[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3355":3,"related-3355":119},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":49,"search_phrases":90,"slug":93,"view_count":20,"doi":94,"paper":95,"created_at":118},3355,"AI and Sustainable Agriculture Through Cost–Benefit Analysis of Smart Irrigation Systems","https:\u002F\u002Fdoi.org\u002F10.22004\u002Fag.econ.412813","The advancing role of Artificial Intelligence (AI) and its application in agriculture have disrupted traditional agricultural practices, with smart irrigation systems representing one of the leading technologies enabling sustainable agriculture. Smart irrigation systems utilize real–time data, machine learning algorithms, and predictive analytics to better optimize irrigation water use, limit wasted resources, and improve the yields of crop products. The proposed research will assess the economic and environmental impacts of AI smart irrigation systems with a full costs–benefits analysis. The proposed research considers both the capital cost and operating cost of smart irrigation systems and compares these traditional irrigation practices while also examining the long–term benefits of potential water savings from Smart Irrigation Systems, expanded agricultural production, and reduced human labour. This will give context for measuring the impacts of Smart Irrigation Systems on farm businesses, including both opportunities and barriers to adoption. Additionally, using a formal literature review to lock down existing research and surveys of irrigation farmers to collect a field data set will provide the proposed researchers a collective sample to measure the efficacy of AI smart irrigation systems, identify barriers, compare opportunities, and measure performance under differing climate and soil properties. The research will find high and substantial respective levels of benefits from the implementation of AI–based smart systems, particularly in water–stressed systems with positive impacts on farm profitability, private, and environmental conservation. This research is essential for informing stakeholders of actions and the delivery of AI–enabled solutions in support of more sustainable agricultural practices.","人工智能（Artificial Intelligence, AI）的不断发展及其在农业中的应用已经颠覆了传统的农业实践，其中智能灌溉系统是实现可持续农业的领先技术之一。智能灌溉系统利用实时数据、机器学习算法和预测分析，更好地优化灌溉用水、减少资源浪费并提高作物产量。拟议研究将通过全面的成本效益分析，评估AI智能灌溉系统的经济和环境影响。该研究将综合考虑智能灌溉系统的资本成本和运营成本，并将其与传统灌溉实践进行比较，同时考察智能灌溉系统在潜在节水、扩大农业生产和减少人力劳动方面的长期效益。这将为衡量智能灌溉系统对农场经营的影响提供背景，包括采用的机会和障碍。此外，通过正式文献综述锁定现有研究成果，并对灌溉农户进行调查以收集实地数据集，将为拟议研究者提供一个集体样本，用以衡量AI智能灌溉系统的效能、识别障碍、比较机会，并评估在不同气候和土壤条件下的表现。研究发现，实施基于AI的智能系统可带来显著且可观的效益，尤其是在水资源紧张的地区，对农场盈利能力、私人利益和环境保护均有积极影响。这项研究对于向利益相关者通报行动方案以及推动AI赋能解决方案以支持更可持续的农业实践至关重要。",null,"AgEcon Search (University of Minnesota, USA)","2026-09-23T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,20,13,8,1,"该研究以成本效益分析评估AI智能灌溉的经济与环境效益，方法系统、结论具参考价值，但属学术论文而非政策或产业事件，适合作为智慧农业主题的深度补充。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","智能灌溉","节水农业","成本效益分析",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[38,43],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture",{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":50,"locked":14,"version":51,"method":52,"evidence":53},"classified","2026-10-22.2","rules",{"candidates":54,"agricultural_evidence":57},[55,63,70,77,85],{"code":32,"score":56,"supported":57,"title":58,"abstract":59,"tags":62},19,true,[33],[60,61,33],"灌溉","水资源",[28,29],{"code":39,"score":64,"supported":57,"title":65,"abstract":67,"tags":69},14,[66],"sustainable agriculture",[68,66],"可持续农业",[],{"code":44,"score":13,"supported":57,"title":71,"abstract":72,"tags":76},[],[73,74,75],"机器学习","machine learning","artificial intelligence",[27],{"code":78,"score":79,"supported":57,"title":80,"abstract":81,"tags":84},"H2",6,[],[82,83],"土壤","soil",[],{"code":86,"score":21,"supported":14,"title":87,"abstract":88,"tags":89},"I1",[],[],[26],[91,92],"AI 智能灌溉 成本效益","AgEcon Search 智能灌溉","AI智能灌溉成本效益-3355","10.22004\u002Fag.econ.412813",{"doi":94,"openalex_id":96,"authors":97,"venue":10,"cited_by_count":110,"oa_url":6,"card":111,"direction":115,"ingested_from":117},"W7214115789",[98,100,102,104,106,108],{"name":99,"orcid":9},"Venkata