[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3314":3,"related-3314":36},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"search_phrases":30,"slug":33,"view_count":34,"doi":8,"paper":8,"created_at":35},3314,"《安徽日报》：深耕沃土四十载 智绘粮仓千亿斤——省农科院青年博士团队接过砂姜黑土改造接力棒 研发高产稳产技术套餐","https:\u002F\u002Fwww.aaas.org.cn\u002F4303153\u002F71232014.html","9月19日，安徽省农业科学院农业科技成果转化应用项目砂姜黑土障碍消减与作物周年高产高效技术转化应用观摩培训会在蒙城县举办。蒙城县马店试验站的长期定位试验田里，土壤墒情监测仪无声运转，将地下不同深度的土壤水分、温度数据上传云端；田埂上，青年科研人员轻点平板电脑，一张彩色施肥处方图跃然屏上。1978年起，省农科院土肥所在马店建立研究基站——蒙城马店试验站，是目前全国研究历史最长、数据最为连续的砂姜黑土长期监测定位点。安徽砂姜黑土面积2300余万亩，约占全国总面积的45%。",null,"安徽省农业科学院 2026年09月19日","2026-09-19T00:00:00Z","报道",10,false,69,{"impact":16,"substance":16,"depth":17,"authority":18,"freshness":19,"relevant":20,"comment":21},18,15,12,6,1,"省级科研机构长期定位试验与数字化土壤监测、施肥处方图结合的实质报道，具行业参考价值，但属区域进展，时效略滞后。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","土壤监测","粮食产能","砂姜黑土","施肥处方图",[31,32],"安徽省农科院 砂姜黑土 蒙城","砂姜黑土 施肥处方图 高产技术","安徽省农科院砂姜黑土蒙城-3314",0,"2026-09-24T00:04:00.397518Z",{"total":19,"page":20,"page_size":19,"items":37},[38,85,120,160,203,237],{"id":39,"title":40,"url":41,"summary":42,"summary_zh":43,"content":8,"source_name":44,"source_url":41,"published_at":45,"category":46,"cover_url":8,"hotness":12,"is_selected":13,"score":47,"score_detail":48,"sources":52,"tags":54,"search_phrases":58,"slug":61,"view_count":34,"doi":62,"paper":63,"created_at":84},2777,"Satellite Positioning-Based Precision Irrigation and Crop Health Monitoring System","https:\u002F\u002Fdoi.org\u002F10.38124\u002Fijisrt\u002F26aug1577","Precision agriculture requires timely information on spatial variations in soil and environmental conditions to improve irrigation efficiency and crop productivity. This paper presents a low-cost satellite positioning-based precision irrigation and crop health monitoring system using an ESP32 microcontroller, soil-moisture sensor, DHT22 temperature and relative-humidity sensor, light-intensity sensor, NPK sensor, pH sensor, and NEO-6 GPS receiver. The system acquires soil, environmental, nutrient, and positional data and associates each measurement with its geographical coordinates. Soil moisture is used as the primary parameter for irrigation control, while temperature, relative humidity, light intensity, NPK, and pH provide supplementary indicators of crop and soil conditions. The GPS receiver enables spatial tagging of measurements for location-specific field monitoring and irrigation decisions. The results demonstrate sensor-based irrigation operation, spatial variability in field conditions, GPS positioning within approximately 3–5 m under open-sky conditions, and 29.3% reduction in water consumption compared with fixed-time irrigation. The proposed system provides a low-cost platform for spatially informed irrigation and crop-condition monitoring, with potential application in small- and medium-scale agricultural fields.","精准农业需要及时获取土壤和环境条件的空间变异信息，以提高灌溉效率和作物生产力。本文提出了一种基于低成本卫星定位的精准灌溉与作物健康监测系统，采用ESP32微控制器、土壤湿度传感器、DHT22温湿度传感器、光照强度传感器、NPK传感器、pH传感器和NEO-6 GPS接收器。该系统采集土壤、环境、养分和位置数据，并将每次测量与其地理坐标关联。