[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2542":3,"related-2542":104},{"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":38,"search_phrases":80,"slug":83,"view_count":20,"doi":84,"paper":85,"created_at":103},2542,"Discrete Element Method-Based Modeling and Application of Agricultural Materials: A Review","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16181958","Discrete element method (DEM) has become an essential tool for analyzing particle-scale behavior and complex interactions in agricultural materials and machinery. This review synthesizes recent advances in DEM-based modeling and applications in agricultural engineering. It systematically examines particle modeling strategies, contact model development, and parameter calibration frameworks for representative materials such as soil, seeds, fertilizers, stalks, and roots. The evolution from simple spherical approximations to multi-sphere, bonded, and 3D scan-based reconstructions is highlighted, along with the shift from classical Hertz–Mindlin models to cohesive, elasto-plastic, and fracture-capable formulations. Calibration methods are traced from empirical assignment to systematic frameworks that integrate design of experiments, response surface methodology, and machine-learning-assisted inverse analysis. These advances in fidelity and accuracy have enabled extensive DEM applications in tillage, seeding, fertilization, and harvesting, with emphasis on equipment optimization, mechanism analysis, and performance prediction. Despite the progress, challenges remain in model standardization, parameter transferability, computational cost, and multiphysics coupling. Future work points to unified modeling frameworks, standardized databases, real-time simulation, and integration with artificial intelligence to support digital agriculture and intelligent machinery. This review aims to serve as a reference for high-fidelity DEM modeling and for advancing the digital transformation of agricultural engineering.","离散元法（DEM）已成为分析农业物料与机械中颗粒尺度行为及复杂相互作用的重要工具。本文综述了基于DEM的建模与农业工程应用的最新进展，系统考察了土壤、种子、肥料、茎秆和根系等代表性物料的颗粒建模策略、接触模型开发及参数标定框架。重点阐述了从简单球形近似到多球体、粘结型及基于三维扫描的重构方法的演变，以及从经典Hertz–Mindlin模型向黏聚、弹塑性及可断裂本构模型的转变。标定方法从经验赋值发展为集成试验设计、响应面法和机器学习辅助反分析的系统框架。这些在保真度和精度方面的进步使得DEM在耕整、播种、施肥和收获等领域得到广泛应用，重点涉及装备优化、机理分析和性能预测。尽管取得了进展，但在模型标准化、参数可迁移性、计算成本和多物理场耦合方面仍存在挑战。未来工作指向统一建模框架、标准化数据库、实时仿真以及与人工智能的融合，以支撑数字农业和智能机械发展。本文旨在为高保真DEM建模及推进农业工程数字化转型提供参考。",null,"Agriculture","2026-09-12T00:00:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,21,13,6,1,"系统综述离散元法在农业物料建模与装备优化中的进展，方法学价值高，对智能农机与数字农业有参考意义。",[24],{"name":10,"url":6},[26,27,28,29,30],"数字农业","智慧农业","农业人工智能","农业机械","离散元仿真",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,53,60,68,75],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":52},9,true,[],[49,50,51],"施肥","肥料","fertilization",[],{"code":54,"score":55,"supported":46,"title":56,"abstract":57,"tags":59},"I1",8,[],[26,58],"digital agriculture",[26,27],{"code":61,"score":62,"supported":46,"title":63,"abstract":64,"tags":67},"I2",7,[],[65,66],"机器学习","artificial intelligence",[28],{"code":69,"score":20,"supported":46,"title":70,"abstract":71,"tags":74},"H2",[],[72,73],"土壤","soil",[],{"code":76,"score":21,"supported":14,"title":77,"abstract":78,"tags":79},"F5",[],[],[29],[81,82],"农业人工智能 离散元仿真 农业机械 数字农业","农业人工智能 离散元仿真","农业人工智能离散元仿真农业机械数字农业-2542","10.3390\u002Fagriculture16181958",{"doi":84,"openalex_id":86,"authors":87,"venue":10,"cited_by_count":95,"oa_url":6,"card":96,"direction":100,"ingested_from":102},"W7213152558",[88,90,92],{"name":89,"orcid":9},"Xingchi Zhou",{"name":91,"orcid":9},"Yanbin Liu",{"name":93,"orcid":94},"Zhenwei Liang","https:\u002F\u002Forcid.org\u002F0000-0001-6501-4364",0,{"tldr":97,"method":98,"finding":99,"direction":100,"opportunity":101},"综述农业物料离散元建模的粒子模型、接触模型与参数标定进展及应用。","综述离散元粒子建模、接触模型、标定方法及在耕播收中的应用。","建模从球形向多球\u002F键合\u002F3D重建演进，标定转向机器学习辅助反演。","农业人工智能与决策模型","可探索DEM与AI实时仿真、多物理场耦合及标准化参数数据库构建。","openalex","2026-09-15T23:30:36.643241Z",{"total":20,"page":21,"page_size":20,"items":105},[106,190,264,361,425,506],{"id":107,"title":108,"url":109,"summary":110,"summary_zh":111,"content":9,"source_name":112,"source_url":109,"published_at":113,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":114,"score_detail":115,"sources":120,"tags":122,"primary_topic":126,"secondary_topics":127,"classification":138,"search_phrases":168,"slug":171,"view_count":95,"doi":9,"paper":172,"created_at":189},4670,"SMART