Suman Jami",{"name":101,"orcid":9},"Purushotham Prasad Kalisetti",{"name":103,"orcid":9},"Sampath Dakshina Murthy A.",{"name":105,"orcid":9},"Gurunadha R.",{"name":107,"orcid":9},"Hema Mamidipaka",{"name":109,"orcid":9},"Gurrapu Omprakash",0,{"tldr":112,"method":113,"finding":114,"direction":115,"opportunity":116},"通过成本效益分析评估AI智能灌溉系统的经济与环境影响。","文献综述结合灌溉农户调查数据，进行成本效益分析。","AI智能灌溉在水资源紧张地区效益显著，提升利润并促进环保。","智慧农业 \u002F 农业物联网","可针对不同气候土壤条件，量化AI灌溉的长期采纳障碍与推广机制。","openalex","2026-09-24T23:30:10.558578Z",{"total":79,"page":21,"page_size":79,"items":120},[121,236,306,384,449,526],{"id":122,"title":123,"url":124,"summary":125,"summary_zh":126,"content":9,"source_name":127,"source_url":124,"published_at":128,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":129,"score_detail":130,"sources":133,"tags":135,"primary_topic":139,"secondary_topics":140,"classification":143,"search_phrases":219,"slug":222,"view_count":215,"doi":223,"paper":224,"created_at":235},2518,"AI-Driven Transformation in Sustainable Agriculture: A Systematic Review","https:\u002F\u002Fdoi.org\u002F10.18805\u002Fijare.a-6627","The agriculture sector is facing unprecedented pressure from a rapidly growing global population, erratic weather conditions and declining water availability. Traditional farming methods are increasingly proving insufficient in meeting the rising food demand, necessitating a shift towards technology-driven solutions. This paper provides a critical and systematic review of the application of Artificial Intelligence (AI) in agriculture, analysing its impact on productivity and sustainability, while identifying potential barriers hindering adoption. Employing a Systematic Literature Review approach, this study synthesizes recent research organized around the core domains of agricultural production - crop, soil and nutrient, water and irrigation and crop-protection (pest, disease and weed) management, alongside yield forecasting - examining how data-driven tools such as machine learning, the Internet of Things, robotics and computer vision are applied within each and prioritizing studies that reported agronomically meaningful outcomes such as yield gains and resource-use efficiency. The strongest gains identified across the review fall within three agricultural areas - crop and soil monitoring, yield forecasting and robotic field operations such as targeted detection, weeding and harvesting. The technologies enabling them, chiefly machine-learning models such as convolutional neural networks combined with field and remote sensors, have delivered high in-field precision, with reported object-detection precision of up to 95.78% in field conditions. In practical terms, this translates into more efficient use of water, fertilizer and other inputs and into improved yield and crop quality. The review concludes that prioritizing Explainable AI and interoperable farm systems is central to building farmer trust and safeguarding food security under a changing climate.","农业部门正面临着来自全球人口快速增长、天气条件不稳定以及水资源日益减少的前所未有的压力。传统耕作方法在满足不断增长的粮食需求方面日益显得力不从心，因此有必要转向技术驱动的解决方案。本文对人工智能（Artificial Intelligence, AI）在农业中的应用进行了批判性和系统性的综述，分析了其对生产力和可持续性的影响，同时识别了阻碍其采用的潜在障碍。本研究采用系统性文献综述方法，围绕农业生产的核心领域——作物、土壤与养分、水资源与灌溉、作物保护（病虫害和杂草）管理以及产量预测——对近期研究进行了综合梳理，考察了机器学习、物联网、机器人技术和计算机视觉等数据驱动工具在各领域中的应用方式，并优先关注报告了具有农学意义成果（如产量提升和资源利用效率）的研究。综述中发现的最显著收益集中在三个农业领域——作物与土壤监测、产量预测以及机器人田间作业（如靶向检测、除草和收获）。实现这些收益的技术，主要是卷积神经网络等机器学习模型与田间及遥感传感器的结合，已在田间条件下实现了高精度，所报告的目标检测精度在田间条件下高达95.78%。在实际应用中，这意味着水、肥料及其他投入品的更高效利用，以及产量和作物品质的提升。