[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1911":3,"related-1911":87},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":56,"slug":59,"view_count":22,"doi":60,"paper":61,"created_at":86},1911,"Intelligent Prediction of Spray Distribution and Hydraulic Behavior in an Automatic Spraying Machine for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.1002\u002Frob.70346","ABSTRACT Existing machine‐learning applications in agricultural spraying have mainly focused on droplet‐size descriptors, canopy coverage, deposition, or image‐based spray detection, while the simultaneous prediction of hydraulic and geometric spray responses from controllable operating variables remains limited. This study developed an experimental–computational framework for predicting mean nozzle discharge, effective spray width, derived spray angle, and mean inter‐nozzle overlap from nozzle model, operating pressure, and spray height. Four hollow‐cone ceramic nozzle models were evaluated at five pressures (6.0–9.5 bar) and three heights (30–50 cm), producing 60 operating conditions tested in triplicate. The resulting 180 experimental runs were averaged into 60 condition‐level records for machine‐learning analysis. Mean nozzle discharge ranged from 0.98 to 2.03 L , effective spray width ranged from 34.75 to 71.50 cm, the maximum derived spray angle reached 96.77°, and mean inter‐nozzle overlap ranged from 34.00 to 71.67 cm across the investigated operating conditions. Extra Trees Regressor provided the best predictions for mean nozzle discharge (, RMSE = 49.25 mL , MAE = 37.99 mL ), effective spray width (, RMSE = 2.37 cm), and mean overlap (, RMSE = 3.13 cm). Support vector regression with a radial basis function kernel performed best for derived spray angle (, RMSE = 3.53°). Pressure increased discharge and lateral footprint, whereas increasing height widened the footprint but generally reduced the geometrically derived angle. The models provide rapid condition‐level estimates within the investigated experimental domain and may complement physical calibration. Their transfer to other nozzle families, moving platforms, field environments, or real‐time control requires additional validation.","摘要 现有机器学习在农业喷施中的应用主要集中在雾滴尺寸描述符、冠层覆盖率、沉积量或基于图像的喷雾检测方面，而同时从可控操作变量预测水力与几何喷雾响应的研究仍较为有限。本研究开发了一个实验-计算框架，用于根据喷嘴型号、操作压力和喷雾高度预测平均喷嘴流量、有效喷雾宽度、推导喷雾角度及平均喷嘴间重叠量。研究评估了四种空心锥陶瓷喷嘴型号，在五个压力水平（6.0–9.5 bar）和三个高度（30–50 cm）下进行测试，共产生60种操作条件，每种条件重复三次。由此得到的180次实验运行被平均为60条条件级记录，用于机器学习分析。在所研究的操作条件下，平均喷嘴流量范围为0.98至2.03 L，有效喷雾宽度范围为34.75至71.50 cm，最大推导喷雾角度达到96.77°，平均喷嘴间重叠量范围为34.00至71.67 cm。极端随机树回归器（Extra Trees Regressor）在预测平均喷嘴流量（，RMSE = 49.25 mL，MAE = 37.99 mL）、有效喷雾宽度（，RMSE = 2.37 cm）和平均重叠量（，RMSE = 3.13 cm）方面表现最佳。采用径向基函数核的支持向量回归（support vector regression with a radial basis function kernel）在推导喷雾角度预测中表现最优（，RMSE = 3.53°）。压力增加了流量和横向覆盖范围，而增加高度则拓宽了覆盖范围，但通常减小了几何推导角度。所建立的模型能够在所研究的实验域内提供快速的条件级估计，并可作为物理校准的补充手段。将其推广至其他喷嘴系列、移动平台、田间环境或实时控制仍需进一步验证。",null,"Journal of Field Robotics","2026-09-07T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,18,14,7,1,"论文提出预测喷雾分布与液压行为的机器学习框架，数据详实，对精准农业装备优化有参考价值。",[25],{"name":10,"url":6},[27,28,29,30],"智慧农业","机器学习","精准农业","植保机械",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51],{"code":45,"score":13,"supported":46,"title":47,"abstract":49,"tags":50},"I1",true,[48],"precision agriculture",[],[27,29],{"code":32,"score":52,"supported":46,"title":53,"abstract":54,"tags":55},4,[],[28],[28],[57,58],"智慧农业 机器学习 植保机械 精准农业","智慧农业 机器学习","智慧农业机器学习植保机械精准农业-1911","10.1002\u002Frob.70346",{"doi":60,"openalex_id":62,"authors":63,"venue":10,"cited_by_count":78,"oa_url":9,"card":79,"direction":83,"ingested_from":85},"W7211920463",[64,67,69,71,73,75],{"name":65,"orcid":66},"Abdallah Elshawadfy Elwakeel","https:\u002F\u002Forcid.org\u002F0000-0002-9360-051X",{"name":68,"orcid":9},"Abdallah Zein Elden",{"name":70,"orcid":9},"Saad Ahmed",{"name":72,"orcid":9},"L Nasrat",{"name":74,"orcid":9},"Mohammad S. AL-Harbi",{"name":76,"orcid":77},"Atef