[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3131":3,"related-3131":58},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":57},3131,"Water stress detection from plant electrophysiology: A machine learning framework for irrigation management","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112434","Fast detection of plant stress is key to plant phenotyping, precision agriculture, and automated crop management. In particular, efficient irrigation management requires early identification of water stress to optimize resource use while maintaining crop performance. Direct physiological sensing offers the potential to detect stress responses before visible symptoms appear. In this study, we recorded electrophysiological signals from greenhouse-grown tomato plants subjected to water stress and developed a framework based on machine learning for online stress detection. The resulting time-series data were segmented into tumbling windows of 1 min, 5 min, 30 min, 1 h and 6 h, then passed through a processing pipeline that includes statistical feature extraction and selection, automated machine learning or alternatively deep learning, and probability calibration. Across multiple input time horizons, we found that a 30-minute look-back horizon strikes the best balance between rapid decision-making and classification performance. Using automated machine learning, the framework achieved classification accuracies of up to 92%, outperforming deep learning approaches. Sequential backward selection reduced the feature set while maintaining performance. Out-of-sample testing on withheld plant individuals provides methodological validation, confirming that the framework detects healthy-to-stress transitions despite inter-individual electrophysiological variability.Overall, we develop and provide a decision-support tool for agricultural practitioners and researchers and establish a foundation for biofeedback-driven irrigation control to improve resource efficiency in (semi-)autonomous crop production systems.","植物胁迫的快速检测是植物表型分析、精准农业和自动化作物管理的关键。特别是，高效的灌溉管理需要早期识别水分胁迫，以优化资源利用并维持作物表现。直接生理传感有望在可见症状出现之前检测到胁迫响应。在本研究中，我们记录了温室种植的番茄植株在水分胁迫下的电生理信号，并开发了一个基于机器学习的在线胁迫检测框架。所得时间序列数据被分割为1分钟、5分钟、30分钟、1小时和6小时的滑动窗口，然后通过一个处理流程，包括统计特征提取与选择、自动化机器学习或深度学习，以及概率校准。在多个输入时间跨度中，我们发现30分钟的回溯窗口在快速决策与分类性能之间取得了最佳平衡。使用自动化机器学习，该框架达到了高达92%的分类准确率，优于深度学习方法。序列后向选择在保持性能的同时减少了特征集。对保留的植株个体进行样本外测试提供了方法学验证，确认该框架能够在个体间电生理变异性存在的情况下检测从健康到胁迫的转变。总体而言，我们为农业从业者和研究人员开发并提供了一种决策支持工具，并为生物反馈驱动的灌溉控制奠定了基础，以提高（半）自主作物生产系统中的资源效率。",null,"Computers and Electronics in Agriculture","2026-09-21T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,9,1,"该研究提出基于植物电生理信号与机器学习的在线水分胁迫检测框架，30分钟窗口下准确率达92%，为精准灌溉与生物反馈控制提供决策支持，方法新颖、结论可靠，值得进入每日精选。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","精准灌溉","水分胁迫","植物电生理",[32,33],"植物电生理 水分胁迫 机器学习","番茄 水分胁迫 灌溉管理","植物电生理水分胁迫机器学习-3131",0,"10.1016\u002Fj.compag.2026.112434",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":6,"card":49,"direction":55,"ingested_from":56},"W7204541435",[40,43,46],{"name":41,"orcid":42},"Eduard Buss","https:\u002F\u002Forcid.org\u002F0000-0001-6993-5873",{"name":44,"orcid":45},"Till Aust","https:\u002F\u002Forcid.org\u002F0000-0003-2863-1341",{"name":47,"orcid":48},"Heiko