[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3627":3,"related-3627":90},{"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":9,"secondary_topics":32,"classification":33,"search_phrases":64,"slug":67,"view_count":68,"doi":69,"paper":70,"created_at":89},3627,"A decision support tool for sustainable cropping pattern and irrigation management under water and nitrogen trade offs","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04797-z","Abstract Sustainable water and nitrogen management has become a major challenge for agricultural systems, particularly in regions facing increasing water scarcity and environmental degradation. Hence, this study integrated the Decision Support System for Agrotechnology Transfer (DSSAT) with a multi-objective optimization framework to evaluate optimal cropping patterns in the Darion Plain, Fars Province, Iran, during the 2022–2023 growing season. Unlike traditional single-objective approaches, the augmented ε-constraint method was employed to generate Pareto-optimal solutions that maximize economic profitability and minimize water loss, while nitrogen leaching was evaluated as an environmental outcome under a fixed nitrogen application rate. The results showed that current management practices lead to unnecessary water consumption and elevated nitrogen leaching, while optimized cropping patterns can substantially reduce environmental impacts without imposing major economic losses. In addition, water loss is translated into an implicit economic cost using a shadow price approach, allowing the economic value of water-saving scenarios to be explicitly assessed across alternative cropping patterns. Overall, this research develops a decision-support tool that helps farmers and policymakers identify cropping strategies that balance profitability with long-term environmental sustainability.","摘要 可持续的水资源和氮素管理已成为农业系统面临的重大挑战，尤其是在面临日益加剧的水资源短缺和环境退化的地区。因此，本研究将农业技术转移决策支持系统（DSSAT）与多目标优化框架相结合，以评估伊朗法尔斯省达里翁平原在2022—2023年生长季的最优种植模式。与传统单目标方法不同，本研究采用增强ε-约束方法生成帕累托最优解，以最大化经济收益并最小化水分损失，同时在固定施氮量下将氮素淋失作为环境结果进行评估。结果表明，当前管理实践导致了不必要的水资源消耗和较高的氮素淋失，而优化后的种植模式能够在不造成重大经济损失的情况下大幅降低环境影响。此外，本研究采用影子价格方法将水分损失转化为隐性经济成本，从而能够在不同种植模式下明确评估节水情景的经济价值。总体而言，本研究开发了一种决策支持工具，有助于农民和政策制定者识别兼顾盈利能力与长期环境可持续性的种植策略。",null,"Discover Sustainability","2026-09-26T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,13,9,1,"将DSSAT与多目标优化结合构建水氮权衡决策支持工具，方法有新意但属区域案例研究，对国内智慧农业与节水灌溉有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","决策支持系统","水肥一体化","灌溉管理","作物种植结构",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":41,"reason":63},[39,47,53,58],{"code":40,"score":17,"supported":41,"title":42,"abstract":44,"tags":46},"H3",true,[43],"irrigation",[45],"水资源",[30],{"code":48,"score":17,"supported":41,"title":49,"abstract":51,"tags":52},"I2",[50],"decision support",[50],[28],{"code":54,"score":22,"supported":14,"title":55,"abstract":56,"tags":57},"F3",[],[],[29],{"code":59,"score":22,"supported":14,"title":60,"abstract":61,"tags":62},"I1",[],[],[27],"多个产业证据相当，需要确认主归属",[65,66],"DSSAT 多目标优化 灌溉","伊朗 Darion Plain 种植结构","DSSAT多目标优化灌溉-3627",2,"10.1007\u002Fs43621-026-04797-z",{"doi":69,"openalex_id":71,"authors":72,"venue":10,"cited_by_count":81,"oa_url":6,"card":82,"direction":86,"ingested_from":88},"W7214492159",[73,76,79],{"name":74,"orcid":75},"Hoda Fouladi","https:\u002F\u002Forcid.org\u002F0009-0008-7156-2445",{"name":77,"orcid":78},"Mohammad Hassan Tarazkar","https:\u002F\u002Forcid.org\u002F0000-0001-9585-4460",{"name":80,"orcid":9},"Mansour