Fathy Ahmed","https:\u002F\u002Forcid.org\u002F0000-0001-8723-9097",0,{"tldr":80,"method":81,"finding":82,"direction":83,"opportunity":84},"用机器学习预测自动喷雾机的流量、喷幅、角度和重叠，实现精准农业。","实验测试4种喷嘴、5压力、3高度共60工况，用Extra Trees和SVR建模","Extra Trees预测流量、喷幅和重叠最佳，SVR预测角度最佳；压力增加流量和喷幅，高度增加喷幅","农业人工智能与决策模型","模型仅限特定喷嘴和静态条件，可扩展至其他喷嘴、移动平台和实时控制，需更多验证。","openalex","2026-09-08T23:30:35.848835Z",{"total":88,"page":22,"page_size":88,"items":89},6,[90,175,266,328,385,446],{"id":91,"title":92,"url":93,"summary":94,"summary_zh":95,"content":9,"source_name":96,"source_url":93,"published_at":97,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":98,"score_detail":99,"sources":103,"tags":105,"primary_topic":108,"secondary_topics":109,"classification":110,"search_phrases":132,"slug":135,"view_count":78,"doi":136,"paper":137,"created_at":174},5306,"A growing challenge: stakeholder perceptions of AI and ML applications in U.S. agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10459-x","Abstract Context The global population is expected to exceed ten billion by 2050 and food demand is projected to increase by up to 100% in the same timeframe. Agricultural management must adapt to meet the increasing global demands. Artificial intelligence (AI) and machine learning (ML) technologies can help the agricultural community respond to these needs. Aims An online survey and follow-up interviews were conducted to examine patterns of use, perceptions, and barriers among early adopting stakeholders regarding AI and ML. Methods The online survey with agricultural stakeholders (n=129) included questions regarding the participants’ views on incorporating AI and ML techniques into farming practices and individual follow-up interviews with agricultural stakeholders (n=10) were conducted to better understand their concerns toward technology use and adoption of AI-enabled tools and technologies. Key results Using a mixed-methods approach, four challenges to establishing AI-\u002FML-enabled technology in agriculture were identified. Namely, challenges related to (1) systems of systems (SoS), (2) sub-seasonal to seasonal weather (S2S) forecasts, (3) precision soil management, and (4) policy and economics. Each challenge was discussed in the context of recent literature. Conclusion Concerns were voiced clustered around four broad research themes. This brief communication brings the perspective of agricultural stakeholders to the discussion of the potential of AI\u002FML-enabled technology and the direction of their development in the agricultural sector. Implications Data collected in 2021, establish an important baseline for the early phase of AI\u002FML use in U.S. agriculture, enabling comparison of future perceptions and developments in the sector. Impact This brief communication describes a survey and follow-up interviews conducted with agricultural stakeholders on factors influencing their willingness and reluctance to incorporate AI and ML techniques into farming practices. The findings, summarized and discussed as four key challenges for AI and ML in the agricultural sector, have the potential to streamline research activities.","摘要\n\n背景 预计到2050年全球人口将超过100亿，同期粮食需求预计将增加多达100%。农业管理必须作出调整以满足不断增长的全球需求。人工智能（AI）和机器学习（ML）技术可帮助农业界应对这些需求。\n\n目的 通过开展在线调查和后续访谈，考察早期采用相关技术的利益相关者在AI和ML方面的使用模式、认知及障碍。\n\n方法 针对农业利益相关者（n=129）的在线调查包含有关参与者对将AI和ML技术纳入农业实践看法的问题，并对农业利益相关者（n=10）进行个体后续访谈，以更好地了解他们对技术使用及采用AI赋能工具和技术的顾虑。\n\n主要结果 采用混合方法，识别出在农业中建立AI\u002FML赋能技术面临的四项挑战，即与以下方面相关的挑战：（1）系统之系统（SoS），（2）次季节至季节天气（S2S）预报，（3）精准土壤管理，以及（4）政策与经济。每项挑战均结合近期文献进行了讨论。\n\n结论 所表达的顾虑集中于四个广泛的研究主题。本简报将农业利益相关者的视角引入关于AI\u002FML赋能技术潜力及其在农业部门发展方向讨论中。\n\n意义 2021年收集的数据为美国农业中AI\u002FML应用的早期阶段建立了重要基线，有助于比较该部门未来的认知和发展。\n\n影响 本简报描述了针对农业利益相关者开展的一项调查和后续访谈，内容涉及影响其愿意和不愿将AI和ML技术纳入农业实践的因素。