Hamann","https:\u002F\u002Forcid.org\u002F0000-0002-2458-8289",{"tldr":50,"method":51,"finding":52,"direction":53,"opportunity":54},"利用番茄电生理信号与机器学习实现水分胁迫早期在线检测。","温室番茄电生理时序，滑窗分段、特征选择、自动机器学习与概率校准。","30分钟回溯窗口最优，自动机器学习分类准确率达92%，优于深度学习。","其他","可探索多作物电生理泛化、田间部署及闭环灌溉控制，提升资源效率。","农业遥感与作物表型","openalex","2026-09-22T23:30:02.024960Z",{"total":59,"page":21,"page_size":59,"items":60},6,[61,100,146,174,217,252],{"id":62,"title":63,"url":64,"summary":65,"summary_zh":66,"content":9,"source_name":67,"source_url":64,"published_at":68,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":69,"score_detail":70,"sources":74,"tags":76,"search_phrases":79,"slug":82,"view_count":35,"doi":83,"paper":84,"created_at":99},3010,"A Multi-Task Stacked Ensemble and IoT-Enabled Decision Support System for Precision Fertigation in Smallholder Agriculture","https:\u002F\u002Fdoi.org\u002F10.5120\u002Fijca9a6e56b81235","Nigerian agriculture's fixed-schedule fertigation causes low efficiency and nutrient leaching.A stacked-ensemble model is developed for precision fertigation that jointly predicts fertigation need, rate (kg\u002Fha) and timing (Early\u002FOptimal\u002FLate).To train and evaluate the ensemble, a unified dataset was integrated, comprising 12,840 records and 42 variables from a Nigerian soil-weather-yield dataset, a locally sourced Nigerian IoT sensor series and historical weather\u002FNDVI feeds.An LSTM soil-dynamics model, an XGBoost rate regressor and Random Forest need\u002Ftiming classifiers are fused through an XGBoost meta-learner trained on out-of-fold predictions.On held-out partitions, the ensemble reduced rate MAE from 0.55 to 0.49 kg\u002Fha (-10.9%) and RMSE from 0.68 to 0.61 kg\u002Fha (-10.3%;R² 0.88→0.92),raised need F1 from 0.83 to 0.86 (accuracy 0.87→0.89;AUC 0.89→0.93)and timing macro-F1 from 0.84 to 0.86, with well-calibrated probabilities (Brier 0.082).The trained ensemble was deployed through a RESTful API and responsive dashboard; under concurrent load, the system recorded 0% request errors with 1.88s median API latency, demonstrating practical deployability for Nigerian smallholder agriculture.","尼日利亚农业的固定日程水肥一体化导致效率低下和养分淋失。本研究开发了一种堆叠集成模型用于精准水肥管理，可联合预测灌溉施肥需求、施用量（kg\u002Fha）和时机（早\u002F最佳\u002F晚）。为训练和评估该集成模型，整合了一个统一数据集，包含来自尼日利亚土壤-天气-产量数据集、本地尼日利亚物联网传感器序列及历史天气\u002FNDVI数据的12,840条记录和42个变量。通过基于折外预测训练的XGBoost元学习器，将LSTM土壤动力学模型、XGBoost施用量回归器和随机森林需求\u002F时机分类器进行融合。在留出集上，该集成模型将施用量MAE从0.55降至0.49 kg\u002Fha（-10.9%），RMSE从0.68降至0.61 kg\u002Fha（-10.3%；R² 0.88→0.92），需求F1从0.83提升至0.86（准确率0.87→0.89；AUC 0.89→0.93），时机宏平均F1从0.84提升至0.86，且概率校准良好（Brier 0.082）。训练后的集成模型通过RESTful API和响应式仪表板部署；在并发负载下，系统录得0%请求错误，API延迟中位数为1.88秒，展示了在尼日利亚小农农业中的实际可部署性。","International Journal of Computer Applications","2026-09-18T00:00:00Z",79,{"impact":17,"substance":18,"depth":17,"authority":71,"freshness":72,"relevant":21,"comment":73},13,8,"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。",[75],{"name":67,"url":64},[26,27,77,78,28],"农业物联网","小农户",[80,81],"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":83,"openalex_id":85,"authors":86,"venue":67,"cited_by_count":35,"oa_url":64,"card":93,"direction":97,"ingested_from":56},"W7213663876",[87,89,91],{"name":88,"orcid":9},"Awojide S.",{"name":90,"orcid":9},"Ikpotokin F.O.",{"name":92,"orcid":9},"Sadiq F.I.",{"tldr":94,"method":95,"finding":96,"direction":97,"opportunity":98},"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。","智慧农业 \u002F 