Zibaei",0,{"tldr":83,"method":84,"finding":85,"direction":86,"opportunity":87},"集成DSSAT与多目标优化，为伊朗平原提供兼顾经济与水氮环境的种植决策工具。","DSSAT作物模型+增广ε-约束多目标优化，以影子价格量化水损失。","优化种植模式可大幅减少水耗与氮淋失，且不造成显著经济损失。","农业人工智能与决策模型","可将该水氮权衡框架扩展至不同气候区，并耦合实时气象与市场数据实现动态决策。","openalex","2026-09-27T23:31:29.607447Z",{"total":91,"page":22,"page_size":91,"items":92},6,[93,164,242,300,368,425],{"id":94,"title":95,"url":96,"summary":97,"summary_zh":98,"content":9,"source_name":99,"source_url":96,"published_at":100,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":101,"score_detail":102,"sources":106,"tags":108,"primary_topic":111,"secondary_topics":116,"classification":117,"search_phrases":149,"slug":152,"view_count":81,"doi":153,"paper":154,"created_at":163},5311,"AI SMART AGRO SYSTEM: A WEB-BASED AI-ASSISTED DECISION SUPPORT PLATFORM FOR FARMERS","https:\u002F\u002Fdoi.org\u002F10.56726\u002Firjmets103514","Farmers often have to decide on disease control, crop selection, soil management and irrigation without timely expert advice, which leads to yield loss and wastage of water and fertiliser.This paper presents AI Smart Agro System, a web-based decision-support platform that brings five services into one dashboard: leaf disease detection from an uploaded photograph, crop recommendation, soil health analysis, irrigation advisory and weather-based farming advice.The system is built with PHP, MySQL and JavaScript, while disease detection and crop recommendation are handled by an external Python Flask AI service that is called through a secured REST API.Soil, irrigation and weather advice are generated by transparent rule-based logic using soil pH, N-P-K values, soil moisture, rainfall, temperature and humidity.Every result is stored in the user's history, and notifications and an administrator panel support monitoring.The prototype is designed to return the disease name, confidence, treatment and prevention tips for an uploaded leaf image, to recommend the three most suitable crops, and to give clear advice for soil and water management.Quantitative evaluation of the AI models is left for future work.","农民常常需要在没有及时专家建议的情况下决定病害防治、作物选择、土壤管理和灌溉，这会导致产量损失以及水和肥料的浪费。本文提出AI Smart Agro System，这是一个基于Web的决策支持平台，将五项服务整合到一个仪表板中：根据上传照片进行叶片病害检测、作物推荐、土壤健康分析、灌溉建议以及基于天气的农事建议。该系统使用PHP、MySQL和JavaScript构建，而病害检测和作物推荐由外部Python Flask AI服务处理，并通过安全的REST API调用。土壤、灌溉和天气建议由透明的基于规则的逻辑生成，使用土壤pH、N-P-K值、土壤湿度、降雨量、温度和湿度。每个结果都存储在用户历史记录中，通知和管理员面板支持监控。该原型旨在为上传的叶片图像返回病害名称、置信度、治疗和预防提示，推荐三种最合适的作物，并为土壤和水管理提供明确建议。AI模型的定量评估留待未来工作。","International Research Journal of Modernization in Engineering Technology and Science","2026-10-17T00:00:00Z",49,{"impact":103,"substance":104,"depth":17,"authority":91,"freshness":21,"relevant":22,"comment":105},8,14,"原型系统整合病害识别、作物推荐与灌溉建议，思路清晰但缺乏定量评估，属细分技术进展，公共影响有限。",[107],{"name":99,"url":96},[27,109,28,110,30],"农业人工智能","病害识别",{"code":48,"slug":112,"name":113,"path":114,"parent_code":115},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":118,"locked":14,"version":35,"method":36,"evidence":119},"classified",{"candidates":120,"agricultural_evidence":41},[121,126,132,139,145],{"code":48,"score":122,"supported":41,"title":123,"abstract":124,"tags":125},11,[50],[],[109,28,110],{"code":40,"score":127,"supported":41,"title":128,"abstract":129,"tags":131},7,[],[130,43],"灌溉",[30],{"code":133,"score":91,"supported":41,"title":134,"abstract":135,"tags":138},"H2",[],[136,137],"土壤","soil",[],{"code":54,"score":140,"supported":41,"title":141,"abstract":142,"tags":144},3,[],[143],"肥料",[],{"code":59,"score":22,"supported":14,"title":146,"abstract":147,"tags":148},[],[],[27],[150,151],"Web 