研究结果总结并讨论为农业部门AI和ML的四项关键挑战，有望优化研究活动。","Precision Agriculture","2026-10-18T00:00:00Z",77,{"impact":19,"substance":18,"depth":100,"authority":20,"freshness":101,"relevant":22,"comment":102},17,8,"基于129份问卷与10次访谈的混合方法研究，系统梳理美国农业AI\u002FML应用的四大挑战，为早期采用者认知提供基线数据，对智慧农业研究有参考价值。",[104],{"name":96,"url":93},[27,106,28,29,107],"农业人工智能","农户认知",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":111},{"candidates":112,"agricultural_evidence":46},[113,120,127],{"code":32,"score":114,"supported":46,"title":115,"abstract":116,"tags":119},11,[],[28,117,118],"machine learning","artificial intelligence",[106,28],{"code":121,"score":88,"supported":46,"title":122,"abstract":123,"tags":126},"H2",[],[124,125],"土壤","soil",[],{"code":45,"score":128,"supported":14,"title":129,"abstract":130,"tags":131},2,[],[],[27,29],[133,134],"美国农业 AI ML 调查","农业人工智能 应用障碍","美国农业AIML调查-5306","10.1007\u002Fs11119-026-10459-x",{"doi":136,"openalex_id":138,"authors":139,"venue":96,"cited_by_count":78,"oa_url":93,"card":169,"direction":83,"ingested_from":85},"W7222866367",[140,143,146,149,152,155,158,161,163,166],{"name":141,"orcid":142},"Jennifer Koch","https:\u002F\u002Forcid.org\u002F0000-0002-7067-2705",{"name":144,"orcid":145},"Andrew Fox","https:\u002F\u002Forcid.org\u002F0000-0001-6719-8902",{"name":147,"orcid":148},"Richa Bhattarai","https:\u002F\u002Forcid.org\u002F0000-0003-3827-7761",{"name":150,"orcid":151},"Hank C. Jenkins‐Smith","https:\u002F\u002Forcid.org\u002F0000-0002-5204-2726",{"name":153,"orcid":154},"Jonathan McFadden","https:\u002F\u002Forcid.org\u002F0000-0003-3762-1428",{"name":156,"orcid":157},"Audrey Reinert","https:\u002F\u002Forcid.org\u002F0000-0001-8951-702X",{"name":159,"orcid":160},"Juan Sesmero","https:\u002F\u002Forcid.org\u002F0000-0002-2669-4536",{"name":162,"orcid":9},"M. Z. Walkup",{"name":164,"orcid":165},"Wolfgang Jentner","https:\u002F\u002Forcid.org\u002F0000-0003-1045-6020",{"name":167,"orcid":168},"David S. Ebert","https:\u002F\u002Forcid.org\u002F0000-0001-6177-1296",{"tldr":170,"method":171,"finding":172,"direction":83,"opportunity":173},"通过问卷与访谈调查美国农业利益相关者对AI\u002FML应用的认知与障碍。","在线问卷129人加10人深度访谈的混合方法。","识别出系统之系统、次季节天气预报、精准土壤管理和政策经济四大挑战。","可针对四类障碍开展分区域、分作物类型的采纳意愿追踪与干预研究。","2026-10-19T23:30:02.345893Z",{"id":176,"title":177,"url":178,"summary":179,"summary_zh":180,"content":9,"source_name":181,"source_url":178,"published_at":182,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":183,"score_detail":184,"sources":188,"tags":190,"primary_topic":193,"secondary_topics":197,"classification":199,"search_phrases":237,"slug":240,"view_count":52,"doi":241,"paper":242,"created_at":265},3516,"Approaches to forecast soil nutrient dynamics for precision agriculture and sustainable fertiliser management: A review","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.16160","Predictive modelling of soil nutrient dynamics is an essential tool for promoting sustainable agricultural practices and environmentally responsible farming methods. The statistical and machine learning techniques used to forecast the availability and dynamics of soil nutrients are summarised in this review. The core frameworks for measuring spatio-temporal nutritional variability are established by traditional statistical approaches such as time-series models autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), multivariate techniques (Principal component analysis (PCA) and factor analysis) and geostatistical tools (kriging). By capturing intricate nonlinear interactions within heterogeneous agroecosystems, machine learning techniques like random forest, support vector machines and ensemble approaches (XGBoost, LightGBM and AdaBoost) provide higher prediction accuracy. Forecasting capabilities are further enhanced by hybrid frameworks [Autoregressive integrated moving average with exogenous variables–artificial neural network. (ARIMAX-ANN)] and deep learning architectures (Convolutional neural network (CNN), long short-term memory (LSTM), ANN). With R2 values above 0.93 and notable decreases in prediction errors, ensemble approaches routinely perform better than traditional linear models. Nevertheless, persistent challenges include data quality limitations, spatial sampling constraints, insufficient environmental covariates and reduced model transferability across diverse pedoclimatic regions. Integrating high-resolution soil properties, climatic variables, terrain attributes and spectral information with advanced modelling architectures remains crucial for enhancing predictive reliability, ultimately supporting precision nutrient management, improved fertiliser efficiency and environmentally responsible agricultural systems.","土壤养分动态的预测建模是推动可持续农业实践和环境友好型耕作方法的重要工具。