农业物联网","可探索多任务集成模型在非洲小农户不同作物与气候区的迁移能力及低成本IoT部署。","2026-09-20T23:30:09.003454Z",{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":9,"source_name":106,"source_url":103,"published_at":107,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":109,"sources":111,"tags":113,"search_phrases":116,"slug":119,"view_count":35,"doi":120,"paper":121,"created_at":145},2666,"Automated Machine Learning-Driven UAV Remote Sensing for Accurate Winter Wheat Water Content Prediction","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18183161","Crop water content is a critical indicator of crop growth status, and its efficient and accurate monitoring is essential for agricultural water resource management. Conventional methods for monitoring winter wheat water content, however, rely mainly on destructive sampling and are labor-intensive and time-consuming. To address these limitations, this study explored the potential of unmanned aerial vehicle (UAV) remote sensing for the rapid and accurate assessment of winter wheat water content. High-resolution canopy remote sensing images were acquired using UAVs equipped with multispectral (MS), RGB, and thermal infrared (TIR) cameras during the flowering and filling stages under six irrigation treatments. Ground-truth sampling data were integrated with the UAV-derived remote sensing data, and an automated machine learning (AutoML) framework—which automatically searches over a range of candidate algorithms and hyperparameters to select the optimal model—was employed to establish regression models for predicting winter wheat moisture content (MC). All models were evaluated using five-fold cross-validation. The results demonstrated that MC prediction performed best during the filling stage, with the TIR sensor achieving the highest accuracy (R2 = 0.812, MAE = 0.0204, RMSE = 0.0274). Compared with single-sensor approaches, multi-sensor fusion further improved predictive performance, achieving an R2 of 0.876, an MAE of 0.0191, and an RMSE of 0.0259 for MC prediction. These findings indicate that UAV-based multi-sensor remote sensing provides an effective means of monitoring winter wheat water content, facilitating timely assessment of crop growth status and optimized irrigation management. Moreover, the use of AutoML enables high-accuracy prediction with minimal human intervention, enhancing the precision of crop water monitoring and advancing precision agriculture.","作物含水量是反映作物生长状况的关键指标，对其进行高效、准确的监测对农业水资源管理至关重要。然而，传统冬小麦含水量监测方法主要依赖破坏性采样，费时费力。为解决这些局限，本研究探索了无人机（UAV）遥感在快速准确评估冬小麦含水量方面的潜力。在六种灌溉处理下，利用搭载多光谱（MS）、RGB和热红外（TIR）相机的无人机在开花期和灌浆期获取了高分辨率冠层遥感图像。将地面实测采样数据与无人机遥感数据相结合，采用自动化机器学习（AutoML）框架——该框架可在一系列候选算法和超参数中自动搜索以选择最优模型——建立预测冬小麦含水量（MC）的回归模型。所有模型均采用五折交叉验证进行评估。结果表明，灌浆期MC预测表现最佳，其中TIR传感器精度最高（R2 = 0.812，MAE = 0.0204，RMSE = 0.0274）。与单传感器方法相比，多传感器融合进一步提升了预测性能，MC预测的R2达到0.876，MAE为0.0191，RMSE为0.0259。这些发现表明，基于无人机的多传感器遥感为监测冬小麦含水量提供了有效手段，有助于及时评估作物生长状况并优化灌溉管理。