农业决策支持平台","农业人工智能 决策支持系统 智慧农业 灌溉管理","Web农业决策支持平台-5311","10.56726\u002Firjmets103514",{"doi":153,"openalex_id":155,"authors":156,"venue":99,"cited_by_count":81,"oa_url":96,"card":157,"direction":162,"ingested_from":88},"W7222785888",[],{"tldr":158,"method":159,"finding":160,"direction":86,"opportunity":161},"构建一个面向农民的网页版AI决策支持平台，集成病害识别、选种、土壤、灌溉与天气建议。","PHP\u002FMySQL\u002FJS前端，Python Flask提供病害与选种AI服务，规","原型可输出病害名称、置信度与防治建议，推荐三种适宜作物并给出水土管理建议。","AI模型尚未定量评估，可补充精度验证与田间实测，并扩展多语言、离线与本地化作物模型。","智慧农业 \u002F 农业物联网","2026-10-19T23:30:08.043132Z",{"id":165,"title":166,"url":167,"summary":168,"summary_zh":169,"content":9,"source_name":170,"source_url":167,"published_at":171,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":172,"score_detail":173,"sources":177,"tags":179,"primary_topic":183,"secondary_topics":188,"classification":189,"search_phrases":221,"slug":224,"view_count":81,"doi":9,"paper":225,"created_at":241},4673,"RESULT ON RENEWABLE ENERGY-BASED SMART FERTIGATION CONTROLLER USING NPK SENSOR","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F301","Agriculture needs good control over water, fertilizers, and energy to make crops grow better and help farming stay sustainable. Old ways of using fertilizers usually relied on looking at the fields by hand and sticking to set times, which often led to wrong use of nutrients, unbalanced soil, and using too much fertilizer. Smart fertigation systems help fix these problems by using IoT, soil nutrient sensors, and real-time tracking. This review looked at NPK-based soil checks and IoT-enabled fertigation, especially focusing on monitoring nitrogen, phosphorus, and potassium to make farming more precise. The system used an ESP32 microcontroller, an NPK sensor, a soil moisture sensor, and a DHT11 sensor to constantly monitor soil nutrients and environmental conditions. The data was shown on an LCD screen and sent via Wi-Fi to the Blynk IoT platform for remote viewing. Based on the real-time NPK levels, the right amount of fertilizer was decided, and the correct fertilizer pump was turned on manually. Solar power was added with a solar charge controller and a battery to cut down on using regular electricity and support eco-friendly farming. The studies showed that combining NPK sensors, IoT, fertigation, and renewable energy offers a great way to use fertilizers better, save resources, cut down on manual work, and help make precision agriculture more sustainable.","农业需要对水、肥料和能源进行良好控制，以使作物生长更好并帮助农业保持可持续性。传统的施肥方式通常依赖人工观察田地并遵循固定时间，这往往导致养分使用不当、土壤失衡以及施肥过量。智能水肥一体化系统通过物联网（IoT）、土壤养分传感器和实时追踪帮助解决这些问题。本综述考察了基于氮磷钾（NPK）的土壤检测和物联网支持的水肥一体化，尤其关注对氮、磷、钾的监测，以使农业更加精准。该系统使用ESP32微控制器、NPK传感器、土壤湿度传感器和DHT11传感器持续监测土壤养分和环境条件。数据在LCD屏幕上显示，并通过Wi-Fi发送到Blynk物联网平台进行远程查看。根据实时NPK水平，确定合适的肥料用量，并手动开启正确的肥料泵。系统还加入了太阳能供电，配有太阳能充电控制器和电池，以减少常规电力使用并支持生态友好型农业。研究表明，将NPK传感器、物联网、水肥一体化和可再生能源相结合，为更好地利用肥料、节约资源、减少人工劳动以及帮助精准农业实现更可持续发展提供了一种极佳途径。","International Journal of Mechanical Engineering Research and Technology","2026-10-08T00:00:00Z",54,{"impact":103,"substance":174,"depth":104,"authority":17,"freshness":175,"relevant":22,"comment":176},16,4,"综述性论文，将NPK传感、物联网与光伏供电结合于水肥一体化控制，方法集成有参考价值，但属实验室\u002F综述层面，公共影响有限且时效偏旧。",[178],{"name":170,"url":167},[27,180,29,181,182],"农业物联网","精准灌溉","光伏农业",{"code":54,"slug":184,"name":185,"path":186,"parent_code":187},"fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[],{"status":118,"locked":14,"version":35,"method":36,"evidence":190},{"candidates":191,"agricultural_evidence":41},[192,200,206,213,217],{"code":54,"score":193,"supported":41,"title":194,"abstract":196,"tags":199},18,[195],"fertigation",[197,143,29,198,195],"施肥","fertilizer",[29],{"code":59,"score":127,"supported":41,"title":201,"abstract":202,"tags":205},[],[203,204],"精准农业","precision