FERTILIZER DISTRIBUTION SYSTEM: A COMPREHENSIVE REVIEW","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F334","Agriculture is an important sector for food production and economic development, and the efficient use of fertilizers is essential for achieving better crop growth and productivity. Conventional fertilizer distribution methods largely depend on manual application, which can lead to uneven distribution, excessive fertilizer usage, increased labor requirements, and wastage of agricultural resources. The development of automation and Internet of Things (IoT) technologies has provided new opportunities for improving fertilizer distribution and management in agricultural fields. This review paper presents a comprehensive study of Smart Fertilizer Distribution Systems, focusing on the technologies, control methods, communication techniques, and approaches reported in existing research. Various systems using microcontrollers, IoT devices, wireless communication, web-based platforms, sensors, motor or pump control, driver circuits, and solenoid valves are reviewed. The working principles of automated fertilizer distribution systems and their role in controlling fertilizer flow and reducing manual intervention are discussed. Different communication technologies, including Wi-Fi and other wireless methods, are compared based on their applicability, cost, operating range, reliability, and control capability. The review also examines the major advantages and limitations of existing systems, such as automation, remote operation, improved fertilizer management, reduced labor, network dependency, power requirements, sensor accuracy, and system complexity. A comparative analysis of selected research works is presented to identify the techniques used, their contributions, and the limitations that remain to be addressed. The review further identifies research gaps and discusses future developments such as automatic fertilizer scheduling, multi-zone fertilizer distribution, real-time monitoring, cloud-based data storage, artificial intelligence, machine learning, and integration with renewable energy sources. The study concludes that smart fertilizer distribution can improve the efficiency, accuracy, and sustainability of fertilizer application and has significant potential for future precision agriculture applications.","农业是粮食生产和经济发展的重要部门，而肥料的高效利用对于实现作物更好生长和生产力提升至关重要。传统的肥料分配方法在很大程度上依赖人工施用，这可能导致分配不均、肥料过量使用、劳动力需求增加以及农业资源浪费。自动化和物联网（IoT）技术的发展为改善农田肥料分配与管理提供了新的机遇。本文综述对智能肥料分配系统进行了全面研究，重点关注现有研究中报道的技术、控制方法、通信技术和方法。本文回顾了使用微控制器、物联网设备、无线通信、基于网络的平台、传感器、电机或泵控制、驱动电路和电磁阀的各种系统。讨论了自动化肥料分配系统的工作原理及其在控制肥料流量和减少人工干预方面的作用。基于适用性、成本、工作范围、可靠性和控制能力，比较了包括Wi-Fi和其他无线方法在内的不同通信技术。本综述还考察了现有系统的主要优势和局限性，如自动化、远程操作、改善肥料管理、减少劳动力、网络依赖、功率需求、传感器精度和系统复杂性。本文对选定的研究工作进行了比较分析，以识别所采用的技术、其贡献以及仍有待解决的局限性。本综述进一步指出了研究空白，并讨论了未来发展方向，如自动肥料调度、多区域肥料分配、实时监测、基于云的数据存储、人工智能、机器学习以及与可再生能源的集成。该研究得出结论，智能肥料分配可以提高肥料施用的效率、准确性和可持续性，并在未来精准农业应用中具有巨大潜力。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",61,{"impact":116,"substance":117,"depth":118,"authority":116,"freshness":20,"relevant":21,"comment":119},12,16,15,"综述性论文系统梳理智能施肥系统的技术路线与研究空白，对智慧农业领域有参考价值，但属文献综述而非原创突破，时效性一般。",[121],{"name":112,"url":109},[27,28,123,124,125],"精准施肥","农业物联网","智能农机",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[128,133],{"code":61,"slug":129,"name":130,"path":131,"parent_code":132},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":134,"slug":135,"name":136,"path":137,"parent_code":132},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":39,"locked":14,"version":40,"method":41,"evidence":139},{"candidates":140,"agricultural_evidence":46},[141,146,151,158,164],{"code":32,"score":118,"supported":46,"title":142,"abstract":144,"tags":145},[143],"fertilizer",[50,143],[123],{"code":61,"score":13,"supported":46,"title":147,"abstract":148,"tags":150},[],[65,149,66],"machine learning",[28],{"code":134,"score":13,"supported":46,"title":152,"abstract":153,"tags":157},[],[154,155,156],"物联网","internet of things","IoT",[124],{"code":54,"score":62,"supported":46,"title":159,"abstract":160,"tags":163},[],[161,162],"精准农业","precision