此外，AutoML的使用使得在最少人工干预下实现高精度预测成为可能，提升了作物水分监测的精度，推动了精准农业发展。","Remote Sensing","2026-09-15T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":72,"relevant":21,"comment":110},"AutoML结合无人机多传感器遥感预测冬小麦含水量，方法新颖、数据扎实，对精准灌溉有实用价值，值得进入每日精选。",[112],{"name":106,"url":103},[26,27,114,115,28],"小麦","遥感",[117,118],"农业人工智能 智慧农业 精准灌溉 小麦","农业人工智能 智慧农业","农业人工智能智慧农业精准灌溉小麦-2666","10.3390\u002Frs18183161",{"doi":120,"openalex_id":122,"authors":123,"venue":106,"cited_by_count":35,"oa_url":103,"card":140,"direction":55,"ingested_from":56},"W7213246708",[124,127,129,131,134,137],{"name":125,"orcid":126},"Fan Ding","https:\u002F\u002Forcid.org\u002F0000-0001-5482-8290",{"name":128,"orcid":9},"Qian Cheng",{"name":130,"orcid":9},"Fuyi Duan",{"name":132,"orcid":133},"Shuaipeng Fei","https:\u002F\u002Forcid.org\u002F0000-0002-8774-7929",{"name":135,"orcid":136},"Junjie Feng","https:\u002F\u002Forcid.org\u002F0000-0001-8900-2691",{"name":138,"orcid":139},"Zhen Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2847-0042",{"tldr":141,"method":142,"finding":143,"direction":55,"opportunity":144},"用无人机多光谱、RGB和热红外遥感结合AutoML预测冬小麦含水量。","无人机多传感器影像与地面采样，AutoML自动选模型，五折交叉验证。","灌浆期热红外精度最高R²=0.812，多传感器融合提升至R²=0.876。","可探索AutoML与多时相\u002F多源卫星遥感融合，实现区域尺度作物水分精准监测。","2026-09-16T23:30:29.163192Z",{"id":147,"title":148,"url":149,"summary":150,"summary_zh":9,"content":9,"source_name":151,"source_url":9,"published_at":152,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":153,"score_detail":154,"sources":157,"tags":159,"search_phrases":162,"slug":164,"view_count":35,"doi":9,"paper":165,"created_at":173},2609,"AI Framework Estimates Crop Water Stress from Satellite Data in Egypt's Nile Delta","https:\u002F\u002Ffirat.rw\u002Farticles\u002Fai-framework-estimates-crop-water-stress-from-satellite-data-in-egypts-nile-delta","Elbeltagi等发表在Smart Agricultural Technology。研究使用MODIS卫星产品(2018-2025)构建最佳子集回归(BSR)与机器学习相结合的特征优化人工智能框架，估计埃及Dakahliyah省农业植被水分胁迫。NDVI成为EVI的压倒性主导预测因子，相关系数0.934。Random Forest在2024-2025测试期达到0.9943的相关系数，平均绝对误差仅0.0063，根均方误差0.0161，相对绝对误差降至5%以下。","Smart Agricultural Technology","2026-09-13T01:00:00Z",77,{"impact":155,"substance":18,"depth":17,"authority":71,"freshness":72,"relevant":21,"comment":156},16,"基于MODIS长时序数据与机器学习融合的水分胁迫估算框架，方法新颖、精度高，对干旱区精准灌溉有参考价值，但属区域性案例研究，影响范围有限。",[158],{"name":151,"url":149},[26,27,160,29,161],"遥感监测","灌溉管理",[163,118],"农业人工智能 智慧农业 水分胁迫 灌溉管理","农业人工智能智慧农业水分胁迫灌溉管理-2609",{"doi":9,"openalex_id":9,"authors":166,"venue":9,"cited_by_count":35,"oa_url":9,"card":167,"direction":55,"ingested_from":172},[],{"tldr":168,"method":169,"finding":170,"direction":55,"opportunity":171},"用MODIS数据与机器学习框架估算埃及尼罗河三角洲农田水分胁迫。","MODIS时序数据，最佳子集回归结合随机森林做特征优化。","NDVI是主导预测因子，随机森林测试相关系数达0.9943，误差低于5%。","可迁移至其他干旱区验证特征优选框架的普适性，并融合多源遥感提升胁迫早期预警。","agent","2026-09-16T00:03:52.319524Z",{"id":175,"title":176,"url":177,"summary":178,"summary_zh":179,"content":9,"source_name":180,"source_url":177,"published_at":181,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":182,"score_detail":183,"sources":187,"tags":189,"search_phrases":192,"slug":195,"view_count":35,"doi":196,"paper":197,"created_at":216},2543,"Effects of Noise on the Accuracy of Estimating Rootzone Total Soil Moisture Using a Non‐Linear Autoregressive Exogenous Machine Learning Model for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fird.70226","ABSTRACT Widely used evapotranspiration (ET)–based irrigation methods contain many uncertainties that can affect irrigation quantity. To reduce these uncertainties, an alternative approach is the use of soil moisture (SM) data to estimate plant water uptake (PWU). If rootzone SM dynamics are understood, a surrogate machine learning (ML) model to predict the behaviour of SM dynamics can be developed to estimate PWU. Given the time and external variable dependency of SM, the non‐linear autoregressive exogenous (NARX) algorithm