agriculture",[27],{"code":207,"score":127,"supported":41,"title":208,"abstract":209,"tags":212},"I5",[],[210,211],"物联网","IoT",[180],{"code":133,"score":91,"supported":41,"title":214,"abstract":215,"tags":216},[],[136,137],[],{"code":40,"score":22,"supported":14,"title":218,"abstract":219,"tags":220},[],[],[181],[222,223],"ESP32 NPK传感器 水肥一体化","太阳能 智能灌溉 控制器","ESP32NPK传感器水肥一体化-4673",{"doi":9,"openalex_id":226,"authors":227,"venue":170,"cited_by_count":81,"oa_url":167,"card":236,"direction":162,"ingested_from":88},"W7221424039",[228,230,232,234],{"name":229,"orcid":9},"Niharika Bodake",{"name":231,"orcid":9},"Akshata Gaurkar",{"name":233,"orcid":9},"Vaishnavi Chavan",{"name":235,"orcid":9},"S. K. Godase",{"tldr":237,"method":238,"finding":239,"direction":162,"opportunity":240},"综述基于NPK传感器、物联网与太阳能的智能水肥一体化控制器，实现精准施肥与远程监控。","ESP32、NPK\u002F土壤湿度\u002FDHT11传感器、Blynk物联网平台与太阳能供电","融合NPK传感、物联网、水肥一体化与可再生能源可优化施肥、节约资源并减少人工。","可研究基于实时NPK数据的自动施肥决策算法与闭环控制，替代当前手动开启施肥泵。","2026-10-11T23:30:16.530895Z",{"id":243,"title":244,"url":245,"summary":246,"summary_zh":247,"content":9,"source_name":170,"source_url":245,"published_at":171,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":248,"score_detail":249,"sources":251,"tags":253,"primary_topic":255,"secondary_topics":256,"classification":257,"search_phrases":283,"slug":286,"view_count":68,"doi":9,"paper":287,"created_at":299},4669,"REVIEW ON RENEWABLE ENERGY-BASED SMART FERTIGATION CONTROLLER USING NPK SENSOR","https:\u002F\u002Fijmert.com\u002Findex.php\u002Fijmert\u002Farticle\u002Fview\u002F302","Agriculture will require efficient management of water, fertilizers, and energy to improve crop productivity and support sustainable farming practices. Traditional fertilizer application methods will often depend on manual observation and fixed schedules, which will lead to improper nutrient management, nutrient imbalance, and unnecessary fertilizer usage. Smart fertigation systems will provide an effective approach for addressing these limitations by integrating IoT, soil nutrient sensing, and real-time monitoring technologies. In this review, NPK-based soil monitoring and IoT-enabled fertigation approaches will be studied with emphasis on nitrogen, phosphorus, and potassium monitoring for precision agriculture. The proposed system will use an ESP32 microcontroller, NPK sensor, soil moisture sensor, and DHT11 sensor to monitor soil nutrient and environmental conditions in real time. The collected data will be displayed on an LCD and transmitted through Wi-Fi to the Blynk IoT platform for remote monitoring. Based on real-time NPK readings, the fertilizer requirement will be determined, and the appropriate fertilizer-supply motor will be manually operated. Solar energy will be integrated with a solar charge controller and rechargeable battery to reduce dependence on conventional electricity and support sustainable operation. The reviewed approaches will indicate that the integration of NPK sensors, IoT monitoring, fertigation, and renewable energy will provide a promising solution for improving fertilizer utilization, reducing resource wastage, minimizing manual effort, and supporting sustainable precision agriculture.","农业需要高效管理水、肥料和能源，以提高作物生产力并支持可持续农业实践。传统施肥方法往往依赖人工观察和固定日程，这会导致养分管理不当、养分失衡和不必要的肥料使用。智能水肥一体化系统通过整合物联网（IoT）、土壤养分传感和实时监测技术，为解决这些局限提供了一种有效途径。