agriculture",[27],{"code":76,"score":21,"supported":14,"title":165,"abstract":166,"tags":167},[],[],[125],[169,170],"智能施肥系统 物联网","精准施肥 自动化","智能施肥系统物联网-4670",{"doi":9,"openalex_id":173,"authors":174,"venue":112,"cited_by_count":95,"oa_url":109,"card":183,"direction":187,"ingested_from":102},"W7221394563",[175,177,179,181],{"name":176,"orcid":9},"Anjali Manoj Kumbhar",{"name":178,"orcid":9},"Shruti Prashant Kulkarni",{"name":180,"orcid":9},"Snehal Santosh Khune",{"name":182,"orcid":9},"B. B. Godbole",{"tldr":184,"method":185,"finding":186,"direction":187,"opportunity":188},"综述智能施肥系统的技术、控制与通信方法，并指出研究空白与未来方向。","综述基于微控制器、物联网、无线通信、传感器与电磁阀的自动施肥系统研究。","智能施肥可提升施肥效率、精度与可持续性，但存在网络依赖、功耗与传感器精度等局限。","智慧农业 \u002F 农业物联网","可探索AI驱动的自动施肥调度、多区域分配与云边协同的实时精准施肥系统。","2026-10-11T23:30:16.128775Z",{"id":191,"title":192,"url":193,"summary":194,"summary_zh":195,"content":9,"source_name":196,"source_url":193,"published_at":197,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":198,"score_detail":199,"sources":203,"tags":205,"primary_topic":208,"secondary_topics":209,"classification":215,"search_phrases":244,"slug":247,"view_count":248,"doi":249,"paper":250,"created_at":263},3920,"Automated Leaf Color Chart (LCC)-BasedNitrogen Fertilizer Recommendation UsingPaddy Leaf Image Color Classification","https:\u002F\u002Fdoi.org\u002F10.70818\u002Fijbass.2026.v07i02.0075","The demand for accurate and timely assessment of crop health is high in precision agriculture to support proper nutrient management. In this work, we present an intelligent deep learning-based solution that evaluates the colour of paddy rice leaves to provide real-time, context-sensitive recommendations regarding the addition of nitrogen fertilizers to grow rice sustainably. For optimum nitrogen management and sustainability in the production of rice crops, an accurate assessment of the color of paddy leaves is required. Herein, we present a deep learning-based framework for paddy leaf color classification with the capability of recommending an appropriate amount of nitrogen fertilizer as needed in real-time. In this research, the Paddy Net dataset containing 12,000 augmented rice leaf images will be used for training the neural network used for classifying the rice leaf's color based on the Leaf Color Chart (LCC) system, which consists of LCC levels from 2 to 5. Additionally, several deep learning architectures were trainedand tested, specifically Custom CNNs, VGG16, ResNet50 and EfficientNetB7, to identify which architecture provided the best classification performance of rice leaf color. In terms of the experimental results, it can be concluded that the Custom CNN with a classification accuracy of 97.55% significantly outperforms the transfer learning models, VGG16 (78.89%) and EfficientNetB7 (78.67%). To also improve practicality, multimodal vision-language model LLa MA 3.2-11B Vision is integrated into the system, allowing it to provide real-time, context-based nitrogen fertilizer recommendations based on visual input images. Additionally, the proposed framework takes into account other agronomic factors such as the growth stage of the crop, type of soil, and fertilizer application history. This allows for more accurate and relevant recommendations. The proposed framework allows for precise and objective nitrogen management without the subjective use of visual assessment methods typically used by farmers. Overall, the proposed system for fertilizer application is significant for the development of precision agriculture because it increases the efficiency of the decision-making process, optimizes the use of fertilizer, and increases the practice of sustainable rice cultivation.","在精准农业中，对作物健康状况进行准确且及时评估的需求很高，以支持合理的养分管理。在这项工作中，我们提出了一种基于深度学习的智能解决方案，通过评估水稻叶片的颜色，实时提供与情境相关的氮肥添加建议，以实现水稻的可持续种植。为了实现水稻作物生产中的优化氮管理和可持续性，需要对水稻叶片颜色进行准确评估。在此，我们提出了一个基于深度学习的稻田叶片颜色分类框架，能够根据实时需求推荐适量的氮肥。在本研究中，包含12,000张增强水稻叶片图像的Paddy Net数据集将用于训练神经网络，该网络基于叶色卡（Leaf Color Chart, LCC）系统对水稻叶片颜色进行分类，LCC等级从2到5。此外，我们训练并测试了多种深度学习架构，特别是自定义CNN、VGG16、ResNet50和EfficientNetB7，以确定哪种架构在水稻叶片颜色分类中表现最佳。从实验结果可以得出结论，自定义CNN以97.55%的分类准确率显著优于迁移学习模型VGG16（78.89%）和EfficientNetB7（78.67%）。为提高实用性，系统还集成了多模态视觉语言模型LLaMA 3.2-11B Vision，使其能够基于视觉输入图像提供实时、基于情境的氮肥推荐。此外，所提出的框架还考虑了其他农艺因素，如作物生长阶段、土壤类型和施肥历史，从而提供更准确和相关的建议。该框架实现了精确、客观的氮管理，避免了农民通常采用的主观视觉评估方法。