can be a better ML model for this purpose. However, the effect of measurement errors can affect prediction quality and hence the full deployment of data collection technology and computational algorithms. This paper presents a methodology consisting of analysing real‐world data, developing a generalized hypothetical SM curve, simulating measurement errors to develop noisy datasets and developing an ML model. The results show that the prediction accuracy is inversely proportional to the noise level. Up to a 10% measurement error, the NARX model can capture the SM dynamics with relatively high accuracy. However, the prediction quality decreases significantly as the noise level increases to 20%. For noise levels within 5%, the model prediction accuracy is significantly high, with correlation coefficients higher than 0.90 for all sets.","摘要 广泛使用的基于蒸散发（ET）的灌溉方法存在许多不确定性，可能影响灌溉量。为减少这些不确定性，一种替代方法是利用土壤水分（SM）数据估算植物吸水量（PWU）。如果理解了根区土壤水分动态，就可以开发一个替代性机器学习（ML）模型来预测土壤水分动态行为，从而估算植物吸水量。鉴于土壤水分对时间和外部变量的依赖性，非线性自回归外生（NARX）算法可能是更适合此目的的机器学习模型。然而，测量误差的影响可能影响预测质量，进而影响数据采集技术和计算算法的全面部署。本文提出了一套方法，包括分析真实世界数据、建立广义假设土壤水分曲线、模拟测量误差以生成含噪数据集，以及开发机器学习模型。结果表明，预测精度与噪声水平成反比。在测量误差不超过10%时，NARX模型能够以较高精度捕捉土壤水分动态。然而，当噪声水平增至20%时，预测质量显著下降。对于5%以内的噪声水平，模型预测精度显著较高，所有数据集的决定系数均高于0.90。","Irrigation and Drainage","2026-09-14T00:00:00Z",72,{"impact":71,"substance":184,"depth":185,"authority":71,"freshness":72,"relevant":21,"comment":186},21,17,"核心期刊论文，量化了测量噪声对NARX土壤水分预测精度的影响，为精准灌溉传感器布设与数据质量控制提供参考，但属细分方法研究，公共影响有限。",[188],{"name":180,"url":177},[26,27,190,28,191],"机器学习","土壤墒情",[193,194],"农业人工智能 土壤墒情 智慧农业 机器学习","农业人工智能 土壤墒情","农业人工智能土壤墒情智慧农业机器学习-2543","10.1002\u002Fird.70226",{"doi":196,"openalex_id":198,"authors":199,"venue":180,"cited_by_count":35,"oa_url":9,"card":210,"direction":214,"ingested_from":56},"W7213181547",[200,203,206,208],{"name":201,"orcid":202},"Fayzul Pasha","https:\u002F\u002Forcid.org\u002F0000-0002-8295-0602",{"name":204,"orcid":205},"Ashok Inturi","https:\u002F\u002Forcid.org\u002F0009-0004-1934-4310",{"name":207,"orcid":9},"Kinnoree R. Pasha",{"name":209,"orcid":9},"Dilruba Yeasmin",{"tldr":211,"method":212,"finding":213,"direction":214,"opportunity":215},"该论文评估了测量噪声对NARX机器学习模型估算根区土壤水分动态精度的影响。","使用NARX模型，基于真实数据、广义假设曲线和模拟噪声数据集。","预测精度与噪声水平成反比；噪声≤10%时精度较高，≤5%时相关系数>0.90，20%时显著下降。","农业人工智能与决策模型","可研究自适应去噪或鲁棒NARX模型，以在20%以上噪声下维持土壤水分预测精度。","2026-09-15T23:30:38.023164Z",{"id":218,"title":219,"url":220,"summary":221,"summary_zh":222,"content":9,"source_name":223,"source_url":220,"published_at":224,"category":12,"cover_url":9,"hotness":225,"is_selected":14,"score":226,"score_detail":227,"sources":230,"tags":234,"search_phrases":236,"slug":239,"view_count":35,"doi":240,"paper":241,"created_at":251},2312,"MridAI: Autonomous Edge-to-Conversational IoT for Democratising Precision Agriculture via Neuro-Symbolic Telemetry","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22724805","While sensor-guided precision agriculture improves water efficiency and curtails chemical run-off, adoption across smallholder farm land in the Global South remains under 1%. Commercial telemetry systems are constrained by high capital acquisition costs (>$300) and complex, dashboard-centric mobile applications that impose heavy cognitive burdens on low-literacy farmers. This paper proposes MridAI, a low-cost (\u003C$45 \u002F ₹3,420 COGS) autonomous