土壤湿度作为灌溉控制的主要参数，而温度、相对湿度、光照强度、NPK和pH则提供作物和土壤条件的辅助指标。GPS接收器实现测量的空间标记，以支持特定位置的田间监测和灌溉决策。结果表明，基于传感器的灌溉操作可行，田间条件存在空间变异，在开阔天空条件下GPS定位精度约为3–5 m，与固定时间灌溉相比用水量减少了29.3%。所提出的系统为空间信息驱动的灌溉和作物状况监测提供了一个低成本平台，具有在中小型农田中应用的潜力。","International Journal of Innovative Science and Research Technology (IJISRT)","2026-09-15T00:00:00Z","论文",56,{"impact":49,"substance":16,"depth":17,"authority":19,"freshness":50,"relevant":20,"comment":51},8,9,"低成本ESP32+GPS精准灌溉系统，实测节水29.3%，方法具体数据可信，但属实验室\u002F小规模验证，产业影响有限，可作为智慧农业技术案例入选。",[53],{"name":44,"url":41},[25,55,56,26,57],"物联网","精准灌溉","卫星定位",[59,60],"卫星定位 土壤监测 智慧农业 精准灌溉","卫星定位 土壤监测","卫星定位土壤监测智慧农业精准灌溉-2777","10.38124\u002Fijisrt\u002F26aug1577",{"doi":62,"openalex_id":64,"authors":65,"venue":44,"cited_by_count":34,"oa_url":76,"card":77,"direction":81,"ingested_from":83},"W7213362841",[66,68,70,72,74],{"name":67,"orcid":8},"O. A. Akintola",{"name":69,"orcid":8},"A. N. Lawal",{"name":71,"orcid":8},"O. B. Goodtalk",{"name":73,"orcid":8},"J.O. Ayankale",{"name":75,"orcid":8},"I. Mafiana","https:\u002F\u002Fwww.ijisrt.com\u002Fassets\u002Fupload\u002Ffiles\u002FIJISRT26AUG1577.pdf",{"tldr":78,"method":79,"finding":80,"direction":81,"opportunity":82},"开发基于ESP32和GPS的低成本精准灌溉与作物健康监测系统，实现空间标记与节水。","ESP32、土壤湿度、DHT22、光照、NPK、pH、NEO-6 GPS，以土壤","GPS定位精度3-5米，相比定时灌溉节水29.3%，并揭示田间空间变异。","智慧农业 \u002F 农业物联网","可研究多传感器融合的智能决策算法，或结合低功耗广域网实现大田远程监测与精准灌溉。","openalex","2026-09-17T23:30:18.174616Z",{"id":86,"title":87,"url":88,"summary":89,"summary_zh":8,"content":8,"source_name":90,"source_url":88,"published_at":91,"category":46,"cover_url":8,"hotness":12,"is_selected":13,"score":92,"score_detail":93,"sources":96,"tags":98,"search_phrases":102,"slug":105,"view_count":34,"doi":106,"paper":107,"created_at":119},2300,"Smart Agriculture Optimization in Off-Grid Terrains via Satellite- Coupled Soil Sensing Framework","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10720368\u002Fv1","Smart Agriculture Optimization in Off-Grid Terrains via Satellite- Coupled Soil Sensing Framework。Research Square","Research Square","2026-09-11T00:00:00Z",57,{"impact":18,"substance":17,"depth":94,"authority":49,"freshness":49,"relevant":20,"comment":95},14,"预印本论文提出卫星耦合土壤感知框架，面向离网地形智慧农业优化，方法有创新但尚缺同行评议，属细分领域进展。",[97],{"name":90,"url":88},[25,99,100,26,101],"精准农业","遥感","卫星遥感",[103,104],"卫星遥感 土壤监测 智慧农业 精准农业","卫星遥感 土壤监测","卫星遥感土壤监测智慧农业精准农业-2300","10.21203\u002Frs.3.rs-10720368\u002Fv1",{"doi":106,"openalex_id":108,"authors":109,"venue":90,"cited_by_count":34,"oa_url":88,"card":8,"direction":81,"ingested_from":83},"W7212267038",[110,112,114,117],{"name":111,"orcid":8},"Masood Ahmad",{"name":113,"orcid":8},"Shahid Kamal",{"name":115,"orcid":116},"Fasee Ullah","https:\u002F\u002Forcid.org\u002F0000-0001-6167-5253",{"name":118,"orcid":8},"Ishtiaq Wahid","2026-09-13T23:30:09.626390Z",{"id":121,"title":122,"url":123,"summary":124,"summary_zh":125,"content":8,"source_name":126,"source_url":123,"published_at":127,"category":46,"cover_url":8,"hotness":12,"is_selected":13,"score":128,"score_detail":129,"sources":133,"tags":135,"search_phrases":138,"slug":141,"view_count":34,"doi":142,"paper":143,"created_at":159},2140,"AIOT