综述得出结论：优先发展可解释人工智能（Explainable AI）和可互操作的农场系统，对于在气候变化背景下建立农民信任和保障粮食安全至关重要。","Indian Journal of Agricultural Research","2026-09-14T00:00:00Z",76,{"impact":17,"substance":18,"depth":131,"authority":19,"freshness":20,"relevant":21,"comment":132},17,"系统综述梳理AI在作物土壤监测、产量预测与机器人田间作业中的应用成效与推广障碍，结论扎实，对智慧农业方向有参考价值。",[134],{"name":127,"url":124},[26,27,136,137,138],"产量预测","可解释AI","精准农业",{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},[141,142],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":50,"locked":14,"version":51,"method":52,"evidence":144},{"candidates":145,"agricultural_evidence":57},[146,154,159,163,170,177,181,188,195,202,208,214],{"code":44,"score":147,"supported":57,"title":148,"abstract":149,"tags":153},11,[],[150,73,151,74,75,152],"目标检测","计算机视觉","computer vision",[27,137],{"code":32,"score":155,"supported":57,"title":156,"abstract":157,"tags":158},9,[],[60,61,33],[],{"code":39,"score":20,"supported":57,"title":160,"abstract":161,"tags":162},[66],[],[],{"code":164,"score":79,"supported":57,"title":165,"abstract":166,"tags":169},"A8",[],[167,168],"粮食安全","food security",[],{"code":171,"score":79,"supported":57,"title":172,"abstract":173,"tags":176},"F3",[],[174,175],"肥料","fertilizer",[],{"code":78,"score":79,"supported":57,"title":178,"abstract":179,"tags":180},[],[82,83],[],{"code":182,"score":79,"supported":57,"title":183,"abstract":184,"tags":187},"I5",[],[185,186],"物联网","internet of things",[],{"code":189,"score":190,"supported":57,"title":191,"abstract":192,"tags":194},"I3",4,[],[193],"遥感",[136],{"code":196,"score":197,"supported":57,"title":198,"abstract":199,"tags":201},"A7",3,[],[200],"间作",[],{"code":203,"score":197,"supported":57,"title":204,"abstract":205,"tags":207},"F4",[],[206],"除草",[],{"code":209,"score":197,"supported":57,"title":210,"abstract":211,"tags":213},"H7",[],[212],"气候变化",[],{"code":86,"score":215,"supported":14,"title":216,"abstract":217,"tags":218},2,[],[],[26,138],[220,221],"农业人工智能 产量预测 智慧农业 精准农业","农业人工智能 产量预测","农业人工智能产量预测智慧农业精准农业-2518","10.18805\u002Fijare.a-6627",{"doi":223,"openalex_id":225,"authors":226,"venue":127,"cited_by_count":110,"oa_url":124,"card":229,"direction":115,"ingested_from":117},"W7212616413",[227],{"name":228,"orcid":9},"Manish Maan",{"tldr":230,"method":231,"finding":232,"direction":233,"opportunity":234},"系统综述AI在可持续农业中的应用，分析生产力与可持续性影响及推广障碍。","系统文献综述，围绕作物、土壤、水、植保和产量预测梳理ML、IoT、机器人、计算机","作物与土壤监测、产量预测和机器人田间作业收益最大，检测精度达95.78%，需可解释AI与互操作系统。","农业人工智能与决策模型","可解释AI与互操作农场系统在农户信任和气候适应中的作用尚缺实证，是值得深入的研究空白。","2026-09-15T23:30:13.705421Z",{"id":237,"title":238,"url":239,"summary":240,"summary_zh":241,"content":9,"source_name":242,"source_url":239,"published_at":243,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":244,"sources":248,"tags":250,"primary_topic":252,"secondary_topics":253,"classification":255,"search_phrases":283,"slug":286,"view_count":79,"doi":287,"paper":288,"created_at":305},4567,"An embedded deep learning–fuzzy logic framework for intelligent and automated irrigation in smart farming","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-72706-5","Water scarcity and the need to improve resource efficiency have made intelligent and automated irrigation management an important research challenge in smart farming. This study proposes IrrigaDL-FL, a low-cost automated smart irrigation framework based on the internet of things, integrating intelligent data routing with adaptive irrigation decision-making. The proposed framework consists of two complementary components. First, a Deep Learning–Fuzzy Logic (DL-FL) framework is integrated with the Open Shortest Path First (OSPF) protocol to enhance data routing efficiency in the network. The DL-FL framework combines these heterogeneous characteristics to identify suitable next-hop nodes and establish energy-efficient routing paths. Second, irrigation decision-making is performed using a lightweight Convolutional Neural Network–Fuzzy Logic Controller (CNN-FLC). The CNN-FLC processes sensor-derived and crop-related information, including air temperature, relative humidity, soil moisture, evapotranspiration rate, crop growth coefficient, crop water requirement, planting date, cumulative rainfall, recent rainfall, irrigation history, crop growth stage, and the difference between actual and target soil moisture. These inputs are used to characterize crop hydration stress and dynamically determine the appropriate irrigation program and sprinkler activation duration. The proposed IrrigaDL-FL framework is evaluated through simulations with different numbers of sensor nodes over predefined routing rounds and is compared with several existing approaches. The results demonstrate that IrrigaDL-FL achieves superior performance in terms of network lifetime, residual energy, energy variance, number of transmitted packets, and average path length.","水资源短缺与提高资源效率的需求，使智能化和自动化灌溉管理成为智慧农业中的重要研究挑战。