in-ground agro-telemetry node coupled with a cloud-based neuro-symbolic artificial intelligence advisory pipeline. The physical layer integrates a multi-parameter Modbus RS485 sensor, an ESP32-C3 micro-controller, an Indian-band 4G LTE Cat-1 modem, and an energy harvesting subsystem within an IP67-rated solar stem. To eliminate measurement errors in high-clay tropical soils (Vertisols), an on-device digital signal-processing pipeline applies an adapted Topp dielectric correction alongside a one-dimensional discrete Kalman filter. To overcome rural telecommunication instability, the firmware implements an asynchronous, non-volatile store-and-forward ring buffer. At the cloud layer, an analytical solver calculates deterministic FAO-56 evapotranspiration deficits and enforces Indian Council of Agricultural Research (ICAR) chemical boundaries, strictly isolating numerical computation from an instruction-tuned Large Language Model (LLM). The generative model functions solely as a linguistic translator, delivering actionable, dialect-adapted recommendations directly via the Meta WhatsApp Cloud API without requiring third-party application downloads. Empirical power-budget modelling confirms indefinite operational autonomy (>240 days without solar irradiance), establishing a scalable paradigm for digital agriculture.","尽管传感器引导的精准农业提高了用水效率并减少了化学品径流，但在全球南方小农农田中的采用率仍不足1%。商业遥测系统受制于高昂的资本购置成本（超过300美元）以及复杂的、以仪表盘为中心的移动应用程序，后者给低识字率农民带来了沉重的认知负担。本文提出MridAI，一种低成本（物料清单成本低于45美元\u002F3，420印度卢比）的自主地下农业遥测节点，并耦合基于云的神经符号人工智能咨询流水线。物理层将多参数Modbus RS485传感器、ESP32-C3微控制器、印度频段4G LTE Cat-1调制解调器以及能量收集子系统集成于IP67防护等级的太阳能杆体内。为消除高黏土热带土壤（变性土）中的测量误差，设备端数字信号处理流水线采用适配的Topp介电校正与一维离散卡尔曼滤波器。为克服农村电信不稳定性，固件实现了异步、非易失性的存储转发环形缓冲区。在云层，分析求解器计算确定性的FAO-56蒸散亏缺，并执行印度农业研究理事会（ICAR）的化学品边界，将数值计算与指令微调的大语言模型（LLM）严格隔离。生成模型仅充当语言翻译器，通过Meta WhatsApp Cloud API直接提供可操作的、适配方言的建议，无需下载第三方应用程序。实证功率预算建模证实了无限期运行自主性（无太阳辐照下超过240天），为数字农业建立了一种可扩展的范式。","Zenodo (CERN European Organization for Nuclear Research)","2026-09-12T00:00:00Z",25,85,{"impact":18,"substance":228,"depth":17,"authority":71,"freshness":20,"relevant":21,"comment":229},23,"面向全球南方小农户的低成本自主土壤遥测节点与神经符号AI咨询管线，方法新颖、成本与能耗数据具体，对农业信息化与普惠数字农业有实质参考价值。",[231,232],{"name":223,"url":220},{"name":223,"url":233},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22724804",[235,26,27,77,78,28],"数字乡村",[237,238],"农业人工智能 农业物联网 数字乡村 智慧农业","农业人工智能 农业物联网","农业人工智能农业物联网数字乡村智慧农业-2312","10.5281\u002Fzenodo.22724805",{"doi":240,"openalex_id":242,"authors":243,"venue":223,"cited_by_count":35,"oa_url":220,"card":246,"direction":97,"ingested_from":56},"W7212357737",[244],{"name":245,"orcid":9},"Pranit Kamble",{"tldr":247,"method":248,"finding":249,"direction":97,"opportunity":250},"提出低成本自主地下物联网节点与神经符号AI咨询管道，让小农户用WhatsApp获取精准农业建议。","ESP32-C3与Modbus传感器、Topp校正和卡尔曼滤波、FAO-56与L","系统成本低于45美元，可独立运行超240天，无需下载应用即可通过WhatsApp获得方言化建议。","可探索低资源语言与方言适配的LLM农业咨询，以及神经符号系统在更多作物和土壤类型中的泛化验证。","2026-09-13T23:30:14.323767Z",{"id":253,"title":254,"url":255,"summary":256,"summary_zh":257,"content":9,"source_name":258,"source_url":255,"published_at":259,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":260,"score_detail":261,"sources":265,"tags":267,"search_phrases":270,"slug":272,"view_count":35,"doi":273,"paper":274,"created_at":286},1986,"AI-Enabled Precision Irrigation for Water-Efficient and Sustainable Agriculture","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.84732","The