总体而言，所提出的施肥系统对精准农业的发展具有重要意义，因为它提高了决策过程的效率，优化了肥料使用，并促进了可持续水稻种植实践。","International Journal of Business Arts and Scientific Study","2026-09-30T00:00:00Z",63,{"impact":116,"substance":200,"depth":201,"authority":20,"freshness":55,"relevant":21,"comment":202},20,17,"用深度学习与视觉语言模型实现水稻叶色分级与氮肥实时推荐，方法新颖、数据规模可观，但期刊影响力有限，属细分领域技术进展。",[204],{"name":196,"url":193},[27,28,123,206,207],"水稻","氮肥管理",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[210],{"code":211,"slug":212,"name":206,"path":213,"parent_code":214},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",{"status":39,"locked":14,"version":40,"method":41,"evidence":216},{"candidates":217,"agricultural_evidence":46},[218,224,230,234,240],{"code":32,"score":219,"supported":46,"title":220,"abstract":221,"tags":223},19,[143],[49,50,222,143],"养分管理",[207,123],{"code":211,"score":13,"supported":46,"title":225,"abstract":226,"tags":229},[],[206,227,212,228],"稻田","paddy",[206],{"code":54,"score":62,"supported":46,"title":231,"abstract":232,"tags":233},[],[161,162],[27],{"code":61,"score":62,"supported":46,"title":235,"abstract":236,"tags":239},[],[237,238],"深度学习","deep learning",[28],{"code":69,"score":20,"supported":46,"title":241,"abstract":242,"tags":243},[],[72,73],[],[245,246],"水稻 叶片颜色 氮肥推荐","Paddy Net 深度学习 叶色卡","水稻叶片颜色氮肥推荐-3920",2,"10.70818\u002Fijbass.2026.v07i02.0075",{"doi":249,"openalex_id":251,"authors":252,"venue":196,"cited_by_count":95,"oa_url":193,"card":258,"direction":100,"ingested_from":102},"W7214974109",[253,255],{"name":254,"orcid":9},"Rusanara Akhter",{"name":256,"orcid":257},"Md Zahidul Islam","https:\u002F\u002Forcid.org\u002F0000-0002-4868-4945",{"tldr":259,"method":260,"finding":261,"direction":100,"opportunity":262},"用深度学习对水稻叶片颜色分级，并结合多模态大模型实时推荐氮肥用量。","Paddy Net 1.2万张增强叶片图像，训练Custom CNN、VGG16","Custom CNN分类准确率达97.55%，显著优于VGG16（78.89%）和Efficient","可探索轻量化模型部署到手机端，并结合多源农情数据提升跨品种、跨区域推荐泛化能力。","2026-10-01T23:30:59.367113Z",{"id":265,"title":266,"url":267,"summary":268,"summary_zh":269,"content":9,"source_name":270,"source_url":267,"published_at":197,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":271,"score_detail":272,"sources":274,"tags":276,"primary_topic":279,"secondary_topics":284,"classification":287,"search_phrases":331,"slug":334,"view_count":335,"doi":336,"paper":337,"created_at":360},3918,"UAV-based RGB imagery and supervised–unsupervised machine learning for estimating sorghum biomass and grain yield under water and nitrogen stress","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffenvs.2026.1928610","Precision agriculture emerged as a key strategy for rational management of water and nutrients amid growing food demand driven by global population expansion. Therefore, timely monitoring of field crops is crucial in maintaining adequate nutrient and water levels in the crop fields. Vegetation indices are increasingly being studied for their use in plant nutrient management and in guiding fertilizer recommendations. However, these indices were not adequately evaluated for nutrient management of crops grown with different water and nitrogen application levels. This study investigated the potential application of RGB imagery from low-cost Unmanned Aerial Vehicles (UAVs), combined with machine learning techniques, for quantifying water and nutrient stress in sorghum [ Sorghum bicolor (L.) Moench] crop performance. The field experimental setup at Prairie View A&M University, located in Southeast Texas, applied four irrigation levels (0, 75, 100, and 125% of irrigation; the 100% irrigation level is a fully irrigated treatment with no water stress) and four nitrogen application rates (0, 90, 180, and 360 kg N ha −1 ). The RGB images were captured before harvesting the grain sorghum. Image segmentation for filtering sorghum crops from soil and weeds was performed using K-means clustering and a Convolutional Neural Network (CNN). The vegetation indices, including the Green-Red Vegetation Index (GRVI), Green Leaf Index (GLI), and Excess Green Index (ExG), derived from the segmented images were used to predict sorghum grain and biomass yields using multiple linear regression, Partial least squares regression, Ridge regression, and XGBoost. The results indicated that no single model consistently outperformed the others across both segmentation approaches and response variables, suggesting that the