in Predictive Agriculture - IoT and AI Integration for Real-Time Soil Monitoring and Smart Irrigation in Predictive Agriculture","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.84727","Modern agriculture faces severe challenges due to climate volatility, accelerating groundwater depletion, and the global imperative to maximize crop production on diminishing arable land. Traditional irrigation frameworks rely predominantly on static schedules or reactive threshold switching, leading to substantial water waste, energy inefficiencies, and suboptimal crop yields. To overcome these limitations, this paper proposes an end-to-end Artificial Intelligence of Things (AIoT) framework designed for real-time multi-parameter soil tracking and predictive smart irrigation. The system architecture deploys low-power IoT field nodes driven by ESP32 microcontrollers, integrated with capacitive soil moisture sensors, environmental sensors, and soil pH probes that stream telemetry data over lightweight MQTT protocols. To transition from reactive monitoring to proactive resource allocation, a cloud-based predictive engine utilizes Long Short-Term Memory (LSTM) neural networks to forecast 24- to-48-hour soil moisture depletion dynamics based on historical moisture profiles and localized meteorological factors. Experimental validation across a 90-day testbed demonstrates that the proposed predictive framework achieves a to reduction in total water consumption while maintaining optimal volumetric soil water content. Furthermore, deep-sleep dynamic power profiling confirms node energy autonomy of up to 219 days on a single battery charge, presenting a scalable, sustainable, and economically viable solution for precision agriculture.","现代农业正面临气候波动、地下水加速枯竭以及全球在日益减少的耕地上最大化作物产量的迫切需求等严峻挑战。传统灌溉框架主要依赖静态调度或反应式阈值切换，导致大量水资源浪费、能源效率低下以及作物产量欠优。为克服这些局限，本文提出了一种端到端的人工智能物联网（AIoT）框架，专为实时多参数土壤监测与预测性智能灌溉而设计。该系统架构部署了由ESP32微控制器驱动的低功耗物联网田间节点，集成了电容式土壤水分传感器、环境传感器和土壤pH探头，通过轻量级MQTT协议传输遥测数据。为实现从反应式监测向主动式资源分配的转变，基于云的预测引擎利用长短期记忆（LSTM）神经网络，根据历史水分剖面和局部气象因素，预测24至48小时的土壤水分消耗动态。在为期90天的测试平台上进行的实验验证表明，所提出的预测框架在保持最优土壤体积含水量的同时，实现了总用水量的降低。此外，深度睡眠动态功耗分析证实，节点在单次电池充电下可实现长达219天的能量自主运行，为精准农业提供了一种可扩展、可持续且经济可行的解决方案。","International Journal for Research in Applied Science and Engineering Technology","2026-09-10T00:00:00Z",75,{"impact":16,"substance":130,"depth":131,"authority":18,"freshness":49,"relevant":20,"comment":132},20,17,"AIoT+LSTM 预测灌溉的完整实证研究，90 天试验与节水、能耗数据扎实，对精准农业落地有参考价值，但期刊层级与影响范围偏细分领域。",[134],{"name":126,"url":123},[25,136,55,137,99,26],"农业人工智能","智能灌溉",[139,140],"农业人工智能 土壤监测 智慧农业 智能灌溉","农业人工智能 土壤监测","农业人工智能土壤监测智慧农业智能灌溉-2140","10.22214\u002Fijraset.2026.84727",{"doi":142,"openalex_id":144,"authors":145,"venue":126,"cited_by_count":34,"oa_url":123,"card":154,"direction":81,"ingested_from":83},"W7212115545",[146,148,150,152],{"name":147,"orcid":8},"Gowri M.",{"name":149,"orcid":8},"Boomika M.",{"name":151,"orcid":8},"S. Rakshana",{"name":153,"orcid":8},"Rubali