本综述总结了用于预测土壤养分有效性及其动态变化的统计与机器学习技术。传统统计方法，如时间序列模型自回归积分滑动平均模型（ARIMA）、季节性自回归积分滑动平均模型（SARIMA）、多变量技术（主成分分析（PCA）和因子分析）以及地统计工具（克里金法），为量化养分的时空变异性奠定了核心框架。通过捕捉异质性农业生态系统中复杂的非线性相互作用，随机森林、支持向量机和集成方法（XGBoost、LightGBM和AdaBoost）等机器学习技术可实现更高的预测精度。混合框架[含外生变量的自回归积分滑动平均模型–人工神经网络（ARIMAX-ANN）]和深度学习架构（卷积神经网络（CNN）、长短期记忆网络（LSTM）、人工神经网络（ANN））进一步增强了预测能力。集成方法的R²值超过0.93，且预测误差显著降低，其表现通常优于传统线性模型。然而，持续存在的挑战包括数据质量限制、空间采样约束、环境协变量不足以及模型在不同土壤气候区域间可迁移性降低等问题。将高分辨率土壤属性、气候变量、地形属性和光谱信息与先进建模架构相结合，对于提高预测可靠性仍然至关重要，最终可为精准养分管理、提高肥料利用效率以及环境友好型农业系统提供支撑。","Plant Science Today","2026-09-24T00:00:00Z",79,{"impact":19,"substance":185,"depth":19,"authority":186,"freshness":101,"relevant":22,"comment":187},22,13,"系统综述土壤养分动态预测的统计与机器学习方法，方法体系完整、结论有量化支撑，对精准施肥与农业信息化有参考价值，但属综述类论文，产业级影响有限。",[189],{"name":181,"url":178},[27,191,28,29,192],"变量施肥","土壤养分",{"code":121,"slug":125,"name":194,"path":195,"parent_code":196},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[198],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":200},{"candidates":201,"agricultural_evidence":46},[202,207,211,217,224,231],{"code":121,"score":203,"supported":46,"title":204,"abstract":205,"tags":206},15,[125],[124,125],[192],{"code":45,"score":13,"supported":46,"title":208,"abstract":209,"tags":210},[48],[],[27,29],{"code":32,"score":13,"supported":46,"title":212,"abstract":213,"tags":216},[],[214,28,117,215],"深度学习","deep learning",[28],{"code":218,"score":21,"supported":46,"title":219,"abstract":220,"tags":223},"F3",[],[221,222],"肥料","养分管理",[191],{"code":225,"score":226,"supported":46,"title":227,"abstract":228,"tags":230},"H6",3,[],[229],"农业生态",[],{"code":232,"score":226,"supported":46,"title":233,"abstract":234,"tags":236},"H8",[],[235],"可持续农业",[],[238,239],"土壤养分 预测模型 精准农业","机器学习 施肥管理 可持续农业","土壤养分预测模型精准农业-3516","10.14719\u002Fpst.16160",{"doi":241,"openalex_id":243,"authors":244,"venue":181,"cited_by_count":78,"oa_url":178,"card":260,"direction":83,"ingested_from":85},"W7214167059",[245,248,251,254,257],{"name":246,"orcid":247},"R Rathna","https:\u002F\u002Forcid.org\u002F0009-0004-7797-2673",{"name":249,"orcid":250},"B Sivasankari","https:\u002F\u002Forcid.org\u002F0000-0001-9921-8170",{"name":252,"orcid":253},"R. Gangai Selvi","https:\u002F\u002Forcid.org\u002F0000-0002-4475-2293",{"name":255,"orcid":256},"J Prabhakaran","https:\u002F\u002Forcid.org\u002F0000-0001-7339-175X",{"name":258,"orcid":259},"K. G. Sabarinathan","https:\u002F\u002Forcid.org\u002F0000-0002-8659-6479",{"tldr":261,"method":262,"finding":263,"direction":83,"opportunity":264},"综述土壤养分动态预测的统计与机器学习方法，比较精度与局限。","综述ARIMA、地统计、随机森林、XGBoost、CNN\u002FLSTM及混合模型。","集成与深度学习模型精度更高（R²>0.93），但数据质量与跨区迁移性仍是瓶颈。","可研究多源遥感与气候数据融合的迁移学习模型，提升跨区域养分预测泛化能力。","2026-09-25T23:30:54.950445Z",{"id":267,"title":268,"url":269,"summary":270,"summary_zh":271,"content":9,"source_name":272,"source_url":269,"published_at":273,"category":12,"cover_url":9,"hotness":274,"is_selected":14,"score":275,"score_detail":276,"sources":280,"tags":284,"primary_topic":286,"secondary_topics":287,"classification":288,"search_phrases":311,"slug":314,"view_count":128,"doi":315,"paper":316,"created_at":327},3271,"A Systematic Study of Supervised and Ensemble Learning Approaches for Crop Selection in Smart Agriculture","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22896343","Selecting the crop best matched to a field’s soil and climate is one of the highest-leverage decisions in agriculture, and one that farmers have