predictive information in the RGB indices is meaningful with selected ML approaches. For example, biomass prediction using ridge regression yielded a Pearson correlation coefficient (r) of 0.65 and an RMSE of 0.72 for K-means clustering-derived indices. The models exhibited moderate, stage-specific predictive performance, with no single regression method consistently outperforming the others across both segmentation approaches and response variables. Because imagery was acquired only once during the late reproductive period, the RGB indices primarily reflected canopy condition, maturity, and senescence at that time. The findings demonstrate the feasibility of a low-cost, single-date UAV RGB workflow for late-season sorghum assessment, while emphasizing the need for multi-temporal imagery and independent validation before application to season-long stress diagnosis or operational yield forecasting.","精准农业已成为在水资源与养分合理管理中的关键策略，以应对全球人口增长所驱动的日益增长的粮食需求。因此，及时监测田间作物对于维持作物田块中适宜的养分和水分水平至关重要。植被指数在植物养分管理和指导施肥推荐中的应用正日益受到研究。然而，这些指数尚未在不同水分和氮素施用水平下对作物养分管理进行充分评估。本研究探讨了来自低成本无人机（UAV）的RGB影像结合机器学习技术在量化高粱[Sorghum bicolor (L.) Moench]作物表现中水分和养分胁迫方面的潜在应用。位于德克萨斯州东南部的Prairie View A&M大学的田间试验设置了四个灌溉水平（灌溉量的0%、75%、100%和125%；100%灌溉水平为充分灌溉处理，无水分胁迫）和四个氮素施用量（0、90、180和360 kg N ha⁻¹）。RGB图像在收获籽粒高粱前采集。使用K-means聚类和卷积神经网络（CNN）进行图像分割，以将高粱作物与土壤和杂草分离。从分割图像中提取的植被指数，包括绿红植被指数（GRVI）、绿叶指数（GLI）和过量绿指数（ExG），被用于通过多元线性回归、偏最小二乘回归、岭回归和XGBoost预测高粱籽粒和生物量产量。结果表明，在两种分割方法和响应变量下，没有单一模型始终优于其他模型，这表明RGB指数中的预测信息在所选机器学习方法下具有意义。例如，使用岭回归进行生物量预测时，基于K-means聚类所得指数的Pearson相关系数（r）为0.65，RMSE为0.72。这些模型表现出中等、特定阶段的预测性能，在两种分割方法和响应变量下，没有单一回归方法始终优于其他方法。由于影像仅在生殖后期采集一次，RGB指数主要反映当时的冠层状况、成熟度和衰老程度。研究结果证明了低成本、","Frontiers in Environmental Science",69,{"impact":116,"substance":200,"depth":117,"authority":19,"freshness":55,"relevant":21,"comment":273},"低成本无人机RGB影像结合机器学习估算高粱生物量与产量的方法学研究，结论审慎，对精准农业养分水分管理有参考价值，但属单点单期试验，影响范围有限。",[275],{"name":270,"url":267},[27,28,161,277,278],"遥感","高粱",{"code":280,"slug":281,"name":282,"path":283,"parent_code":214},"A5","tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains",[285,286],{"code":61,"slug":129,"name":130,"path":131,"parent_code":132},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":288},{"candidates":289,"agricultural_evidence":46},[290,295,299,303,311,315,319,326],{"code":280,"score":118,"supported":46,"title":291,"abstract":293,"tags":294},[292],"sorghum",[278,292],[278],{"code":61,"score":118,"supported":46,"title":296,"abstract":297,"tags":298},[149],[65,149],[28],{"code":32,"score":45,"supported":46,"title":300,"abstract":301,"tags":302},[],[49,222,143],[],{"code":304,"score":45,"supported":46,"title":305,"abstract":306,"tags":310},"H3",[],[307,308,309],"灌溉","水资源","irrigation",[],{"code":54,"score":55,"supported":46,"title":312,"abstract":313,"tags":314},[],[161,162],[27,161],{"code":69,"score":20,"supported":46,"title":316,"abstract":317,"tags":318},[],[72,73],[],{"code":320,"score":321,"supported":46,"title":322,"abstract":323,"tags":325},"A7",3,[],[324],"间作",[],{"code":327,"score":21,"supported":14,"title":328,"abstract":329,"tags":330},"I3",[],[],[277],[332,333],"Prairie View A&M University 高粱 无人机","高粱 生物量 产量 无人机 RGB","PrairieViewAMUniversity高粱无人机-3918",4,"10.3389\u002Ffenvs.2026.1928610",{"doi":336,"openalex_id":338,"authors":339,"venue":270,"cited_by_count":95,"oa_url":267,"card":354,"direction":100,"ingested_from":102},"W7214994063",[340,342,345,347,350,352],{"name":341,"orcid":9},"Anoop Valiya Veettil",{"name":343,"orcid":344},"Ripendra Awal","https:\u002F\u002Forcid.org\u002F0000-0002-2453-2592",{"name":346,"orcid":9},"Ali Fares",{"name":348,"orcid":349},"Binita Thapa","https:\u002F\u002Forcid.org\u002F0000-0001-7093-4915",{"name":351,"orcid":9},"Almoutaz Elhassan",{"name":353,"orcid":9},"Atikur Rahman",{"tldr":355,"method":356,"finding":357,"direction":358,"opportunity":359},"用无人机RGB影像与机器学习估算水分氮胁迫下高粱生物量和产量。","低成本无人机RGB影像，K-means与CNN分割，多种回归模型预测。","单一模型未持续最优，RGB指数在特定时期具中等预测能力。","农业遥感与作物表型","可研究多时相RGB影像与深度学习融合，提升胁迫下作物产量预测精度。","2026-10-01T23:30:54.379150Z",{"id":362,"title":363,"url":364,"summary":365,"summary_zh":366,"content":9,"source_name":367,"source_url":364,"published_at":368,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":114,"score_detail":369,"sources":371,"tags":373,"primary_topic":376,"secondary_topics":380,"classification":382,"search_phrases":407,"slug":410,"view_count":248,"doi":411,"paper":412,"created_at":424},3680,"AI-Driven