R.",{"tldr":155,"method":156,"finding":157,"direction":81,"opportunity":158},"提出AIoT框架，用LSTM预测土壤湿度实现智能灌溉，节水并延长节点续航。","ESP32节点+电容湿度\u002FpH传感器，MQTT上云，LSTM预测24-48小时湿","90天试验节水显著，土壤含水量保持最优，单次电池续航达219天。","可探索多作物多气候下LSTM泛化能力，及边缘端轻量预测模型降低云依赖。","2026-09-11T23:30:10.173013Z",{"id":161,"title":162,"url":163,"summary":164,"summary_zh":165,"content":8,"source_name":166,"source_url":163,"published_at":167,"category":46,"cover_url":8,"hotness":12,"is_selected":13,"score":168,"score_detail":169,"sources":173,"tags":175,"search_phrases":177,"slug":179,"view_count":34,"doi":180,"paper":181,"created_at":202},1597,"Lite-HCAR-CNN: A Lightweight Deep Learning Model for Rapid Forest Soil Organic Carbon Estimation from Laboratory Vis-NIR Spectra","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102530","Rapid and reliable monitoring of soil organic carbon (SOC) is important for precision forest management and soil ecological assessment. In this study, 853 surface soil samples were collected from subtropical plantation and secondary forest regions in Guangxi, China. After spectral quality control and SOC outlier screening, 842 valid samples were retained for model development. Laboratory-acquired visible and near-infrared spectra were preprocessed using Savitzky-Golay smoothing and standard normal variate correction, followed by a dimensionality-reduction comparison involving the full spectrum, 10 nm, 20 nm and 40 nm binning, principal component analysis, and PLS-VIP feature selection. To improve computational efficiency for rapid SOC estimation from Vis-NIR spectra, a lightweight model named Lite-HCAR-CNN was developed. The model adopts two one-dimensional convolutional blocks with a kernel size of 7, dual squeeze-and-excitation channel recalibration, Swish activation, and a compact residual dense regression head. Model evaluation employed an SOC-stratified outer five-fold cross-validation strategy, with an inner validation subset used only for checkpoint selection and each held-out outer test fold evaluated once. Under the selected 20 nm binning strategy, Lite-HCAR-CNN achieved an average R² of 0.810 ± 0.027 and RMSE of 4.617 ± 0.373 g\u002Fkg. Compared with 1D-CNN, Lite-HCAR-CNN reduced RMSE by approximately 8.4%, and compared with 1D-ResNet18 it reduced trainable parameters by 87.9%, model storage size by 87.9%, FLOPs by 96.7%, and CPU inference latency by 88.6%. Binning comparison, kernel-size and hidden-unit sensitivity analyses, ablation experiments, residual diagnosis, and MC Dropout uncertainty analysis were further conducted to clarify the model design and reliability. The results indicate that Lite-HCAR-CNN achieves a favorable balance between prediction accuracy and computational efficiency for fast estimation of forest soil organic carbon using laboratory Vis-NIR spectroscopy.","土壤有机碳（SOC）的快速可靠监测对于精准森林管理和土壤生态评估具有重要意义。本研究从中国广西的亚热带人工林和次生林区域采集了853份表层土壤样品，经光谱质量控制和SOC异常值筛选后，保留842份有效样品用于模型构建。实验室获取的可见-近红外光谱采用Savitzky-Golay平滑和标准正态变量校正进行预处理，随后对全光谱、10 nm、20 nm和40 nm分箱、主成分分析及PLS-VIP特征选择进行了降维比较。为提高基于可见-近红外光谱快速估算SOC的计算效率，开发了一种轻量级模型Lite-HCAR-CNN。该模型采用两个核大小为7的一维卷积块、双重挤压-激励通道重标定、Swish激活函数以及紧凑的残差密集回归头。