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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","论文",false,54,{"impact":173,"substance":585,"depth":345,"authority":416,"freshness":400,"relevant":455,"comment":586},16,"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。",[588],{"name":579,"url":576},[590,591,592,593,594],"智慧农业","农业物联网","水肥一体化","精准灌溉","光伏农业",{"code":96,"slug":97,"name":98,"path":99,"parent_code":72},[],{"status":598,"locked":582,"version":4,"method":599,"evidence":600},"classified","rules",{"candidates":601,"agricultural_evidence":603},[602,611,618,624,630],{"code":96,"score":361,"supported":603,"title":604,"abstract":606,"tags":610},true,[605],"fertigation",[607,608,592,609,605],"施肥","肥料","fertilizer",[592],{"code":16,"score":612,"supported":603,"title":613,"abstract":614,"tags":617},7,[],[615,616],"精准农业","precision agriculture",[590],{"code":48,"score":612,"supported":603,"title":619,"abstract":620,"tags":623},[],[621,622],"物联网","IoT",[591],{"code":144,"score":625,"supported":603,"title":626,"abstract":627,"tags":629},6,[],[628,145],"土壤",[],{"code":152,"score":455,"supported":582,"title":631,"abstract":632,"tags":633},[],[],[593],[635,636],"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":11,"openalex_id":639,"authors":640,"venue":579,"cited_by_count":424,"oa_url":576,"card":649,"direction":653,"ingested_from":655},"W7221424039",[641,643,645,647],{"name":642,"orcid":11},"Niharika Bodake",{"name":644,"orcid":11},"Akshata Gaurkar",{"name":646,"orcid":11},"Vaishnavi Chavan",{"name":648,"orcid":11},"S. K. Godase",{"tldr":650,"method":651,"finding":652,"direction":653,"opportunity":654},"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。","智慧农业 \u002F 农业物联网","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。","openalex","2026-10-11T23:30:16.530895Z",{"id":658,"title":659,"url":660,"summary":661,"summary_zh":662,"content":11,"source_name":579,"source_url":660,"published_at":580,"category":581,"cover_url":11,"hotness":385,"is_selected":582,"score":663,"score_detail":664,"sources":667,"tags":669,"primary_topic":673,"secondary_topics":674,"classification":677,"search_phrases":704,"slug":707,"view_count":424,"doi":11,"paper":708,"created_at":724},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。",61,{"impact":416,"substance":585,"depth":665,"authority":416,"freshness":625,"relevant":455,"comment":666},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。",[668],{"name":579,"url":660},[590,670,671,591,672],"农业人工智能","精准施肥","智能农机",{"code":96,"slug":97,"name":98,"path":99,"parent_code":72},[675,676],{"code":24,"slug":25,"name":26,"path":27,"parent_code":7},{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},{"status":598,"locked":582,"version":4,"method":599,"evidence":678},{"candidates":679,"agricultural_evidence":603},[680,684,691,696,700],{"code":96,"score":665,"supported":603,"title":681,"abstract":682,"tags":683},[609],[608,609],[671],{"code":24,"score":385,"supported":603,"title":685,"abstract":686,"tags":690},[],[687,688,689],"机器学习","machine learning","artificial intelligence",[670],{"code":48,"score":385,"supported":603,"title":692,"abstract":693,"tags":695},[],[621,694,622],"internet of things",[591],{"code":16,"score":612,"supported":603,"title":697,"abstract":698,"tags":699},[],[615,616],[590],{"code":112,"score":455,"supported":582,"title":701,"abstract":702,"tags":703},[],[],[672],[705,706],"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":11,"openalex_id":709,"authors":710,"venue":579,"cited_by_count":424,"oa_url":660,"card":719,"direction":653,"ingested_from":655},"W7221394563",[711,713,715,717],{"name":712,"orcid":11},"Anjali Manoj Kumbhar",{"name":714,"orcid":11},"Shruti Prashant Kulkarni",{"name":716,"orcid":11},"Snehal Santosh Khune",{"name":718,"orcid":11},"B. B. Godbole",{"tldr":720,"method":721,"finding":722,"direction":653,"opportunity":723},"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。","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":726,"title":727,"url":728,"summary":729,"summary_zh":730,"content":11,"source_name":731,"source_url":728,"published_at":732,"category":581,"cover_url":11,"hotness":385,"is_selected":582,"score":733,"score_detail":734,"sources":737,"tags":739,"primary_topic":741,"secondary_topics":742,"classification":745,"search_phrases":781,"slug":784,"view_count":69,"doi":785,"paper":786,"created_at":800},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":361,"substance":321,"depth":361,"authority":735,"freshness":173,"relevant":455,"comment":736},13,"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。",[738],{"name":731,"url":728},[590,670,591,740,593],"小农户",{"code":96,"slug":97,"name":98,"path":99,"parent_code":72},[743,744],{"code":48,"slug":49,"name":50,"path":51,"parent_code":7},{"code":496,"slug":497,"name":498,"path":499,"parent_code":480},{"status":598,"locked":582,"version":4,"method":599,"evidence":746},{"candidates":747,"agricultural_evidence":603},[748,753,757,762,768,772,777],{"code":96,"score":749,"supported":603,"title":750,"abstract":751,"tags":752},17,[605],[607,592,605],[],{"code":48,"score":665,"supported":603,"title":754,"abstract":755,"tags":756},[622],[621,622],[591],{"code":496,"score":416,"supported":603,"title":758,"abstract":760,"tags":761},[759],"smallholder",[759],[740],{"code":24,"score":763,"supported":603,"title":764,"abstract":766,"tags":767},9,[765],"decision support",[],[670],{"code":144,"score":625,"supported":603,"title":769,"abstract":770,"tags":771},[],[628,145],[],{"code":152,"score":400,"supported":603,"title":773,"abstract":774,"tags":776},[],[775],"灌溉",[593],{"code":16,"score":455,"supported":582,"title":778,"abstract":779,"tags":780},[],[],[590],[782,783],"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":785,"openalex_id":787,"authors":788,"venue":731,"cited_by_count":424,"oa_url":728,"card":795,"direction":653,"ingested_from":655},"W7213663876",[789,791,793],{"name":790,"orcid":11},"Awojide S.",{"name":792,"orcid":11},"Ikpotokin F.O.",{"name":794,"orcid":11},"Sadiq F.I.",{"tldr":796,"method":797,"finding":798,"direction":653,"opportunity":799},"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",{"technologies":802,"keywords":830},[803,804,805,806,808,810,812,813,815,817,819,821,823,825,827,829],{"name":670,"count":612},{"name":615,"count":612},{"name":591,"count":571},{"name":807,"count":571},"遥感监测",{"name":809,"count":69},"农业机器人",{"name":811,"count":69},"农业遥感",{"name":687,"count":69},{"name":814,"count":69},"遥感",{"name":816,"count":455},"人工智能",{"name":818,"count":455},"作物表型",{"name":820,"count":455},"农业大模型",{"name":822,"count":455},"农产品溯源",{"name":824,"count":455},"分子育种",{"name":826,"count":455},"图像识别",{"name":828,"count":455},"大模型",{"name":621,"count":455},[831,833,835,836,838,840,841,842,844,846,848,849],{"name":590,"count":832},42,{"name":592,"count":834},24,{"name":671,"count":735},{"name":837,"count":625},"科学施肥",{"name":839,"count":408},"变量施肥",{"name":740,"count":408},{"name":218,"count":408},{"name":843,"count":400},"化肥减量",{"name":845,"count":400},"单产提升",{"name":847,"count":400},"数字乡村",{"name":672,"count":400},{"name":210,"count":400}]