Nutrient Optimization Systems for Precision Agriculture: A Data-Integrated Approach to Reducing Nutrient Runoff and Enhancing Soil Health in Agricultural Systems","https:\u002F\u002Fdoi.org\u002F10.36948\u002Fijfmr.2026.v08i05.85286","Nutrients play a fundamental role in plant growth, soil fertility, and overall agricultural productivity. However, inefficient fertilizer application in conventional farming systems often leads to nutrient imbalance, reduced soil quality, and increased environmental degradation. The emergence of precision agriculture and artificial intelligence (AI) has introduced new opportunities for improving nutrient management through data-driven decision-making. This study investigates an AI-driven nutrient optimization system designed to enhance fertilizer efficiency while reducing nutrient runoff and improving soil health. Findings indicate that AI-based systems significantly improve nutrient use efficiency by enabling site-specific fertilizer recommendations and adaptive management strategies. The integration of multi-source agricultural data enhances prediction accuracy for soil nutrient status and crop demand, while also supporting early identification of nutrient loss risks such as runoff and leaching. The study concludes that AI-driven nutrient optimization provides a sustainable and scalable approach for modern agriculture, supporting both productivity enhancement and environmental protection through intelligent, data-integrated farming systems.","养分在植物生长、土壤肥力和农业整体生产力中发挥着基础性作用。然而，传统农业系统中低效的施肥方式往往导致养分失衡、土壤质量下降以及环境退化加剧。精准农业与人工智能（AI）的出现，为通过数据驱动决策改善养分管理带来了新的机遇。本研究探讨了一种AI驱动的养分优化系统，旨在提高肥料利用效率，同时减少养分径流并改善土壤健康。研究结果表明，基于AI的系统通过实现特定地块的施肥建议和适应性管理策略，显著提高了养分利用效率。多源农业数据的整合提升了土壤养分状况和作物需求的预测精度，同时也有助于早期识别径流和淋溶等养分流失风险。研究得出结论，AI驱动的养分优化为现代农业提供了一种可持续且可扩展的方法，通过智能化、数据整合的农业系统，同时支持生产力提升和环境保护。","International Journal For Multidisciplinary Research","2026-09-26T00:00:00Z",{"impact":116,"substance":17,"depth":118,"authority":55,"freshness":55,"relevant":21,"comment":370},"论文提出AI驱动的养分优化系统，方法有新意但属学术探讨，产业落地影响有限，可作为智慧农业技术动态收录。",[372],{"name":367,"url":364},[27,28,123,374,375],"土壤健康","养分流失",{"code":69,"slug":73,"name":377,"path":378,"parent_code":379},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[381],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":383},{"candidates":384,"agricultural_evidence":46},[385,389,393,397,401],{"code":69,"score":118,"supported":46,"title":386,"abstract":387,"tags":388},[73],[72,73],[374],{"code":54,"score":118,"supported":46,"title":390,"abstract":391,"tags":392},[162],[161,162],[27],{"code":32,"score":13,"supported":46,"title":394,"abstract":395,"tags":396},[],[49,50,222,143],[123],{"code":61,"score":335,"supported":46,"title":398,"abstract":399,"tags":400},[],[66],[28],{"code":402,"score":321,"supported":46,"title":403,"abstract":404,"tags":406},"I6",[],[405],"agricultural data",[],[408,409],"AI 养分优化 精准农业","养分流失 土壤健康","AI养分优化精准农业-3680","10.36948\u002Fijfmr.2026.v08i05.85286",{"doi":411,"openalex_id":413,"authors":414,"venue":367,"cited_by_count":95,"oa_url":364,"card":419,"direction":100,"ingested_from":102},"W7214553198",[415,417],{"name":416,"orcid":9},"Olufisayo Obebe",{"name":418,"orcid":9},"Ebenezer Akinola",{"tldr":420,"method":421,"finding":422,"direction":100,"opportunity":423},"研究AI驱动的养分优化系统，通过数据集成实现精准施肥，减少养分流失并改善土壤健康。","集成多源农业数据与AI算法，进行站点特异性施肥推荐和自适应管理。","AI系统显著提高养分利用效率，增强土壤养分与作物需求预测精度，并早期识别流失风险。","可探索多源数据融合的实时决策模型，以及在不同土壤气候条件下AI系统的可扩展性与长期环境效应评估。","2026-09-28T23:30:49.535963Z",{"id":426,"title":427,"url":428,"summary":429,"summary_zh":430,"content":9,"source_name":431,"source_url":428,"published_at":432,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":433,"score_detail":434,"sources":437,"tags":439,"primary_topic":442,"secondary_topics":443,"classification":451,"search_phrases":486,"slug":489,"view_count":248,"doi":490,"paper":491,"created_at":505},3010,"A Multi-Task Stacked Ensemble and IoT-Enabled Decision Support System for Precision Fertigation in Smallholder Agriculture","https:\u002F\u002Fdoi.org\u002F10.5120\u002Fijca9a6e56b81235","Nigerian agriculture's fixed-schedule fertigation causes low efficiency and nutrient leaching.A stacked-ensemble model is developed for