模型评估采用基于SOC分层的五折外交叉验证策略，内验证子集仅用于检查点选择，每个保留的外测试折仅评估一次。在选定的20 nm分箱策略下，Lite-HCAR-CNN实现了平均R²为0.810 ± 0.027，RMSE为4.617 ± 0.373 g\u002Fkg。与1D-CNN相比，Lite-HCAR-CNN的RMSE降低了约8.4%；与1D-ResNet18相比，可训练参数减少了87.9%，模型存储大小减少了87.9%，FLOPs减少了96.7%，CPU推理延迟降低了88.6%。进一步开展了分箱比较、核大小和隐藏单元敏感性分析、消融实验、残差诊断及MC Dropout不确定性分析，以阐明模型设计及其可靠性。结果表明，Lite-HCAR-CNN在利用实验室可见-近红外光谱快速估算森林土壤有机碳方面，实现了预测精度与计算效率之间的良好平衡。","Smart Agricultural Technology","2026-09-01T00:00:00Z",68,{"impact":18,"substance":170,"depth":16,"authority":18,"freshness":171,"relevant":20,"comment":172},22,4,"提出轻量化深度学习模型，兼顾精度与效率，对森林土壤碳估算有实用价值。",[174],{"name":166,"url":163},[25,136,26,176],"模型优化",[178,140],"农业人工智能 土壤监测 智慧农业 模型优化","农业人工智能土壤监测智慧农业模型优化-1597","10.1016\u002Fj.atech.2026.102530",{"doi":180,"openalex_id":182,"authors":183,"venue":166,"cited_by_count":34,"oa_url":195,"card":196,"direction":200,"ingested_from":83},"W7207551174",[184,186,189,191,193],{"name":185,"orcid":8},"Jian Tang",{"name":187,"orcid":188},"Zubo Meng","https:\u002F\u002Forcid.org\u002F0009-0008-9632-5067",{"name":190,"orcid":8},"Yuanyuan Shi",{"name":192,"orcid":8},"Yun Deng",{"name":194,"orcid":8},"Junyu Zhao","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2772375526007550\u002Fpdf",{"tldr":197,"method":198,"finding":199,"direction":200,"opportunity":201},"提出轻量级深度学习模型Lite-HCAR-CNN，用于从实验室可见-近红外光谱快速估算森林土壤有机碳","采用一维卷积、挤压激励、Swish激活及残差密集回归头，结合20nm分箱和SG-","模型在精度与效率间取得平衡，RMSE降低8.4%，参数减少87.9%，推理延迟降低88.6%。","农业遥感与作物表型","可探索将轻量级模型部署于便携式或机载光谱设备，实现野外实时土壤碳监测，并扩展至其他土壤属性。","2026-09-04T23:30:04.459941Z",{"id":204,"title":205,"url":206,"summary":207,"summary_zh":208,"content":8,"source_name":209,"source_url":206,"published_at":210,"category":46,"cover_url":8,"hotness":12,"is_selected":13,"score":211,"score_detail":212,"sources":215,"tags":217,"search_phrases":219,"slug":221,"view_count":34,"doi":222,"paper":223,"created_at":236},1328,"Explainable Machine Learning for Simultaneous Prediction of Soil Texture and Gravimetric Water Content: A SHAP-Driven Multi-Model Framework","https:\u002F\u002Fdoi.org\u002F10.70917\u002Fijcisim-2026-5335","Soil texture and gravimetric water content (GWC) are important properties which need to be determined accurately for proper irrigation and sustainable land management. Although machine learning (ML) has enhanced the ability to predict pedometrics, many of the high performing algorithms have been described as a \"black-box\" and practitioners have been reluctant to trust the model and connect the model output with soil physics. Furthermore, the existing approaches mostly assume independence between the attributes to be predicted and do not take into account their physical relationships. The present study aims to fill these voids by proposing a robust multi-model approach for simultaneous prediction of soil texture and GWC. A variety of data was used to assess state-of-the-art algorithms, such as XGBoost, CatBoost, and Random Forest. However, to achieve local and global interpretability, we integrated Shapley Additive Explanations (SHAP). This XAI-based approach filled the gap