diminishing availability of water for agriculture, sub optimal irrigation methods, climate uncertainty and rising food demands have led to a demand for intelligent and sustainable solutions to water management problems. This research introduces an AI Driven Arduino and Raspberry Pi irrigation system that maximizes agricultural water use by real time sensing, intelligent decision making, and automated irrigation control. The proposed system comprises soil moisture, soil temperature, soil relative humidity, rainfall, and water level sensors to continuously monitor the field conditions. Arduino is used for interfacing with sensors and actuators, Raspberry Pi for data processing, AI based analysis, monitoring and data logging. The sensor data collected is preprocessed and analyzed with an AI\u002FML model to calculate the irrigation needs depending on soil and environmental conditions. The water pump or irrigation valve is automatically turned on or off with a relay mechanism, based on the decision generated. The experimental results show the effectiveness of the proposed method in reducing unnecessary irrigation and the utilization of the resources. With a representative prototype set of results, the AI model was able to identify 94.2% of the events correctly, whereas the automated system was able to detect around 98.6% of events, with a mean response time of 2.4 seconds. About 35% less water was used than traditional irrigation methods and still kept the soil moisture level appropriate. The proposed system uses minimal manual intervention, it facilitates real time monitoring and offers adaptive irrigation management. The research shows the potential of the integration of artificial intelligence with low cost embedded platforms to create scalable, water efficient and sustainable smart agriculture solutions.","农业可用水资源的日益减少、灌溉方法的不尽完善、气候的不确定性以及不断增长的粮食需求，催生了对水资源管理问题智能化、可持续解决方案的需求。本研究提出了一种基于人工智能驱动的Arduino与Raspberry Pi灌溉系统，通过实时感知、智能决策和自动化灌溉控制，最大化农业用水效率。该系统包含土壤湿度、土壤温度、土壤相对湿度、降雨量及水位传感器，以持续监测田间状况。其中，Arduino用于连接传感器和执行器，Raspberry Pi则负责数据处理、基于人工智能的分析、监控及数据记录。采集到的传感器数据经过预处理，并利用AI\u002FML模型进行分析，根据土壤及环境条件计算灌溉需求。基于生成的决策，通过继电器机制自动开启或关闭水泵或灌溉阀门。实验结果表明，该方法在减少不必要灌溉及资源利用方面具有显著效果。以代表性原型测试结果为例，AI模型能够正确识别94.2%的事件，而自动化系统可检测约98.6%的事件，平均响应时间为2.4秒。与传统灌溉方法相比，用水量减少了约35%，同时仍能保持适宜的土壤湿度水平。该系统所需人工干预极少，支持实时监控，并提供适应性灌溉管理。本研究展示了将人工智能与低成本嵌入式平台相结合，以构建可扩展、节水且可持续的智慧农业解决方案的潜力。","International Journal for Research in Applied Science and Engineering Technology","2026-09-08T00:00:00Z",66,{"impact":17,"substance":262,"depth":155,"authority":13,"freshness":263,"relevant":21,"comment":264},20,2,"研究展示AI与低成本硬件结合的精准灌溉系统，节水35%，具实践价值，但发表于普通期刊且时效性低。",[266],{"name":258,"url":255},[26,27,268,28,269],"传感器","节水农业",[271,118],"农业人工智能 智慧农业 精准灌溉 节水农业","农业人工智能智慧农业精准灌溉节水农业-1986","10.22214\u002Fijraset.2026.84732",{"doi":273,"openalex_id":275,"authors":276,"venue":258,"cited_by_count":35,"oa_url":255,"card":281,"direction":97,"ingested_from":56},"W7211968732",[277,279],{"name":278,"orcid":9},"Priyadharshini V",{"name":280,"orcid":9},"Hemapriya",{"tldr":282,"method":283,"finding":284,"direction":97,"opportunity":285},"提出AI驱动的Arduino和树莓派灌溉系统，实现实时监测与自动灌溉，节水35%。","集成多种传感器，Arduino采集，树莓派运行AI\u002FML模型决策，继电器控制灌溉","系统事件识别准确率94.2%，自动检测98.6%，响应2.4秒，节水35%且维持土壤湿度。","可探索AI模型在不同作物和气候下的泛化性，以及多节点协同和能耗优化，提升系统可扩展性。","2026-09-09T23:30:08.891805Z"]