本研究提出IrrigaDL-FL，一种基于物联网的低成本自动化智能灌溉框架，将智能数据路由与自适应灌溉决策相结合。该框架由两个互补部分组成。首先，将深度学习-模糊逻辑（DL-FL）框架与开放最短路径优先（OSPF）协议集成，以提高网络中的数据路由效率。DL-FL框架结合这些异构特征来识别合适的下一跳节点并建立节能路由路径。其次，灌溉决策通过轻量级卷积神经网络-模糊逻辑控制器（CNN-FLC）执行。CNN-FLC处理来自传感器的信息和作物相关信息，包括空气温度、相对湿度、土壤湿度、蒸散速率、作物生长系数、作物需水量、种植日期、累计降雨量、近期降雨量、灌溉历史、作物生长阶段以及实际土壤湿度与目标土壤湿度之间的差值。这些输入用于表征作物水分胁迫，并动态确定合适的灌溉方案和喷灌器启动时长。所提出的IrrigaDL-FL框架通过在预定义路由轮次下对不同数量传感器节点进行仿真评估，并与若干现有方法进行比较。结果表明，IrrigaDL-FL在网络寿命、剩余能量、能量方差、传输数据包数量和平均路径长度方面均取得了更优性能。","Scientific Reports","2026-10-08T00:00:00Z",{"impact":245,"substance":246,"depth":17,"authority":64,"freshness":20,"relevant":21,"comment":247},16,21,"提出低成本物联网智能灌溉框架，融合深度学习与模糊逻辑优化路由与灌溉决策，方法新颖、仿真验证充分，对智慧农业节水研究有参考价值。",[249],{"name":242,"url":239},[26,27,251,28,29],"农业物联网",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[254],{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},{"status":50,"locked":14,"version":51,"method":52,"evidence":256},{"candidates":257,"agricultural_evidence":57},[258,263,270,275,279],{"code":32,"score":56,"supported":57,"title":259,"abstract":260,"tags":262},[33],[60,61,33,261],"evapotranspiration",[28,29],{"code":44,"score":264,"supported":57,"title":265,"abstract":267,"tags":269},15,[266],"deep learning",[268,266],"深度学习",[27],{"code":182,"score":271,"supported":57,"title":272,"abstract":273,"tags":274},7,[],[185,186],[251],{"code":78,"score":79,"supported":57,"title":276,"abstract":277,"tags":278},[],[82,83],[],{"code":86,"score":190,"supported":57,"title":280,"abstract":281,"tags":282},[],[26],[26],[284,285],"IrrigaDL-FL 智能灌溉","CNN-FLC 灌溉决策","IrrigaDL-FL智能灌溉-4567","10.1038\u002Fs41598-026-72706-5",{"doi":287,"openalex_id":289,"authors":290,"venue":242,"cited_by_count":110,"oa_url":299,"card":300,"direction":115,"ingested_from":117},"W7221040139",[291,293,295,297],{"name":292,"orcid":9},"Ze Luo",{"name":294,"orcid":9},"Zhuo Yang",{"name":296,"orcid":9},"Yi Hu",{"name":298,"orcid":9},"Jun Wang","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-72706-5_reference.pdf",{"tldr":301,"method":302,"finding":303,"direction":115,"opportunity":304},"提出IrrigaDL-FL低成本物联网智能灌溉框架，融合深度学习与模糊逻辑优化路由和灌溉决策。","用CNN-模糊逻辑控制器处理多源传感器与作物数据，结合OSPF协议做节能路由。","在仿真中网络寿命、剩余能量、能耗方差、传输包数和平均路径长度均优于现有方法。","可探索真实农田部署下多作物、多气候场景的泛化能力与边缘计算资源约束优化。","2026-10-10T23:30:11.306685Z",{"id":307,"title":308,"url":309,"summary":310,"summary_zh":311,"content":9,"source_name":312,"source_url":309,"published_at":313,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":314,"score_detail":315,"sources":318,"tags":320,"primary_topic":321,"secondary_topics":322,"classification":328,"search_phrases":361,"slug":364,"view_count":215,"doi":365,"paper":366,"created_at":383},2926,"An artificial intelligence-based stacking ensemble framework for smart irrigation pump control using IoT sensor data","https:\u002F\u002Fdoi.org\u002F10.11591\u002Fijece.v16i5.pp2652-2663","Efficient water management is essential for sustainable agricultural production, particularly in arid and semi-arid regions where water resources are limited. Machine-learning-based irrigation systems can support automated pump-operation decisions using environmental and soil-related sensor data. However, most previous studies have focused on individual machine-learning models, while the application of stacked ensembles to binary irrigation pump-status prediction remains relatively limited. This study proposes a stacking-based framework for predicting irrigation pump operation in an ON\u002FOFF classification setting. The framework uses environmental and soil-related variables, including soil moisture, temperature, humidity, and a numerical time-related feature. Random forest (RF), extreme gradient boosting (XGBoost), and multilayer perceptron models were trained as base learners, and their out-of-fold (OOF) predictions were combined using a logistic-regression meta-learner. The models were evaluated on a held-out test set using accuracy, precision, recall, specificity, and F1-score. The individual models achieved accuracies ranging from 97.34% to 99.95%, with