between computational intelligence and soil physics, allowing a fine-grained diagnosis of the contribution of the different features. The results show that the Random Forest framework outperformed in terms of concurrent performance with R2 value of 0.908, accuracy of 90.8% and Cohen's Kappa of 0.82 for soil texture and R2 value of 0.798 for GWC. Two features, Cation Exchange Capacity (CEC) and Organic Matter Percentage (OMP), were identified as the most influential using SHAP analysis, and they were found to exert the greatest influence on the moisture retention thresholds, with CEC being the most discriminative feature between the Clayey and Sandy texture classes, which aligns with the model's validity in light of the known pedology. This transparency and simultaneous monitoring of key soil properties makes it an appealing solution for precision agriculture and for resource management in the context of climate change.","土壤质地与重量含水量（GWC）是精准灌溉和可持续土地管理中需精确测定的重要属性。尽管机器学习（ML）增强了预测土壤计量学（pedometrics）的能力，但许多高性能算法被描述为“黑箱”，实践者往往不愿信任模型，也难以将模型输出与土壤物理学联系起来。此外，现有方法大多假设待预测属性之间相互独立，未考虑其物理关系。本研究旨在通过提出一种稳健的多模型方法，同时预测土壤质地和GWC，以填补这些空白。研究使用了多种数据评估先进算法，如XGBoost、CatBoost和随机森林（Random Forest）。然而，为实现局部和全局可解释性，我们整合了Shapley加法解释（SHAP）。这种基于可解释人工智能（XAI）的方法弥合了计算智能与土壤物理学之间的鸿沟，能够对不同特征的贡献进行细粒度诊断。结果表明，随机森林框架在并发性能上表现优异，土壤质地的R²值为0.908，准确率为90.8%，Cohen's Kappa系数为0.82，GWC的R²值为0.798。通过SHAP分析，阳离子交换量（CEC）和有机质百分比（OMP）被识别为最具影响力的两个特征，且它们对水分保持阈值的影响最大，其中CEC是区分黏土和砂土质地类别的最具判别力的特征，这与已知土壤学知识下模型的效度相吻合。这种对关键土壤属性的透明性和同步监测，使其成为精准农业及气候变化背景下资源管理的理想解决方案。","International Journal of Computer Information Systems and Industrial Management Applications","2026-08-30T00:00:00Z",73,{"impact":16,"substance":170,"depth":16,"authority":18,"freshness":213,"relevant":20,"comment":214},3,"研究提出可解释的机器学习框架，同时预测土壤质地与含水量，提升模型透明度，对精准农业有实质贡献。",[216],{"name":209,"url":206},[25,136,218,26],"机器学习",[220,140],"农业人工智能 土壤监测 智慧农业 机器学习","农业人工智能土壤监测智慧农业机器学习-1328","10.70917\u002Fijcisim-2026-5335",{"doi":222,"openalex_id":224,"authors":225,"venue":209,"cited_by_count":34,"oa_url":206,"card":230,"direction":235,"ingested_from":83},"W7204858035",[226,228],{"name":227,"orcid":8},"Chetana Shivanagi",{"name":229,"orcid":8},"Shivayogi  Ullagaddi",{"tldr":231,"method":232,"finding":233,"direction":81,"opportunity":234},"提出可解释机器学习框架，同时预测土壤质地和含水量，提升模型透明度和可信度。","使用XGBoost、CatBoost、随机森林，结合SHAP进行局部和全局解释。","随机森林表现最佳，CEC和有机质是关键特征，与土壤物理规律一致。","可探索将SHAP解释与因果推断结合，或扩展到其他土壤属性及动态预测，增强模型泛化性。","农业人工智能与决策模型","2026-09-01T23:30:40.247344Z",{"id":238,"title":239,"url":240,"summary":241,"summary_zh":242,"content":8,"source_name":243,"source_url":240,"published_at":244,"category":46,"cover_url":8,"hotness":12,"is_selected":13,"score":245,"score_detail":246,"sources":250,"tags":252,"search_phrases":254,"slug":257,"view_count":34,"doi":258,"paper":259,"created_at":271},1305,"IoT-Based Soil Condition Monitoring System for Supporting Precision and Sustainable Agriculture in West Java","https:\u002F\u002Fdoi.org\u002F10.55981\u002Fjet.807","West Java Province is one of the largest horticultural producers in Indonesia. The horticulture