traditionally made by intuition or inherited practice. Soil pH together with nitrogen, phosphorus and potassium concentration, and the local temperature, humidity and rainfall regime, jointly determine which crop will flourish and which will fail, and the relationship between those variables and crop performance is neither linear nor independent. Machine learning has therefore become the dominant approach to automated crop recommendation. This paper reviews the field across four technique families — classical supervised learning, ensemble and boosting methods, deep learning and metaheuristic hybrids, and IoT and deployment-oriented systems — and compares twenty-four representative studies published between 2016 and 2026 in terms of method, data source, reported accuracy, advantage and limitation. A generic seven-stage recommendation pipeline is presented and each family is situated within it. The comparison shows that reported accuracy on the standard nutrient-and-climate benchmark has converged in a narrow band between roughly 98 and 99.5 per cent, that boosting and ensemble methods occupy the upper part of that band, and that further gains on the benchmark are no longer the binding constraint on the field. The gaps that remain open are instead the absence of socio-economic and market variables from the decision, the lack of region-specific and long-horizon environmental validation, dataset narrowness and geographic bias, limited interpretability, and the accessibility of these systems to small and resource-poor farmers. These are consolidated into a set of research directions for future work.","选择与田块土壤和气候最匹配的作物是农业中杠杆效应最高的决策之一，而农民传统上依靠直觉或世代相传的经验来做出这一决策。土壤pH值以及氮、磷、钾浓度，加上当地的气温、湿度和降雨状况，共同决定了哪种作物能够茁壮成长、哪种会歉收，而这些变量与作物表现之间的关系既非线性也非相互独立。因此，机器学习已成为自动化作物推荐的主流方法。本文从四个技术族系——经典监督学习、集成与提升方法、深度学习与元启发式混合方法，以及物联网与面向部署的系统——对该领域进行了综述，并从方法、数据来源、报告精度、优势和局限性方面比较了2016年至2026年间发表的二十四项代表性研究。本文提出了一个通用的七阶段推荐流程，并将每个技术族系置于该流程中加以定位。比较结果表明，在标准养分与气候基准上的报告精度已收敛于约98%至99.5%的狭窄区间内，提升与集成方法占据该区间的上端，而在该基准上进一步提升已不再是该领域的约束瓶颈。真正尚未填补的空白在于：决策中缺乏社会经济和市场变量，缺少针对特定区域和长期环境验证，数据集狭窄且存在地理偏差，可解释性有限，以及这些系统对小型和资源匮乏农户的可及性不足。这些空白被归纳为未来工作的一系列研究方向。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-22T00:00:00Z",25,78,{"impact":277,"substance":185,"depth":19,"authority":186,"freshness":278,"relevant":22,"comment":279},16,9,"系统综述24项研究并指出基准精度已趋饱和，真正瓶颈转向社会经济变量与可解释性，对智慧农业选种方向有参考价值。",[281,282],{"name":272,"url":269},{"name":272,"url":283},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22896344",[27,106,28,29,285],"作物推荐",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":289},{"candidates":290,"agricultural_evidence":46},[291,295,300,304],{"code":32,"score":114,"supported":46,"title":292,"abstract":293,"tags":294},[],[214,28,117,215],[106,28],{"code":45,"score":13,"supported":46,"title":296,"abstract":298,"tags":299},[297],"smart agriculture",[],[27,29],{"code":121,"score":88,"supported":46,"title":301,"abstract":302,"tags":303},[],[124,125],[],{"code":305,"score":88,"supported":46,"title":306,"abstract":307,"tags":310},"I5",[],[308,309],"物联网","IoT",[],[312,313],"农业人工智能 作物推荐 智慧农业 机器学习","农业人工智能 作物推荐","农业人工智能作物推荐智慧农业机器学习-3271","10.5281\u002Fzenodo.22896343",{"doi":315,"openalex_id":317,"authors":318,"venue":272,"cited_by_count":78,"oa_url":269,"card":321,"direction":326,"ingested_from":85},"W7214002748",[319],{"name":320,"orcid":9},"Prof. Nagendra Patel Sahil Verma",{"tldr":322,"method":323,"finding":324,"direction":83,"opportunity":325},"系统综述2016-2026年24项作物推荐研究，比较四类机器学习方法并指出基准精度已趋饱和。","综述监督学习、集成提升、深度学习与元启发式、物联网部署四类方法及七阶段流程。","标准基准精度收敛于98%-99.5%，提升集成法最优，但精度已非领域瓶颈。","将社会经济与市场变量、区域长期环境验证及可解释性纳入作物推荐，服务小农户。","智慧农业 \u002F 农业物联网","2026-09-23T23:30:09.369987Z",{"id":329,"title":330,"url":331,"summary":332,"summary_zh":333,"content":9,"source_name":334,"source_url":331,"published_at":335,"category":12,"cover_url":9,"hotness":274,"is_selected":14,"score":336,"score_detail":337,"sources":339,"tags":343,"primary_topic":346,"secondary_topics":347,"classification":348,"search_phrases":359,"slug":362,"view_count":226,"doi":363,"paper":364,"created_at":384},3157,"Influence