":443,"summary_zh":444,"content":9,"source_name":445,"source_url":442,"published_at":446,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":447,"score_detail":448,"sources":450,"tags":452,"primary_topic":455,"secondary_topics":456,"classification":458,"search_phrases":485,"slug":488,"view_count":177,"doi":9,"paper":489,"created_at":499},4577,"Automatic irrigation system-2","https:\u002F\u002Fijerst.org\u002Findex.php\u002Fijerst\u002Farticle\u002Fview\u002F4719","Agriculture is one of the major consumers of freshwater, and inefficient irrigation can result in significant water loss. Traditional irrigation methods often depend on manual observation and fixed watering schedules, which may lead to over-irrigation or under-irrigation. An automatic irrigation system provides a technological solution by monitoring soil moisture and automatically supplying water according to the requirements of the crop. This research paper presents the design and working principle of an automatic irrigation system based on soilmoisture sensing, a microcontroller, a relay, and a water pump. The proposed system continuously measures the moisture level of the soil. When the moisture falls below a predefined threshold, the controller activates the water pump. Once sufficient moisture is restored, the pump is automatically switched off. The system can additionally incorporate Internet of Things (IoT) technology, allowing farmers to monitor soil and environmental conditions remotely through a mobile or web interface. Previous research has demonstrated the potential of sensor-based irrigation automation to improve water-use efficiency. For example, a soil-moisture-based automated basin irrigation system reported irrigation application efficiency of up to 86.6%. I More recent systems combine soil moisture, temperature, humidity, weather information, IoT connectivity, and machine learning to improve irrigation scheduling. Wiley Online Library+1 The proposed system is therefore a low-cost and scalable approach toward precision agriculture, water conservation, reduced labor requirements, and sustainable agricultural production.","农业是淡水的主要消耗领域之一，低效灌溉会造成大量水资源浪费。传统灌溉方法往往依赖人工观察和固定灌溉计划，可能导致过度灌溉或灌溉不足。自动灌溉系统通过监测土壤湿度并根据作物需求自动供水，提供了一种技术解决方案。本文介绍了一种基于土壤湿度传感、微控制器、继电器和水泵的自动灌溉系统的设计和工作原理。该系统持续测量土壤湿度水平。当湿度低于预设阈值时，控制器启动水泵。当湿度恢复到足够水平后，水泵自动关闭。该系统还可集成物联网（IoT）技术，使农民能够通过移动端或网页界面远程监测土壤和环境状况。已有研究表明，基于传感器的灌溉自动化具有提高水资源利用效率的潜力。例如，一项基于土壤湿度的自动化畦灌系统报告称，其灌溉施用效率可达86.6%。近期系统则结合土壤湿度、温度、湿度、天气信息、物联网连接和机器学习来优化灌溉调度。因此，所提出的系统是一种低成本、可扩展的方案，有助于实现精准农业、水资源保护、减少劳动力需求以及可持续农业生产。","International Journal of Engineering Research and Science & Technology","2026-10-07T00:00:00Z",42,{"impact":22,"substance":14,"depth":224,"authority":96,"freshness":96,"relevant":23,"comment":449},"主题契合智慧农业，但属通用性论文介绍，方法无新意、来源权威性一般，不建议进入每日精选。",[451],{"name":445,"url":442},[28,116,29,453,454],"土壤墒情","自动灌溉",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[457],{"code":126,"slug":127,"name":128,"path":129,"parent_code":123},{"status":41,"locked":15,"version":42,"method":43,"evidence":459},{"candidates":460,"agricultural_evidence":48},[461,465,469,473,477,481],{"code":34,"score":20,"supported":48,"title":462,"abstract":463,"tags":464},[35],[57,159,35],[],{"code":126,"score":14,"supported":48,"title":466,"abstract":467,"tags":468},[],[116,240,153],[116],{"code":47,"score":22,"supported":48,"title":470,"abstract":471,"tags":472},[],[29,50],[28,29],{"code":169,"score":54,"supported":48,"title":474,"abstract":475,"tags":476},[],[172,173],[453],{"code":119,"score":96,"supported":48,"title":478,"abstract":479,"tags":480},[],[139,141],[],{"code":320,"score":177,"supported":48,"title":482,"abstract":483,"tags":484},[],[323],[],[486,487],"自动灌溉系统 