本文综述了基于氮磷钾（NPK）的土壤监测和物联网水肥一体化方法，重点关注精准农业中的氮、磷、钾监测。所提出的系统将使用ESP32微控制器、NPK传感器、土壤湿度传感器和DHT11传感器实时监测土壤养分和环境条件。采集的数据将显示在LCD上，并通过Wi-Fi传输至Blynk物联网平台进行远程监测。根据实时NPK读数确定肥料需求量，并手动操作相应的供肥电机。将太阳能与太阳能充电控制器和可充电电池集成，以减少对传统电力的依赖并支持可持续运行。综述的方法表明，将NPK传感器、物联网监测、水肥一体化和可再生能源相结合，为提高肥料利用率、减少资源浪费、降低人工投入和支持可持续精准农业提供了一种有前景的解决方案。",46,{"impact":103,"substance":104,"depth":17,"authority":91,"freshness":91,"relevant":22,"comment":250},"综述性论文，提出基于ESP32与NPK传感器的太阳能智能水肥一体化方案，有一定技术参考价值，但属常规技术集成，非重大突破，且期刊权威性一般。",[252],{"name":170,"url":245},[27,210,203,29,254],"可再生能源",{"code":54,"slug":184,"name":185,"path":186,"parent_code":187},[],{"status":118,"locked":14,"version":35,"method":36,"evidence":258},{"candidates":259,"agricultural_evidence":41},[260,265,269,273,277],{"code":54,"score":193,"supported":41,"title":261,"abstract":262,"tags":264},[195],[197,143,29,263,198,195],"养分管理",[29],{"code":59,"score":103,"supported":41,"title":266,"abstract":267,"tags":268},[],[203,204],[27,203],{"code":207,"score":127,"supported":41,"title":270,"abstract":271,"tags":272},[],[210,211],[210],{"code":133,"score":91,"supported":41,"title":274,"abstract":275,"tags":276},[],[136,137],[],{"code":278,"score":140,"supported":41,"title":279,"abstract":280,"tags":282},"H8",[],[281],"可持续农业",[],[284,285],"ESP32 NPK传感器 智能灌溉","太阳能 水肥一体化 控制器","ESP32NPK传感器智能灌溉-4669",{"doi":9,"openalex_id":288,"authors":289,"venue":170,"cited_by_count":81,"oa_url":245,"card":294,"direction":162,"ingested_from":88},"W7221368129",[290,291,292,293],{"name":229,"orcid":9},{"name":231,"orcid":9},{"name":233,"orcid":9},{"name":235,"orcid":9},{"tldr":295,"method":296,"finding":297,"direction":162,"opportunity":298},"综述基于NPK传感器与物联网的可再生能源智能水肥一体化控制器方案。","ESP32、NPK\u002F土壤湿度\u002FDHT11传感器、Blynk平台与太阳能供电。","NPK传感+物联网+水肥一体化+可再生能源可提升肥料利用率并减少浪费。","肥料供给仍靠手动，可研究基于实时NPK的自动决策与闭环变量施肥控制。","2026-10-11T23:30:16.034599Z",{"id":301,"title":302,"url":303,"summary":304,"summary_zh":305,"content":9,"source_name":306,"source_url":303,"published_at":307,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":308,"score_detail":309,"sources":312,"tags":314,"primary_topic":318,"secondary_topics":319,"classification":320,"search_phrases":347,"slug":350,"view_count":22,"doi":351,"paper":352,"created_at":367},4559,"Comparative analysis of gradient boosting and random forest regressors for nutrient prediction in smart agriculture","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-74825-5","Accurate estimation of nutrient concentrations can support monitoring in hydroponic systems. This study compares Random Forest Regressor (RFR) and Gradient Boosting Regressor (GBR) for predicting nitrogen, phosphorus, and potassium from pH, electrical conductivity, temperature, and relative humidity in a precompiled agricultural sensor dataset. The framework uses mean imputation, correlation-based feature selection, and ensemble regression. Observations were randomly divided into 80% training and 20% test partitions; preprocessing, feature selection, and grid-search optimization with five-fold cross-validation were confined to the training partition. On the held-out test partition, GBR achieved lower MAE and RMSE and higher R² than RFR for all three nutrients. Unweighted arithmetic means of the reported nutrient-specific test metrics were MAE 3.60 versus 4.06, mean nutrient RMSE 4.22 versus 4.90, and mean nutrient R² 0.89 versus 0.84 for GBR and RFR, respectively. These summaries were calculated from the rounded nutrient-specific entries and are not pooled metrics. The comparison rests on one train–test split without quantified variability because fold-level scores were not retained and repeated-seed evaluations were unavailable. Missing acquisition metadata and unverified nutrient units further limit interpretation. The findings support GBR as a candidate for nutrient estimation within this dataset; external validation and operational evaluation are required before use in automated fertigation.","在水培系统中，准确估算养分浓度有助于实现有效监测。