of Sound Frequencies on Plant Growth and Physiological Development","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22873563","Plant growth and physiological development are influenced by a wide range of environmental stimuli, and recent research has increasingly focused on the role of acoustic energy as a non-invasive growth-modulating factor. Conventional agricultural practices rely primarily on light, water, nutrients, and temperature control, while the potential of sound frequency exposure remains largely underexplored in mainstream cultivation systems. This paper presents a systematic experimental framework for studying the Influence of Sound Frequencies on Plant Growth and Physiological Development, which examines how controlled acoustic stimuli across different frequency ranges affect germination rate, stem elongation, leaf area, chlorophyll content, and overall biomass accumulation. The proposed framework integrates a calibrated sound frequency generator, a controlled plant exposure chamber, and a network of growth-parameter sensors to capture physiological responses under repeatable experimental conditions. Statistical and machine-learning-based correlation analysis is applied to the collected data to identify frequency ranges that produce measurable and consistent effects on plant development. In addition, an intelligent recommendation module suggests optimal frequency and exposure duration for specific plant species based on observed growth trends. The framework is designed to minimize experimental variability, ensure reproducibility across trials, and support data-driven insights for sustainable and technology-assisted agriculture. Experimental evaluation demonstrates measurable variation in physiological parameters across frequency treatments, improved understanding of acoustic-plant interaction, and a practical pathway toward sound-assisted cultivation techniques, making the proposed framework a valuable contribution to smart and precision agriculture research.","植物生长和生理发育受到多种环境刺激的影响，近年来的研究日益关注声能作为一种非侵入性生长调控因子的作用。传统农业实践主要依赖光照、水分、养分和温度控制，而声频暴露的潜力在主流栽培系统中仍未得到充分探索。本文提出了一个系统性的实验框架，用于研究声频对植物生长和生理发育的影响，该框架考察不同频率范围内的受控声刺激如何影响发芽率、茎伸长、叶面积、叶绿素含量和整体生物量积累。所提出的框架集成了经过校准的声频发生器、受控植物暴露舱以及生长参数传感器网络，以在可重复的实验条件下捕获生理响应。研究对采集的数据应用统计和基于机器学习的相关性分析，以识别对植物发育产生可测量且一致影响的频率范围。此外，智能推荐模块根据观察到的生长趋势，为特定植物物种建议最佳频率和暴露时长。该框架旨在最大限度地减少实验变异性，确保跨试验的可重复性，并支持面向可持续和技术辅助农业的数据驱动洞察。实验评估表明，不同频率处理下生理参数存在可测量的差异，增进了对声-植物相互作用的理解，并提供了通向声辅助栽培技术的实践路径，使所提出的框架成为智能和精准农业研究的有价值贡献。","International Journal of Science Engineering and Technology","2026-09-21T00:00:00Z",70,{"impact":17,"substance":18,"depth":100,"authority":186,"freshness":101,"relevant":22,"comment":338},"该论文提出声频刺激植物生长的系统实验框架并引入智能推荐模块，方法新颖、结论可靠，对智慧农业与精准农业研究有参考价值，但属细分领域基础研究，产业影响有限。",[340,341],{"name":334,"url":331},{"name":334,"url":342},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22873562",[27,28,29,344,345],"声波助长","植物生理",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":349},{"candidates":350,"agricultural_evidence":46},[351,355],{"code":45,"score":101,"supported":46,"title":352,"abstract":353,"tags":354},[],[29,48],[27,29],{"code":32,"score":52,"supported":46,"title":356,"abstract":357,"tags":358},[],[28],[28],[360,361],"声频 植物生长 生理发育","声波助长 智慧农业","声频植物生长生理发育-3157","10.5281\u002Fzenodo.22873563",{"doi":363,"openalex_id":365,"authors":366,"venue":334,"cited_by_count":78,"oa_url":331,"card":379,"direction":326,"ingested_from":85},"W7213863269",[367,369,371,373,375,377],{"name":368,"orcid":9},"R. Baby",{"name":370,"orcid":9},"J. Jerlin",{"name":372,"orcid":9},"M. Veni",{"name":374,"orcid":9},"S. Divya",{"name":376,"orcid":9},"M. Arunatharan",{"name":378,"orcid":9},"A. Mohamed