XGBoost providing the best individual performance. The proposed stacking ensemble achieved 99.97% accuracy, 99.94% precision, 100.00% recall, 99.94% specificity, and a 99.97% F1-score. Compared with XGBoost, the ensemble further refined predictive performance, improving accuracy by 0.02 percentage points and F1-score by 0.01 percentage points while achieving complete elimination of false negatives (100.00% recall). These results demonstrate the potential of stacked ensemble learning to improve binary pump-operation prediction and support data-driven irrigation management in water-limited environments.","高效的水资源管理对可持续农业生产至关重要，尤其是在水资源有限的干旱和半干旱地区。基于机器学习的灌溉系统可以利用环境和土壤相关传感器数据支持自动化水泵运行决策。然而，以往大多数研究集中于单一机器学习模型，而堆叠集成（stacked ensemble）在二元灌溉水泵状态预测中的应用仍相对有限。本研究提出了一种基于堆叠（stacking）的框架，用于在开\u002F关（ON\u002FOFF）分类场景下预测灌溉水泵运行状态。该框架使用环境和土壤相关变量，包括土壤湿度、温度、湿度和一个数值型时间相关特征。随机森林（RF）、极端梯度提升（XGBoost）和多层感知机模型被训练为基学习器，其折外（OOF）预测结果通过逻辑回归元学习器进行组合。模型在留出测试集上使用准确率、精确率、召回率、特异度和F1分数进行评估。单一模型的准确率范围为97.34%至99.95%，其中XGBoost的单一模型性能最佳。所提出的堆叠集成达到了99.97%的准确率、99.94%的精确率、100.00%的召回率、99.94%的特异度和99.97%的F1分数。与XGBoost相比，该集成进一步优化了预测性能，准确率提高了0.02个百分点，F1分数提高了0.01个百分点，同时实现了假阴性的完全消除（100.00%召回率）。这些结果表明，堆叠集成学习在改进二元水泵运行预测和支持水资源受限环境下的数据驱动灌溉管理方面具有潜力。","International Journal of Power Electronics and Drive Systems\u002FInternational Journal of Electrical and Computer Engineering","2026-09-18T00:00:00Z",72,{"impact":316,"substance":246,"depth":131,"authority":19,"freshness":155,"relevant":21,"comment":317},12,"提出基于IoT传感器数据的堆叠集成模型实现灌溉水泵开关预测，准确率达99.97%，方法新颖、结论可靠，但属实验室验证阶段，产业影响有限。",[319],{"name":312,"url":309},[26,27,73,185,28],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[323,324],{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},{"code":182,"slug":325,"name":326,"path":327,"parent_code":48},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":50,"locked":14,"version":51,"method":52,"evidence":329},{"candidates":330,"agricultural_evidence":57},[331,335,339,344,348,353,357],{"code":32,"score":17,"supported":57,"title":332,"abstract":333,"tags":334},[33],[60,61,33],[28],{"code":44,"score":19,"supported":57,"title":336,"abstract":337,"tags":338},[75],[73],[27,73],{"code":182,"score":155,"supported":57,"title":340,"abstract":342,"tags":343},[341],"IoT",[],[185],{"code":78,"score":79,"supported":57,"title":345,"abstract":346,"tags":347},[],[82,83],[],{"code":209,"score":197,"supported":57,"title":349,"abstract":350,"tags":352},[],[351],"干旱",[],{"code":39,"score":197,"supported":57,"title":354,"abstract":355,"tags":356},[],[68],[],{"code":86,"score":21,"supported":14,"title":358,"abstract":359,"tags":360},[],[],[26],[362,363],"智能灌溉 水泵控制 物联网","农业人工智能 智慧农业 智能灌溉 机器学习","智能灌溉水泵控制物联网-2926","10.11591\u002Fijece.v16i5.pp2652-2663",{"doi":365,"openalex_id":367,"authors":368,"venue":312,"cited_by_count":110,"oa_url":309,"card":378,"direction":115,"ingested_from":117},"W7213546497",[369,372,375],{"name":370,"orcid":371},"Sarra Gourari","https:\u002F\u002Forcid.org\u002F0009-0005-1846-0886",{"name":373,"orcid":374},"Wafa Difallah","https:\u002F\u002Forcid.org\u002F0000-0002-6181-0395",{"name":376,"orcid":377},"Belkacem Draoui","https:\u002F\u002Forcid.org\u002F0000-0001-5490-3991",{"tldr":379,"method":380,"finding":381,"direction":115,"opportunity":382},"提出基于堆叠集成学习的灌溉泵ON\u002FOFF预测框架，用物联网传感器数据实现智能灌溉控制。","用RF、XGBoost、MLP作基学习器，逻辑回归元学习器融合OOF预测，基于土","堆叠集成达99.97%准确率和100%召回率，完全消除假阴性，优于单一XGBoost模型。","可探索多模态传感器融合与在线增量学习，提升堆叠集成在不同作物和气候区的泛化与实时部署能力。","2026-09-19T23:30:11.009076Z",{"id":385,"title":386,"url":387,"summary":388,"summary_zh":389,"content":9,"source_name":390,"source_url":387,"published_at":391,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":392,"score_detail":393,"sources":395,"tags":397,"primary_topic":400,"secondary_topics":401,"classification":403,"search_phrases":431,"slug":434,"view_count":197,"doi":435,"paper":436,"created_at":448},1986,"AI-Enabled Precision Irrigation for Water-Efficient and Sustainable Agriculture","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.84732","The diminishing availability of water for agriculture, sub optimal irrigation methods, climate uncertainty and rising food demands have led to