precision fertigation that jointly predicts fertigation need, rate (kg\u002Fha) and timing (Early\u002FOptimal\u002FLate).To train and evaluate the ensemble, a unified dataset was integrated, comprising 12,840 records and 42 variables from a Nigerian soil-weather-yield dataset, a locally sourced Nigerian IoT sensor series and historical weather\u002FNDVI feeds.An LSTM soil-dynamics model, an XGBoost rate regressor and Random Forest need\u002Ftiming classifiers are fused through an XGBoost meta-learner trained on out-of-fold predictions.On held-out partitions, the ensemble reduced rate MAE from 0.55 to 0.49 kg\u002Fha (-10.9%) and RMSE from 0.68 to 0.61 kg\u002Fha (-10.3%;R² 0.88→0.92),raised need F1 from 0.83 to 0.86 (accuracy 0.87→0.89;AUC 0.89→0.93)and timing macro-F1 from 0.84 to 0.86, with well-calibrated probabilities (Brier 0.082).The trained ensemble was deployed through a RESTful API and responsive dashboard; under concurrent load, the system recorded 0% request errors with 1.88s median API latency, demonstrating practical deployability for Nigerian smallholder agriculture.","尼日利亚农业的固定日程水肥一体化导致效率低下和养分淋失。本研究开发了一种堆叠集成模型用于精准水肥管理，可联合预测灌溉施肥需求、施用量（kg\u002Fha）和时机（早\u002F最佳\u002F晚）。为训练和评估该集成模型，整合了一个统一数据集，包含来自尼日利亚土壤-天气-产量数据集、本地尼日利亚物联网传感器序列及历史天气\u002FNDVI数据的12,840条记录和42个变量。通过基于折外预测训练的XGBoost元学习器，将LSTM土壤动力学模型、XGBoost施用量回归器和随机森林需求\u002F时机分类器进行融合。在留出集上，该集成模型将施用量MAE从0.55降至0.49 kg\u002Fha（-10.9%），RMSE从0.68降至0.61 kg\u002Fha（-10.3%；R² 0.88→0.92），需求F1从0.83提升至0.86（准确率0.87→0.89；AUC 0.89→0.93），时机宏平均F1从0.84提升至0.86，且概率校准良好（Brier 0.082）。训练后的集成模型通过RESTful API和响应式仪表板部署；在并发负载下，系统录得0%请求错误，API延迟中位数为1.88秒，展示了在尼日利亚小农农业中的实际可部署性。","International Journal of Computer Applications","2026-09-18T00:00:00Z",79,{"impact":17,"substance":435,"depth":17,"authority":19,"freshness":55,"relevant":21,"comment":436},22,"面向小农户的精准水肥一体化多任务集成模型与物联网决策支持系统，数据规模与方法验证扎实，对智慧农业落地有参考价值。",[438],{"name":431,"url":428},[27,28,124,440,441],"小农户","精准灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[444,445],{"code":134,"slug":135,"name":136,"path":137,"parent_code":132},{"code":446,"slug":447,"name":448,"path":449,"parent_code":450},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",{"status":39,"locked":14,"version":40,"method":41,"evidence":452},{"candidates":453,"agricultural_evidence":46},[454,460,464,469,474,478,482],{"code":32,"score":201,"supported":46,"title":455,"abstract":457,"tags":459},[456],"fertigation",[49,458,456],"水肥一体化",[],{"code":134,"score":118,"supported":46,"title":461,"abstract":462,"tags":463},[156],[154,156],[124],{"code":446,"score":116,"supported":46,"title":465,"abstract":467,"tags":468},[466],"smallholder",[466],[440],{"code":61,"score":45,"supported":46,"title":470,"abstract":472,"tags":473},[471],"decision support",[],[28],{"code":69,"score":20,"supported":46,"title":475,"abstract":476,"tags":477},[],[72,73],[],{"code":304,"score":335,"supported":46,"title":479,"abstract":480,"tags":481},[],[307],[441],{"code":54,"score":21,"supported":14,"title":483,"abstract":484,"tags":485},[],[],[27],[487,488],"尼日利亚 精准灌溉 物联网","堆叠集成 施肥决策 小农户","尼日利亚精准灌溉物联网-3010","10.5120\u002Fijca9a6e56b81235",{"doi":490,"openalex_id":492,"authors":493,"venue":431,"cited_by_count":95,"oa_url":428,"card":500,"direction":187,"ingested_from":102},"W7213663876",[494,496,498],{"name":495,"orcid":9},"Awojide S.",{"name":497,"orcid":9},"Ikpotokin F.O.",{"name":499,"orcid":9},"Sadiq F.I.",{"tldr":501,"method":502,"finding":503,"direction":187,"opportunity":504},"构建多任务堆叠集成模型与物联网决策支持系统，实现小农户精准水肥一体化。","LSTM、XGBoost、随机森林堆叠集成，融合尼日利亚土壤气象、IoT与NDV","集成模型将施肥量MAE降低10.9%，需求与时机分类F1提升，系统部署零错误。","可探索多任务集成模型在非洲小农户不同作物与气候区的迁移能力及低成本IoT部署。","2026-09-20T23:30:09.003454Z",{"id":507,"title":508,"url":509,"summary":510,"summary_zh":511,"content":9,"source_name":512,"source_url":509,"published_at":513,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":514,"score_detail":515,"sources":517,"tags":519,"primary_topic":521,"secondary_topics":522,"classification":524,"search_phrases":547,"slug":550,"view_count":321,"doi":551,"paper":552,"created_at":565},2517,"Artificial Intelligence-Based Intelligent Fertigation Recommendation Systems for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.64388\u002Firev10i3-1722972","Artificial-intelligence-enabled fertilizer and fertigation recommendation has progressed