Esmail",{"tldr":380,"method":381,"finding":382,"direction":326,"opportunity":383},"构建声频刺激植物生长实验框架，分析不同频率对生理指标的影响并推荐最优频率。","校准声频发生器、受控暴露舱与生长传感器网络，结合统计与机器学习分析。","不同频率处理下植物生理参数存在可测量差异，声频可辅助栽培。","可探索特定作物声频响应机制，并将声频调控集成到物联网精准农业系统中。","2026-09-22T23:30:10.713102Z",{"id":386,"title":387,"url":388,"summary":389,"summary_zh":390,"content":9,"source_name":391,"source_url":388,"published_at":392,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":275,"score_detail":393,"sources":395,"tags":397,"primary_topic":400,"secondary_topics":401,"classification":402,"search_phrases":418,"slug":421,"view_count":22,"doi":422,"paper":423,"created_at":445},2803,"Volatile Fingerprinting Empowers Salinity Monitoring in Peppermint Using MOS Sensors and Feature-Optimized Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Felectronics15184233","Early detection of salinity stress is essential for precision agriculture, particularly in scalable, resource-constrained monitoring systems. This study presents a portable sensing module integrating a low-cost metal oxide semiconductor (MOS) sensor array with potential application for edge deployment to detect salinity stress in peppermint. Salinity significantly reduced plant biomass, confirming physiological stress induction. Volatile organic compound (VOC) fingerprints were collected over eleven consecutive days in a controlled enclosure. Sensor signals underwent outlier filtering, normalization, and smoothing, while treatment discrimination was verified using the Kruskal–Wallis test. Thirty-three machine learning models were evaluated using a 75:25 train–test split with five-fold cross-validation. Wide neural network models achieved the highest predictive performance, exceeding 98% test accuracy and a 97% macro F1 score. Feature adequacy analysis showed that six sensors captured the dominant variance required for reliable classification. Considering computational constraints, a bilayered neural network using only six features maintained over 97% accuracy with a memory footprint of 0.008 MB while remaining Pareto optimal. These findings support the feasibility of a compact, computationally efficient, and edge-compatible VOC sensing framework for salinity stress detection in precision agriculture and intelligent crop monitoring.","盐胁迫的早期检测对精准农业至关重要，尤其是在可扩展、资源受限的监测系统中。本研究提出了一种便携式传感模块，将低成本金属氧化物半导体（MOS）传感器阵列集成其中，具备边缘部署的应用潜力，用于检测薄荷中的盐胁迫。盐胁迫显著降低了植物生物量，证实了生理胁迫的诱导作用。在受控密闭环境中连续十一天采集了挥发性有机化合物（VOC）指纹图谱。对传感器信号进行了异常值过滤、归一化和平滑处理，并使用Kruskal–Wallis检验验证了处理组间的区分度。采用75:25的训练-测试划分和五折交叉验证评估了三十三种机器学习模型。宽神经网络模型取得了最高的预测性能，测试准确率超过98%，宏F1分数达到97%。特征充分性分析表明，六个传感器即可捕获可靠分类所需的主要方差。考虑到计算约束，仅使用六个特征的双层神经网络在保持超过97%准确率的同时，内存占用仅为0.008 MB，且仍处于帕累托最优。这些发现支持了一种紧凑、计算高效且兼容边缘计算的VOC传感框架用于精准农业和智能作物监测中盐胁迫检测的可行性。","Electronics","2026-09-17T00:00:00Z",{"impact":203,"substance":185,"depth":19,"authority":186,"freshness":13,"relevant":22,"comment":394},"低成本MOS传感器阵列结合特征优化机器学习实现薄荷盐胁迫早期无损检测，方法新颖、数据扎实，对边缘部署式作物监测有参考价值。",[396],{"name":391,"url":388},[27,28,29,398,399],"农业传感器","盐胁迫监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":403},{"candidates":404,"agricultural_evidence":46},[405,409,413],{"code":32,"score":203,"supported":46,"title":406,"abstract":407,"tags":408},[117],[28,117],[28],{"code":45,"score":101,"supported":46,"title":410,"abstract":411,"tags":412},[],[29,48],[27,29],{"code":305,"score":52,"supported":46,"title":414,"abstract":415,"tags":417},[],[416],"边缘计算",[398],[419,420],"农业传感器 盐胁迫监测 智慧农业 机器学习","农业传感器 盐胁迫监测","农业传感器盐胁迫监测智慧农业机器学习-2803","10.3390\u002Felectronics15184233",{"doi":422,"openalex_id":424,"authors":425,"venue":391,"cited_by_count":78,"oa_url":388,"card":440,"direction":83,"ingested_from":85},"W7213452763",[426,429,432,434,437],{"name":427,"orcid":428},"Ahmad Ali","https:\u002F\u002Forcid.org\u002F0000-0001-5530-7374",{"name":430,"orcid":431},"Vinie Lee Silva Alvarado","https:\u002F\u002Forcid.org\u002F0009-0000-5857-3248",{"name":433,"orcid":9},"Arman Heydari",{"name":435,"orcid":436},"Sandra Sendra","https:\u002F\u002Forcid.org\u002F0000-0001-9556-9088",{"name":438,"orcid":439},"Jaime