a demand for intelligent and sustainable solutions to water management problems. This research introduces an AI Driven Arduino and Raspberry Pi irrigation system that maximizes agricultural water use by real time sensing, intelligent decision making, and automated irrigation control. The proposed system comprises soil moisture, soil temperature, soil relative humidity, rainfall, and water level sensors to continuously monitor the field conditions. Arduino is used for interfacing with sensors and actuators, Raspberry Pi for data processing, AI based analysis, monitoring and data logging. The sensor data collected is preprocessed and analyzed with an AI\u002FML model to calculate the irrigation needs depending on soil and environmental conditions. The water pump or irrigation valve is automatically turned on or off with a relay mechanism, based on the decision generated. The experimental results show the effectiveness of the proposed method in reducing unnecessary irrigation and the utilization of the resources. With a representative prototype set of results, the AI model was able to identify 94.2% of the events correctly, whereas the automated system was able to detect around 98.6% of events, with a mean response time of 2.4 seconds. About 35% less water was used than traditional irrigation methods and still kept the soil moisture level appropriate. The proposed system uses minimal manual intervention, it facilitates real time monitoring and offers adaptive irrigation management. The research shows the potential of the integration of artificial intelligence with low cost embedded platforms to create scalable, water efficient and sustainable smart agriculture solutions.","农业可用水资源的日益减少、灌溉方法的不尽完善、气候的不确定性以及不断增长的粮食需求，催生了对水资源管理问题智能化、可持续解决方案的需求。本研究提出了一种基于人工智能驱动的Arduino与Raspberry Pi灌溉系统，通过实时感知、智能决策和自动化灌溉控制，最大化农业用水效率。该系统包含土壤湿度、土壤温度、土壤相对湿度、降雨量及水位传感器，以持续监测田间状况。其中，Arduino用于连接传感器和执行器，Raspberry Pi则负责数据处理、基于人工智能的分析、监控及数据记录。采集到的传感器数据经过预处理，并利用AI\u002FML模型进行分析，根据土壤及环境条件计算灌溉需求。基于生成的决策，通过继电器机制自动开启或关闭水泵或灌溉阀门。实验结果表明，该方法在减少不必要灌溉及资源利用方面具有显著效果。以代表性原型测试结果为例，AI模型能够正确识别94.2%的事件，而自动化系统可检测约98.6%的事件，平均响应时间为2.4秒。与传统灌溉方法相比，用水量减少了约35%，同时仍能保持适宜的土壤湿度水平。该系统所需人工干预极少，支持实时监控，并提供适应性灌溉管理。本研究展示了将人工智能与低成本嵌入式平台相结合，以构建可扩展、节水且可持续的智慧农业解决方案的潜力。","International Journal for Research in Applied Science and Engineering Technology","2026-09-08T00:00:00Z",66,{"impact":17,"substance":18,"depth":245,"authority":13,"freshness":215,"relevant":21,"comment":394},"研究展示AI与低成本硬件结合的精准灌溉系统，节水35%，具实践价值，但发表于普通期刊且时效性低。",[396],{"name":390,"url":387},[26,27,398,399,29],"传感器","精准灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[402],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":50,"locked":14,"version":51,"method":52,"evidence":404},{"candidates":405,"agricultural_evidence":57},[406,410,414,419,423,427],{"code":32,"score":56,"supported":57,"title":407,"abstract":408,"tags":409},[33],[60,61,33],[399,29],{"code":39,"score":20,"supported":57,"title":411,"abstract":412,"tags":413},[66],[],[],{"code":86,"score":271,"supported":57,"title":415,"abstract":416,"tags":418},[],[26,417],"smart agriculture",[26],{"code":78,"score":79,"supported":57,"title":420,"abstract":421,"tags":422},[],[82,83],[],{"code":44,"score":190,"supported":57,"title":424,"abstract":425,"tags":426},[],[75],[27],{"code":182,"score":21,"supported":14,"title":428,"abstract":429,"tags":430},[],[],[398],[432,433],"农业人工智能 智慧农业 精准灌溉 节水农业","农业人工智能 智慧农业","农业人工智能智慧农业精准灌溉节水农业-1986","10.22214\u002Fijraset.2026.84732",{"doi":435,"openalex_id":437,"authors":438,"venue":390,"cited_by_count":110,"oa_url":387,"card":443,"direction":115,"ingested_from":117},"W7211968732",[439,441],{"name":440,"orcid":9},"Priyadharshini V",{"name":442,"orcid":9},"Hemapriya",{"tldr":444,"method":445,"finding":446,"direction":115,"opportunity":447},"提出AI驱动的Arduino和树莓派灌溉系统，实现实时监测与自动灌溉，节水35%。","集成多种传感器，Arduino采集，树莓派运行AI\u002FML模型决策，继电器控制灌溉","系统事件识别准确率94.2%，自动检测98.6%，响应2.4秒，节水35%且维持土壤湿度。","可探索AI模型在不同作物和气候下的泛化性，以及多节点协同和能耗优化，提升系统可扩展性。","2026-09-09T23:30:08.891805Z",{"id":450,"title":451,"url":452,"summary":453,"summary_zh":9,"content":9,"source_name":454,"source_url":452,"published_at":455,"category":12,"cover_url":9,"hotness":456,"is_selected":14,"score":457,"score_detail":458,"sources":461,"tags":471,"primary_topic":473,"secondary_topics":474,"classification":477,"search_phrases":506,"slug":508,"view_count":190,"doi":509,"paper":510,"created_at":525},1249,"Enhancing