subsector in West Java covers tens of thousands of hectares of farmland and contributes approximately 1.8% to the province’s total Gross Regional Domestic Product (GRDP). However, this sector faces serious challenges. Climate change has increased the frequency of droughts and floods, disrupting horticultural production. In addition, soil fertility has declined due to unsustainable farming practices; many agricultural lands in West Java contain less than 2% organic matter as a result of long-term chemical fertilizer use. These challenges have led to decreased productivity and threaten the sustainability of horticultural farming. Precision agriculture emerges as a solution to these issues. This concept leverages IoT technologies such as the ESP32 microcontroller with SPIFFS, soil sensors, and a web-based dashboard to monitor land conditions in real time and manage inputs more efficiently. The IoT-based soil monitoring system developed in this study was tested in a farmer’s yard in Pameungpeuk Subdistrict, Garut. The results demonstrated practical benefits, including improved irrigation efficiency (up to ~30% water savings) and reduced risk of crop losses through early detection of drought conditions. Moreover, the application of IoT in potato cultivation in West Java has been shown to double seed tuber yields from 15,000 to 30,000 tubers. These findings are consistent with regional policy directions, as reflected in the West Java Regional Medium-Term Development Plan (RPJMD) and the “Petani Milenial” (Millennial Farmers) program, which promote sustainable agricultural modernization through IoT-based technologies to enhance the productivity of young farmers.","西爪哇省是印度尼西亚最大的园艺生产地之一。该省的园艺分部门覆盖数万公顷农田，约占全省地区生产总值（GRDP）总额的1.8%。然而，该部门面临严峻挑战。气候变化增加了干旱和洪水的发生频率，扰乱了园艺生产。此外，由于不可持续的耕作方式，土壤肥力下降；长期使用化肥导致西爪哇许多农业用地的有机质含量低于2%。这些挑战导致生产力下降，并威胁到园艺农业的可持续性。精准农业应运而生，成为解决这些问题的方案。这一概念利用物联网（IoT）技术，如搭载SPIFFS的ESP32微控制器、土壤传感器和基于网页的仪表盘，实时监测土地状况并更高效地管理投入。本研究中开发的基于物联网的土壤监测系统在加鲁特县帕梅翁佩乌克分区的一位农民庭院中进行了测试。结果表明其具有实际效益，包括提高灌溉效率（节水约30%）以及通过早期检测干旱状况降低作物损失风险。此外，物联网在西爪哇马铃薯种植中的应用已被证明可使种薯产量翻倍，从每公顷15,000个增加到30,000个。这些发现与区域政策方向一致，正如西爪哇省中期发展规划（RPJMD）和“千禧农民”计划所反映的那样，这些政策通过基于物联网的技术促进可持续农业现代化，以提高年轻农民的生产力。","Jurnal Elektronika dan Telekomunikasi","2026-08-31T00:00:00Z",61,{"impact":17,"substance":16,"depth":247,"authority":12,"freshness":248,"relevant":20,"comment":249},16,2,"论文展示IoT土壤监测系统在西爪哇的应用，节水30%并提升马铃薯产量，对区域精准农业有参考价值，但时效性较低。",[251],{"name":243,"url":240},[25,55,99,253,26],"可持续农业",[255,256],"可持续农业 土壤监测 智慧农业 精准农业","可持续农业 土壤监测","可持续农业土壤监测智慧农业精准农业-1305","10.55981\u002Fjet.807",{"doi":258,"openalex_id":260,"authors":261,"venue":243,"cited_by_count":34,"oa_url":240,"card":266,"direction":81,"ingested_from":83},"W7204810936",[262,264],{"name":263,"orcid":8},"Ayu Latifah",{"name":265,"orcid":8},"Dendi Ardimansah",{"tldr":267,"method":268,"finding":269,"direction":81,"opportunity":270},"开发基于IoT的土壤监测系统，支持西爪哇精准可持续农业，提高灌溉效率并增产。","使用ESP32微控制器、SPIFFS、土壤传感器和Web仪表盘构建实时监测系统。","系统节省约30%灌溉用水，马铃薯种薯产量翻倍至3万块，符合区域政策。","可扩展至更多作物和地区，结合机器学习预测土壤养分，优化施肥策略，提升系统普适性。","2026-09-01T23:30:12.797772Z"]