Lloret","https:\u002F\u002Forcid.org\u002F0000-0002-0862-0533",{"tldr":441,"method":442,"finding":443,"direction":326,"opportunity":444},"用低成本MOS传感器阵列采集薄荷VOC指纹，结合特征优化机器学习实现盐胁迫检测。","11天VOC指纹采集，33种机器学习模型，五折交叉验证，特征充分性分析。","宽神经网络准确率超98%，仅用6个特征的双层网络保持97%以上且内存仅0.008MB。","可探索多作物VOC指纹迁移学习与田间边缘设备长期稳定性验证。","2026-09-17T23:30:59.249847Z",{"id":447,"title":448,"url":449,"summary":450,"summary_zh":451,"content":9,"source_name":452,"source_url":449,"published_at":392,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":453,"score_detail":454,"sources":456,"tags":458,"primary_topic":460,"secondary_topics":461,"classification":462,"search_phrases":485,"slug":488,"view_count":52,"doi":489,"paper":490,"created_at":509},2802,"Advanced olive leaf area prediction using machine learning methods","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71390-9","Abstract Accurate leaf area estimation is essential for understanding olive tree physiology, productivity, and stress adaptationThis study developed and evaluated machine learning models for non-destructive olive leaf area prediction using linear measurements (length and width) from 30 diverse cultivars at the Tarom Olive Research Station, Iran. Six machine learning algorithms, Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest, Decision Tree, AdaBoost, and XGBoost were optimized using Bayesian optimization, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Results demonstrated that PSO consistently outperformed other optimization methods across most models. The ANN model optimized with PSO achieved the highest prediction accuracy (R 2 = 0.9828, RMSE = 0.3009 cm 2 ). External validation using eight additional cultivars confirmed model generalizability, with the universal ANN model maintaining R 2 > 0.98. This study provides a robust, non-destructive methodology for olive leaf area estimation applicable across diverse cultivars, offering practical implications for precision agriculture, phenotyping, and orchard management under changing climatic conditions.","摘要 准确的叶面积估算对于理解油橄榄树的生理特性、生产力及逆境适应性至关重要。本研究在伊朗塔罗姆油橄榄研究站，利用来自30个不同品种的线性测量数据（长度和宽度），开发并评估了用于无损油橄榄叶面积预测的机器学习模型。采用贝叶斯优化、遗传算法（GA）和粒子群优化（PSO）对六种机器学习算法——人工神经网络（ANN）、支持向量回归（SVR）、随机森林、决策树、AdaBoost和XGBoost——进行了优化。结果表明，在大多数模型中，PSO始终优于其他优化方法。经PSO优化后的ANN模型取得了最高的预测精度（R² = 0.9828，RMSE = 0.3009 cm²）。利用另外八个品种进行的外部验证证实了模型的泛化能力，通用ANN模型保持R² > 0.98。本研究为适用于不同品种的油橄榄叶面积估算提供了一种稳健的无损方法，为气候变化条件下的精准农业、表型分析和果园管理提供了实际应用价值。","Scientific Reports",73,{"impact":17,"substance":18,"depth":100,"authority":20,"freshness":13,"relevant":22,"comment":455},"基于30个品种的机器学习叶片面积无损预测研究，方法新颖、验证充分，对精准农业与表型分析有实用价值，但属细分领域技术进展，影响范围有限。",[457],{"name":452,"url":449},[27,106,28,29,459],"表型分析",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":463},{"candidates":464,"agricultural_evidence":46},[465,469,473,479],{"code":32,"score":277,"supported":46,"title":466,"abstract":467,"tags":468},[117],[28,117],[106,28],{"code":45,"score":101,"supported":46,"title":470,"abstract":471,"tags":472},[],[29,48],[27,29],{"code":474,"score":226,"supported":46,"title":475,"abstract":476,"tags":478},"B2",[],[477],"orchard",[],{"code":480,"score":226,"supported":46,"title":481,"abstract":482,"tags":484},"H7",[],[483],"气候变化",[],[486,487],"农业人工智能 智慧农业 机器学习 精准农业","农业人工智能 智慧农业","农业人工智能智慧农业机器学习精准农业-2802","10.1038\u002Fs41598-026-71390-9",{"doi":489,"openalex_id":491,"authors":492,"venue":452,"cited_by_count":78,"oa_url":449,"card":503,"direction":83,"ingested_from":85},"W7213449017",[493,496,498,500],{"name":494,"orcid":495},"Ahmad Reza Dadras","https:\u002F\u002Forcid.org\u002F0000-0001-8591-5813",{"name":497,"orcid":9},"Hossein Sabouri",{"name":499,"orcid":9},"Ali Tanhaei",{"name":501,"orcid":502},"Sayed Javad Sajadi","https:\u002F\u002Forcid.org\u002F0000-0002-6555-080X",{"tldr":504,"method":505,"finding":506,"direction":507,"opportunity":508},"用机器学习基于叶长宽非破坏性预测30个橄榄品种叶面积，PSO优化ANN精度最高。","30个品种叶长宽数据，六种ML算法结合贝叶斯、GA、PSO优化。","PSO优化ANN最优（R²=0.9828），外部8品种验证R²>0.98，通用性好。","农业遥感与作物表型","可拓展至多物种、多环境及无人机\u002F手机图像自动测量，构建通用叶面积表型平台。","2026-09-17T23:30:59.169744Z"]