water management with intelligent irrigation systems using edge computing, machine learning and IoT","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1827416","Agriculture is responsible for about 70% of freshwater withdrawals, and water scarcity and inefficient methods of water use remain threats to food security. The traditional way of irrigation causes more water loss and requires innovative solutions to optimize water distribution. This research provides an overview of an intelligent irrigation system, which combines the Internet of Things (IoT), machine learning (ML), and edge computing to optimize water usage and improve crop production. The system uses IoT sensors and edge devices to collect data on the environment and estimate the need for irrigation in real-time. It combines machine learning with system components like the Soil Monitoring Unit (SMU) and Water Management Unit (WMU) to provide scalable, real-time and efficient irrigation management. It is deployed locally at the edge, without the need for cloud computing, has the potential to integrate multi-sensor fusion for storm detection, and offers multiple ML models in a modular, low cost system that can be adapted to various agricultural settings. To optimize irrigation schedules, six supervised machine-learning models were used, such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD) and Multi-Layer Perceptron (MLP). DT and RF had the highest classification accuracy of 93% and R 2 regression scores of 0.98 respectively, indicating high prediction reliability. However, Support Vector Machine (SVM) did not do that well, with an accuracy percentage of 58% and an R 2 score of 0.18. Although more complex, RF’s accuracy is a great option for larger applications and DT balances efficiency and scalability. The findings corroborate the utility of water efficient irrigation system and its prospect in augmenting productivity of agriculture. Future work will include expanding the field trials and developing hybrid models to further improve adaptability of the system in different agricultural environments. The findings of this research can offer understanding for sustainable farming practices, that are essential when it comes to water scarcity and food security in the global context.","Frontiers in Sustainable Food Systems","2026-08-25T00:00:00Z",55,74,{"impact":17,"substance":459,"depth":17,"authority":19,"freshness":197,"relevant":21,"comment":460},22,"研究提出结合IoT、ML与边缘计算的智能灌溉系统，DT和RF模型精度高，对节水农业有实质参考价值。",[462,463,466,468],{"name":454,"url":452},{"name":464,"url":465},"Zenodo (CERN European Organization for Nuclear Research)","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790399",{"name":464,"url":467},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22790398",{"name":469,"url":470},"Journal of Animal Science","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjas\u002Fskag311",[26,27,472,185,28],"边缘计算",{"code":182,"slug":325,"name":326,"path":327,"parent_code":48},[475,476],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},{"status":50,"locked":14,"version":51,"method":52,"evidence":478},{"candidates":479,"agricultural_evidence":57},[480,486,490,494,498,502],{"code":182,"score":481,"supported":57,"title":482,"abstract":484,"tags":485},27,[341,483],"edge computing",[186,341,483],[185,472],{"code":32,"score":316,"supported":57,"title":487,"abstract":488,"tags":489},[33],[33],[28],{"code":44,"score":316,"supported":57,"title":491,"abstract":492,"tags":493},[74],[74],[27],{"code":164,"score":197,"supported":57,"title":495,"abstract":496,"tags":497},[],[168],[],{"code":78,"score":197,"supported":57,"title":499,"abstract":500,"tags":501},[],[83],[],{"code":86,"score":21,"supported":14,"title":503,"abstract":504,"tags":505},[],[],[26],[507,433],"农业人工智能 智慧农业 智能灌溉 