本研究基于一个预编译的农业传感器数据集，比较了随机森林回归器（Random Forest Regressor, RFR）与梯度提升回归器（Gradient Boosting Regressor, GBR）在利用pH、电导率、温度和相对湿度预测氮、磷、钾方面的性能。该框架采用均值插补、基于相关性的特征选择和集成回归。观测数据被随机划分为80%的训练集和20%的测试集；预处理、特征选择以及基于五折交叉验证的网格搜索优化均仅限于训练集内进行。在留出的测试集上，GBR对三种养分的预测均取得了比RFR更低的MAE和RMSE以及更高的R²。对报告中各养分测试指标取未加权算术平均值，GBR和RFR的结果分别为：MAE 3.60对4.06，平均养分RMSE 4.22对4.90，平均养分R² 0.89对0.84。这些汇总值系根据四舍五入后的各养分指标计算得出，并非合并指标。该比较仅基于一次训练-测试划分，且未量化变异性，因为未保留折级评分，也无法进行重复随机种子评估。缺失的采集元数据和未经核实的养分单位进一步限制了结果解读。研究结果表明，在该数据集内GBR可作为养分估算的候选方法；在用于自动化施肥之前，仍需进行外部验证和运行评估。","Scientific Reports","2026-10-09T00:00:00Z",63,{"impact":103,"substance":193,"depth":310,"authority":104,"freshness":103,"relevant":22,"comment":311},15,"方法对比扎实但仅单次划分、缺乏外部验证，属细分领域技术进展，未达每日精选门槛。",[313],{"name":306,"url":303},[27,315,29,316,317],"机器学习","养分预测","无土栽培",{"code":54,"slug":184,"name":185,"path":186,"parent_code":187},[],{"status":118,"locked":14,"version":35,"method":36,"evidence":321},{"candidates":322,"agricultural_evidence":41},[323,328,332,338,343],{"code":59,"score":21,"supported":41,"title":324,"abstract":326,"tags":327},[325],"smart agriculture",[],[27],{"code":54,"score":127,"supported":41,"title":329,"abstract":330,"tags":331},[],[197,195],[29],{"code":333,"score":175,"supported":41,"title":334,"abstract":335,"tags":337},"B7",[],[336],"hydroponic",[317],{"code":207,"score":140,"supported":41,"title":339,"abstract":340,"tags":342},[],[341],"农业传感",[],{"code":48,"score":22,"supported":14,"title":344,"abstract":345,"tags":346},[],[],[315],[348,349],"随机森林 梯度提升 养分预测","水培 氮磷钾 传感器","随机森林梯度提升养分预测-4559","10.1038\u002Fs41598-026-74825-5",{"doi":351,"openalex_id":353,"authors":354,"venue":306,"cited_by_count":81,"oa_url":303,"card":362,"direction":162,"ingested_from":88},"W7221072509",[355,358,360],{"name":356,"orcid":357},"Shaista Farhat","https:\u002F\u002Forcid.org\u002F0009-0008-9348-8370",{"name":359,"orcid":9},"Dr. Chokka Anuradha",{"name":361,"orcid":9},"Dawit Tafesse Gebreyohannes",{"tldr":363,"method":364,"finding":365,"direction":162,"opportunity":366},"比较梯度提升与随机森林回归器，用水培传感器数据预测氮磷钾浓度。","预处理、相关性特征选择、网格搜索五折交叉验证，对比GBR与RFR。","GBR在三种养分预测上MAE、RMSE更低、R²更高，但仅单次划分且缺变异量化。","可开展多源传感器融合、重复种子评估与外部验证，提升养分预测稳健性并接入自动施肥。","2026-10-10T23:30:07.557842Z",{"id":369,"title":370,"url":371,"summary":372,"summary_zh":373,"content":9,"source_name":374,"source_url":371,"published_at":375,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":376,"score_detail":377,"sources":380,"tags":382,"primary_topic":385,"secondary_topics":389,"classification":391,"search_phrases":407,"slug":410,"view_count":81,"doi":411,"paper":412,"created_at":424},4498,"SMART CROP SELECTION MODEL USING SOIL PH VALUE, TEMPERATURE, HUMIDITY, AND RAINFALL PREDICTION","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23211381","Crop suitability with soil and climatic conditions plays a vital role in increasing productivity of agriculture. This study proposes a smart crop selection model which recommends the suitable crop based on the machine learning techniques with the input parameters of soil pH, soil temperature, soil humidity and rainfall. The data set consisted of 1000 observations and 