from single-task crop or fertilizer classification toward integrated decision-support architectures. However, predictive classification, agronomic dose calculation, irrigation scheduling, explainability, real-time sensing, and farmer-facing delivery are often studied separately. This paper presents a structured narrative review of these strands and an applied machine-learning case study using the publicly available Crop and Fertilizer Dataset for Western Maharashtra. The case study contains 4,513 records spanning five districts, 16 crops, and 19 fertilizer classes. Seven classifiers were compared using an 80:20 stratified train-test split with five-fold stratified cross-validation on the training set. XGBoost achieved 97.34% test accuracy and 94.21% ± 1.15% cross-validated accuracy, while Random Forest achieved 93.58% and 90.89% ± 0.80%, respectively. TreeSHAP analysis of Random Forest identified crop identity, potassium, and nitrogen as the leading predictors of the historical fertilizer class. These results are interpreted as a computational baseline rather than proof of agronomic optimality because the target label represents recorded fertilizer choices. The review also incorporates evidence on IoT\u002Fedge-cloud sensing, multilingual agricultural advisory, and federated learning. It concludes that RF\u002FSHAP, IoT sensing, and multilingual interfaces are established capabilities; a more defensible research direction is an auditable pipeline that separates fertilizer identity, nutrient dose, and application timing, connects explainable prediction to sequential scheduling, and independently benchmarks outputs against authoritative agronomic guidance. The proposed Intelligent Fertigation Recommendation System (IFRS) is therefore presented as a research framework requiring multi-season and field validation before claims of yield, water, nutrient-use-efficiency, or adoption benefits.","人工智能驱动的肥料与灌溉施肥推荐已从单一任务的作物或肥料分类，发展为集成式决策支持架构。然而，预测性分类、农艺用量计算、灌溉调度、可解释性、实时感知以及面向农户的交付往往被分别研究。本文对这些研究脉络进行了结构化叙述性综述，并基于公开的“西马哈拉施特拉邦作物与肥料数据集”开展了一项应用机器学习案例研究。该案例研究包含4，513条记录，涵盖五个地区、16种作物和19个肥料类别。采用80：20分层训练-测试划分，并在训练集上进行五折分层交叉验证，比较了七种分类器。XGBoost取得了97.34%的测试准确率和94.21% ± 1.15%的交叉验证准确率，而随机森林分别取得了93.58%和90.89% ± 0.80%。对随机森林的TreeSHAP分析表明，作物身份、钾和氮是历史肥料类别的主要预测因子。这些结果被解释为计算基线，而非农艺最优性的证明，因为目标标签代表的是有记录的肥料选择。该综述还纳入了关于物联网\u002F边缘-云感知、多语言农业咨询和联邦学习的证据。结论认为，随机森林\u002FSHAP、物联网感知和多语言界面已是成熟能力；更具可辩护性的研究方向是构建一条可审计的流水线，将肥料身份、养分用量和施用时机分离，将可解释预测与序贯调度相连接，并依据权威农艺指南对输出进行独立基准测试。因此，所提出的智能灌溉施肥推荐系统（IFRS）被作为一个研究框架提出，在声称产量、水分、养分利用效率或采用效益之前，仍需进行多季和田间验证。","Iconic Research and Engineering Journals","2026-09-14T00:00:00Z",62,{"impact":116,"substance":17,"depth":201,"authority":20,"freshness":45,"relevant":21,"comment":516},"对AI水肥推荐研究进行系统综述并给出可复现的机器学习基线，方法透明、结论审慎，但来源为普通工程类期刊且属综述性论文，产业影响有限，适合作为技术参考而非每日精选头条。",[518],{"name":512,"url":509},[27,28,123,520,458],"可解释AI",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[523],{"code":61,"slug":129,"name":130,"path":131,"parent_code":132},{"status":39,"locked":14,"version":40,"method":41,"evidence":525},{"candidates":526,"agricultural_evidence":46},[527,531,535,539,543],{"code":32,"score":219,"supported":46,"title":528,"abstract":529,"tags":530},[456],[49,50,143,456],[458,123],{"code":61,"score":19,"supported":46,"title":532,"abstract":533,"tags":534},[66],[65],[28,520],{"code":54,"score":45,"supported":46,"title":536,"abstract":537,"tags":538},[162],[],[27],{"code":304,"score":20,"supported":46,"title":540,"abstract":541,"tags":542},[],[307,309],[],{"code":134,"score":20,"supported":46,"title":544,"abstract":545,"tags":546},[],[154,156],[],[548,549],"农业人工智能 水肥一体化 智慧农业 精准施肥","农业人工智能 水肥一体化","农业人工智能水肥一体化智慧农业精准施肥-2517","10.64388\u002Firev10i3-1722972",{"doi":551,"openalex_id":553,"authors":554,"venue":512,"cited_by_count":95,"oa_url":559,"card":560,"direction":187,"ingested_from":102},"W7212934364",[555,557],{"name":556,"orcid":9},"Shraddha S. Tayade",{"name":558,"orcid":9},"Yogesh V. Chimate","https:\u002F\u002Fwww.irejournals.com\u002Fformatedpaper\u002F1722972.pdf",{"tldr":561,"method":562,"finding":563,"direction":100,"opportunity":564},"综述AI施肥推荐研究，并用马哈拉施特拉数据集比较七种分类器，提出可审计智能施肥推荐框架。","结构化综述加机器学习案例，4513条记录，七分类器对比，XGBoost与Tree","XGBoost测试准确率97.34%，但标签仅为历史施肥选择，不能证明农艺最优。","可研究分离肥料种类、养分剂量与施用时序的可审计推荐流水线，并进行多季田间验证。","2026-09-15T23:30:12.675690Z"]