土壤湿度 物联网","土壤墒情 智慧农业 精准农业 自动灌溉","自动灌溉系统土壤湿度物联网-4577",{"doi":9,"openalex_id":490,"authors":491,"venue":445,"cited_by_count":192,"oa_url":442,"card":494,"direction":212,"ingested_from":93},"W7221285464",[492],{"name":493,"orcid":9},"Pooja Rajendra Sangole,Shravani Datta Dhomane ,Shreyas nivrutti thete ,amol Dinakar laware,Sumit sudan dalu,Kalpesh Gopal Agrawal,Piyush Sitaram Ghore",{"tldr":495,"method":496,"finding":497,"direction":212,"opportunity":498},"设计基于土壤湿度传感、微控制器、继电器和水泵的自动灌溉系统，实现按需供水。","土壤湿度传感器、微控制器、继电器、水泵，可选IoT远程监控。","系统在湿度低于阈值时自动启泵，恢复后停泵，低成本可扩展，提升水分利用效率。","可结合机器学习与气象数据优化灌溉阈值，并开展多作物田间验证与能耗优化。","2026-10-10T23:30:15.132034Z",{"id":501,"title":502,"url":503,"summary":504,"summary_zh":505,"content":9,"source_name":506,"source_url":503,"published_at":507,"category":13,"cover_url":9,"hotness":14,"is_selected":48,"score":508,"score_detail":509,"sources":512,"tags":514,"primary_topic":517,"secondary_topics":523,"classification":525,"search_phrases":558,"slug":561,"view_count":23,"doi":562,"paper":563,"created_at":585},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":108,"substance":109,"depth":108,"authority":510,"freshness":110,"relevant":23,"comment":511},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。",[513],{"name":506,"url":503},[28,115,515,29,516],"智能灌溉","温室种植",{"code":518,"slug":519,"name":520,"path":521,"parent_code":522},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[524],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":41,"locked":15,"version":42,"method":43,"evidence":526},{"candidates":527,"agricultural_evidence":48},[528,532,538,542,546,550,554],{"code":34,"score":108,"supported":48,"title":529,"abstract":530,"tags":531},[35],[57,159,35],[515],{"code":518,"score":243,"supported":48,"title":533,"abstract":535,"tags":537},[534],"greenhouse",[536,534],"温室",[516],{"code":47,"score":22,"supported":48,"title":539,"abstract":540,"tags":541},[],[29,50],[28,29],{"code":169,"score":96,"supported":48,"title":543,"abstract":544,"tags":545},[],[172,173],[],{"code":381,"score":177,"supported":48,"title":547,"abstract":548,"tags":549},[],[384],[],{"code":162,"score":177,"supported":48,"title":551,"abstract":552,"tags":553},[],[165],[],{"code":119,"score":23,"supported":15,"title":555,"abstract":556,"tags":557},[],[],[115],[559,560],"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":562,"openalex_id":564,"authors":565,"venue":506,"cited_by_count":192,"oa_url":503,"card":580,"direction":92,"ingested_from":93},"W7220746994",[566,569,572,574,577],{"name":567,"orcid":568},"Jamel Riahi","https:\u002F\u002Forcid.org\u002F0009-0006-1434-4367",{"name":570,"orcid":571},"Hamza Nasri","https:\u002F\u002Forcid.org\u002F0009-0008-7343-4199",{"name":573,"orcid":9},"Abdelkader Mami",{"name":575,"orcid":576},"Mario Carpentieri","https:\u002F\u002Forcid.org\u002F0000-0001-5165-5873",{"name":578,"orcid":579},"Silvano Vergura","https:\u002F\u002Forcid.org\u002F0000-0002-0826-9237",{"tldr":581,"method":582,"finding":583,"direction":212,"opportunity":584},"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"]