8 different crop classes and missing values were handled, crop labels were encoded and numerical features were selected. Five-fold cross validation was used to compare the performance of the three classifiers: Random Forest, XGBoost and Support Vector Machine (SVM). Finally, a Gradient-Boosted classification model was trained and evaluated with test-set accuracy, classification metrics, Multiclass ROC analysis and feature importance. The results of the application were 93.60% of the final test accuracy and the model comparison was used to compare the three classification methods. The contribution of four environmental parameters to the crop classification was analyzed by feature-importance analysis. To facilitate practical accessibility, the entire process was carried out using an interactive dashboard developed with Streamlit, allowing for the upload of datasets, evaluation of models, selection of parameters and the generation of predictions in real time for the crops of interest. The developed system is an example of how machine learning can be integrated with an interactive decision support interface to develop a data-driven, crop selection system, relying on simple soil and weather parameters.","作物适宜性与土壤及气候条件对提高农业生产率至关重要。本研究提出了一种智能作物选择模型，该模型基于机器学习技术，以土壤pH值、土壤温度、土壤湿度和降雨量为输入参数，推荐适宜的作物。数据集包含1000个观测值和8个不同作物类别，对缺失值进行了处理，对作物标签进行了编码，并筛选了数值特征。采用五折交叉验证比较了三种分类器——随机森林（Random Forest）、XGBoost和支持向量机（Support Vector Machine, SVM）的性能。最终，训练了一个梯度提升分类模型，并通过测试集准确率、分类指标、多类ROC分析和特征重要性进行了评估。应用结果为最终测试准确率93.60%，并通过模型比较对三种分类方法进行了对比。通过特征重要性分析，探讨了四个环境参数对作物分类的贡献。为便于实际应用，整个流程通过使用Streamlit开发的交互式仪表板实现，支持数据集上传、模型评估、参数选择以及针对感兴趣作物的实时预测生成。所开发的系统展示了如何将机器学习与交互式决策支持界面相结合，构建一个数据驱动的作物选择系统，且仅依赖于简单的土壤和气象参数。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-07T00:00:00Z",69,{"impact":17,"substance":378,"depth":174,"authority":20,"freshness":103,"relevant":22,"comment":379},20,"基于土壤pH、温湿度与降雨的机器学习选种模型，含93.6%测试精度与Streamlit交互看板，方法完整但属常规应用型研究，产业影响有限。",[381],{"name":374,"url":371},[27,109,28,383,384],"土壤墒情","作物选择",{"code":133,"slug":137,"name":386,"path":387,"parent_code":388},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[390],{"code":48,"slug":112,"name":113,"path":114,"parent_code":115},{"status":118,"locked":14,"version":35,"method":36,"evidence":392},{"candidates":393,"agricultural_evidence":41},[394,398,403],{"code":133,"score":310,"supported":41,"title":395,"abstract":396,"tags":397},[137],[136,137],[383],{"code":48,"score":122,"supported":41,"title":399,"abstract":400,"tags":402},[],[315,401,50],"machine learning",[109,28],{"code":59,"score":22,"supported":14,"title":404,"abstract":405,"tags":406},[],[],[27],[408,409],"土壤pH 降雨量 作物选择模型","机器学习 作物推荐 智慧农业","土壤pH降雨量作物选择模型-4498","10.5281\u002Fzenodo.23211381",{"doi":411,"openalex_id":413,"authors":414,"venue":374,"cited_by_count":81,"oa_url":371,"card":419,"direction":162,"ingested_from":88},"W7220991946",[415,417],{"name":416,"orcid":9},"C. SAI KUMAR",{"name":418,"orcid":9},"Prof. G SREENIVASULU",{"tldr":420,"method":421,"finding":422,"direction":86,"opportunity":423},"基于土壤pH、温湿度和降雨，用机器学习构建智能选种模型并开发交互式仪表板。","1000条8类作物数据，五折交叉验证比较RF、XGBoost、SVM，最终用梯度","梯度提升模型测试准确率达93.60%，特征重要性揭示四参数对选种的贡献。","可引入时序气象与多源土壤数据，提升模型泛化性并适配不同区域作物推荐。","2026-10-09T23:30:12.285129Z",{"id":426,"title":427,"url":428,"summary":429,"summary_zh":9,"content":430,"source_name":431,"source_url":9,"published_at":432,"category":433,"cover_url":9,"hotness":13,"is_selected":14,"score":434,"score_detail":435,"sources":438,"tags":440,"primary_topic":443,"secondary_topics":449,"classification":455,"search_phrases":502,"slug":505,"view_count":81,"doi":9,"paper":9,"created_at":506},4460,"中国农科院资划所牵头智慧农作体系观摩会在山东桓台举行 