边缘计算","农业人工智能智慧农业智能灌溉边缘计算-1249","10.3389\u002Ffsufs.2026.1827416",{"doi":509,"openalex_id":511,"authors":512,"venue":454,"cited_by_count":110,"oa_url":519,"card":520,"direction":115,"ingested_from":117},"W7204189894",[513,516],{"name":514,"orcid":515},"Mohammed Sulaiman Mustafa","https:\u002F\u002Forcid.org\u002F0000-0002-2920-9428",{"name":517,"orcid":518},"Hakan Kutucu","https:\u002F\u002Forcid.org\u002F0000-0001-7144-7246","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1827416\u002Fpdf",{"tldr":521,"method":522,"finding":523,"direction":115,"opportunity":524},"提出结合IoT、机器学习和边缘计算的智能灌溉系统，优化用水并提高作物产量。","使用IoT传感器和边缘设备，结合六种监督学习模型（如DT、RF）进行实时灌溉管理","决策树和随机森林表现最佳，准确率93%，R²为0.98，优于其他模型。","可探索混合模型和扩展田间试验，提高系统在不同农业环境中的适应性。","2026-09-01T04:03:09.639788Z",{"id":527,"title":528,"url":529,"summary":530,"summary_zh":531,"content":9,"source_name":464,"source_url":529,"published_at":532,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":533,"score_detail":534,"sources":536,"tags":538,"primary_topic":542,"secondary_topics":543,"classification":545,"search_phrases":565,"slug":568,"view_count":110,"doi":569,"paper":570,"created_at":582},5591,"Hierarchical Conformal Risk Control for Artificial Intelligence Agriculture Irrigation Scheduling Under Unmodeled Infiltration: A Computer Safety-Guarantee Evaluation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23543665","Irrigation scheduling algorithms that rely on soil-moisture forecasts are vulnerable to unmodeled infiltration, the process by which water bypasses the root zone through preferential pathways that resistance-based soil models cannot represent. The framework is evaluated through a replication-based design study that characterises the operating characteristics of the before-after-control-impact estimator used to audit scheduler performance, under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64. Power was estimated across 2000 design cells spanning cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 54 clusters are required to detect a 5-percentage-point effect at 80% power, and that the minimum detectable effect declines steeply as cluster count rises from 8 to 32. The contribution is a formal safety guarantee for irrigation decision support that remains valid when the infiltration model is wrong, together with an audit design that makes the guarantee empirically checkable.","依赖土壤湿度预报的灌溉调度算法容易受到未建模入渗的影响，即水分通过优先路径绕过根区的过程，而基于阻力的土壤模型无法表征这一过程。该框架通过一项基于重复的设计研究进行评估，该研究刻画了用于审计调度器性能的前后对照影响估计量的运行特性，其所依据的声明方差结构为：组间标准差6.0、残差标准差4.5、组内相关系数0.64。功效估计覆盖2000个设计单元，涵盖聚类数8、16、24和32以及效应量3、5和8个百分点。分析表明，在80%功效下检测5个百分点效应需要54个聚类，且最小可检测效应随聚类数从8增至32而急剧下降。本文的贡献在于为灌溉决策支持提供了一种正式的安全保证，该保证在入渗模型错误时仍然有效，同时提出了一种使该保证可经实证检验的审计设计。","2026-10-21T00:00:00Z",69,{"impact":20,"substance":459,"depth":17,"authority":316,"freshness":155,"relevant":21,"comment":535},"该研究提出在入渗模型失配条件下仍有效的灌溉决策安全保证框架，并给出可审计的试验设计，方法新颖、结论可靠，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[537],{"name":464,"url":529},[26,27,539,540,541],"灌溉决策","安全保证","试验设计",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[544],{"code":44,"slug":45,"name":46,"path":47,"parent_code":48},{"status":50,"locked":14,"version":51,"method":52,"evidence":546},{"candidates":547,"agricultural_evidence":57},[548,552,557,561],{"code":32,"score":264,"supported":57,"title":549,"abstract":550,"tags":551},[33],[60,33],[539],{"code":44,"score":316,"supported":57,"title":553,"abstract":554,"tags":556},[75],[555],"decision support",[27],{"code":78,"score":79,"supported":57,"title":558,"abstract":559,"tags":560},[],[82,83],[],{"code":86,"score":21,"supported":14,"title":562,"abstract":563,"tags":564},[],[],[26],[566,567],"灌溉调度 安全保证","农业人工智能 安全保证 智慧农业 灌溉决策","灌溉调度安全保证-5591","10.5281\u002Fzenodo.23543665",{"doi":569,"openalex_id":571,"authors":572,"venue":464,"cited_by_count":110,"oa_url":529,"card":577,"direction":233,"ingested_from":117},"W7223131205",[573,575],{"name":574,"orcid":9},"Nassima Benkhelifa",{"name":576,"orcid":9},"Yacine Aït Ahmed",{"tldr":578,"method":579,"finding":580,"direction":233,"opportunity":581},"提出分层保形风险控制，为未建模入渗下的灌溉调度提供安全保证。","分层保形风险控制与基于重复设计的BACI审计，2000个设计单元功效分析。","检测5个百分点效应需54个集群，最小可检测效应随集群数增加陡降。","可将保形风险控制扩展到其他未建模扰动下的农业决策安全保证与审计设计。","2026-10-22T11:21:10.283561Z"]