千亩玉米单产提升8%—12%","https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-10\u002F03\u002Fcontent_591573.html","中国农业科学院农业资源与农业区划研究所牵头粮食作物精准智慧化生产管理关键技术与集成示范项目黄淮海麦玉轮作主产区智慧农作体系应用示范观摩会在山东桓台中化MAP桓台农场举行；项目构建以多源感知为基础、智能诊断为核心、农机精准执行为保障、数据平台为中枢的智慧农作技术体系，千亩示范方玉米单产较周边常规种植对照提升8%—12%，优质品率稳定在88%以上，化肥、农药、灌溉用水量较常规种植分别减少10%—15%、8%—10%、12%—18%。","![Image 4](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Findex\u002Fimages\u002Fshare_logo.jpg)\n\n[![Image 5](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Findex\u002Fimages\u002Flogo.png)](http:\u002F\u002Fwww.stdaily.com\u002F)\n\n*   [时政](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fszxw\u002Fnode_357.html)\n*   [热点](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Frdxw\u002Fnode_327.html)\n*   [政务](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fzwxw\u002Fnode_362.html)\n*   [深瞳](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fstbd\u002Fnode_434.html)\n*   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6](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimages\u002Ffdj.png)](https:\u002F\u002Fsearch.stdaily.com:8888\u002Ffounder\u002FNewSearchServlet.do?siteID=1)[登录](https:\u002F\u002Fwww.stdaily.com\u002Fapi\u002Fwebsite\u002FpersonalCenter.html?url=https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-10\u002F03\u002Fcontent_591573.html)[-](https:\u002F\u002Fwww.stdaily.com\u002Fapi\u002Fwebsite\u002FpersonalCenter.html)\n\n 所在位置： [中国科技网首页](https:\u002F\u002Fwww.stdaily.com\u002Findex.html)>[滚动](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fnode_324.html)> 正文 \n\n# 智慧农作体系应用示范观摩会在山东桓台举行\n\n 2026-10-03 14:47:23 来源: 科技日报 点击数：\n\n![Image 7](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimg\u002F20240301dzv.png)0\n\n[](javascript:; \"新浪微博\")[](javascript:; \"微信\")\n\n科技日报记者 马爱平\n\n近日，由中国农业科学院农业资源与农业区划研究所牵头的“粮食作物精准智慧化生产管理关键技术与集成示范”项目黄淮海麦玉轮作主产区智慧农作体系应用示范观摩会，在山东桓台中化MAP桓台农场举行。\n\n与会人员实地观摩项目针对黄淮海小麦—玉米轮作精准智慧生产现实需求，围绕麦玉种、肥、水、药管理关键环节研发的作物表型监测、精量播种、养分诊断与变量追肥、精准灌溉、病虫害监测与变量喷施等系列新技术、新装备，现场考察了集实时监测、智能诊断、定量调控、变量作业于一体，农艺农机信息深度融合的区域智慧农作技术体系及示范成效。\n\n![Image 8](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-10\u002F03\u002F591573_4296d9a8-883a-4a2a-98e1-7290f3adff4fcopy.jpg)\n\n据中化MAP桓台农场负责人介绍，这套数字技术解决方案通过精准播种、变量施肥和智能灌溉等核心环节的优化，实现千亩示范方玉米单产较周边常规种植对照提升8%—12%，优质品率稳定在88%以上；同时，实现农业资源的节约，化肥、农药和灌溉用水量较常规种植分别减少10%—15%、8%—10%、12%—18%，资源利用效率得到显著提升，目前已形成面向周边普通农户和新型经营主体的技术培训、推广和辐射服务能力。\n\n![Image 9](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-10\u002F03\u002F591573_e7499078-dee9-4a40-bf66-504e09cef576copy.jpg)\n\n项目首席科学家吴文斌研究员表示，玉米是我国种植面积最大、产量最高的粮食作物。但长期以来，我国玉米种植生产管理整体偏粗放。播种质量参差不齐，关键农情信息获取滞后，水肥药施用多依赖传统经验，不仅造成玉米单产与先进国家相比有较大差距，也带来农业资源浪费、农田生态压力加大等问题。为此，项目组联合中化MAP团队，选择灌溉条件完善、机械化基础扎实的MAP桓台农场作为核心基地，打造玉米生产方式数字化转型升级示范区。该项目构建一套以多源感知为基础、智能诊断为核心、农机精准执行为保障、数据平台为中枢的智慧农作技术体系，实现玉米种植全过程的数据驱动和精准管控。\n\n（中国农业科学院农业资源与农业区划研究所供图）\n\n责任编辑：陈可轩\n\n相关稿件：\n\n![Image 10](https:\u002F\u002Fstatics.kjrb.com.cn\u002Fresource\u002Fkjw\u002Fimages\u002Fpinglun.png)网友评论\n\n评论\n\n最热评论\n\n没有更多评论了\n\n[热点](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Frdxw\u002Fnode_327.html)\n\n[![Image 11](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002Fpic\u002F2026-10\u002F08\u002F592769_1df74a15-67d5-4910-aece-debcc638606a_1009x6734106423copy.JPG)](https:\u002F\u002Fwww.stdaily.com\u002Fweb\u002Fgdxw\u002F2026-10\u002F08\u002Fcontent_592769.html)\n\n[国庆假期服务消费旺盛 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