[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-545":3,"related-545":79},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":34,"secondary_topics":40,"classification":41,"search_phrases":74,"slug":77,"view_count":22,"doi":8,"paper":8,"created_at":78},545,"Development and Validation of an IoF-Based Sensing Architecture for Microclimate Monitoring: Quantifying Spatial ET₀ Bias in Mediterranean Viticulture","https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154","提出并验证一种模块化的农业物联网(IoF)传感架构,用于精准葡萄栽培参考蒸散发(ET₀)估算的高保真微气候监测。系统部署于土耳其安塔利亚的地中海葡萄园,年数据完整率 98.6%。对比 IoF 田间实测与区域气象站数据,发现区域网络对 ET₀ 系统性高估(MBE = −0.695 mm\u002F天,MAPE = 10.56%),风速、净辐射与相对湿度差异为主要驱动(分别贡献 36.6%、31% 与 30%);依赖区域数据将导致生长季灌溉盈余约 97.5 mm(11.4%)。研究凸显本地化传感层在捕捉区域网格无法反映的微气候异质性中的关键作用。",null,"## 1. Introduction\n\nThe depletion of freshwater resources and the increasing unpredictability of precipitation patterns pose critical challenges to sustainable agricultural production worldwide. Among the strategies to address water scarcity, precision irrigation management stands out as one of the most effective approaches, as it directly links water application to actual crop requirements rather than relying on generalized schedules [[1](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B1-sensors-26-05154),[2](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B2-sensors-26-05154)]. Central to precision irrigation is the accurate estimation of reference evapotranspiration (ET 0), which serves as the primary input for calculating crop water demand (ET C) through crop coefficient (K C) relationships [[3](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B3-sensors-26-05154)]. Widely recognized as the benchmark for estimation, the FAO Penman–Monteith method [[4](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B4-sensors-26-05154)] necessitates a comprehensive set of inputs, including air temperature, relative humidity, wind speed, and solar radiation [[5](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B5-sensors-26-05154)].\n\nIn practice, these meteorological inputs are typically sourced from government-operated weather stations, which serve broad geographic areas. Specifically, in the context of Türkiye, the climatic parameters utilized in the study are traditionally analyzed based on official records obtained from the national meteorological network, managed by the Turkish State Meteorological Service (TSMS). However, considerable evidence suggests that spatial heterogeneity in microclimatic conditions, driven by differences in topography, land cover, proximity to urban heat islands, and local vegetation patterns, can introduce significant deviations between regional meteorological observations and actual field-level conditions [[6](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B6-sensors-26-05154)].\n\nAchieving accurate results in ET C parameter optimization through various computational and statistical ET 0 estimation methodologies [[7](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B7-sensors-26-05154),[8](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B8-sensors-26-05154),[9](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B9-sensors-26-05154),[10](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B10-sensors-26-05154),[11](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B11-sensors-26-05154),[12](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B12-sensors-26-05154),[13](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B13-sensors-26-05154),[14](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B14-sensors-26-05154),[15](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B15-sensors-26-05154)] remains essential for the effective implementation of precision irrigation strategies. However, there remains a need for site-specific benchmark comparison of these models using localized, real-time data to overcome the inherent limitations of distant TSMS stations.\n\nThe integration of Internet of Farming (IoF) frameworks and low-cost sensor platforms has recently become instrumental in the high-fidelity acquisition of environmental variables that govern evapotranspiration dynamics. Technological advances have made it both technically and economically feasible to deploy field-level monitoring systems tailored to individual agricultural plots [[16](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B16-sensors-26-05154),[17](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B17-sensors-26-05154),[18](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B18-sensors-26-05154)]. Several studies have demonstrated the potential of these architectures; for instance, Abba et al. [[19](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B19-sensors-26-05154)] reported satisfactory performance for a smart IoT-based irrigation system, while Rodríguez-Robles et al. [[20](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B20-sensors-26-05154)] developed an autonomous sensor network for rural environments. Similarly, Rajak et al. [[21](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B21-sensors-26-05154)] emphasized the role of smart sensors in enhancing crop productivity. In a complementary approach, Ikram et al. [[22](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B22-sensors-26-05154)] demonstrated IoT-based decision systems for yield maximization. Studies in the Thessaly plain [[23](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B23-sensors-26-05154)] further integrated surface measurements with remote-sensing data to evaluate irrigation needs against the FAO framework. Furthermore, the deployment of Machine Learning (ML) algorithms alongside these IoT frameworks has shown superior efficacy in predictive modeling of ET values [[24](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B24-sensors-26-05154),[25](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B25-sensors-26-05154),[26](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B26-sensors-26-05154),[27](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B27-sensors-26-05154)]. Such precision in forecasting serves as a cornerstone for optimizing irrigation scheduling and enhancing overall agricultural productivity [[28](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B28-sensors-26-05154)]. Complementing these efforts, recent systematic reviews have synthesized trends and challenges in IoT-based irrigation sensing more broadly [[29](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B29-sensors-26-05154)], while advances in computationally efficient ET estimation approaches continue to support real-time smart-agriculture applications [[30](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B30-sensors-26-05154)].\n\nWhile the broader literature predominantly employs the term Internet of Things (IoT) to describe sensor-based connectivity in agricultural contexts, the present study adopts the more domain-specific concept of IoF. IoF extends the generic IoT paradigm by explicitly embedding agricultural operational logic, including crop-specific data acquisition, agrometeorological processing, and irrigation decision-support, into the system architecture [[17](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B17-sensors-26-05154)]. Unlike general-purpose IoT deployments, IoF frameworks are designed from the outset to capture, transmit, and interpret the biological and physical variables that govern plant-water relations under field conditions, making the term more precise and scientifically appropriate for the system described herein. This terminological distinction carries substantive scientific implications that directly shape the present study’s design and contribution. At the system design level, the IoF paradigm mandates the co-integration of crop-specific sensing priorities, agrometeorological processing logic, and irrigation decision-support within a unified architecture, commitments directly reflected in the sensor selection, canopy-level placement, and duty-cycle management strategy employed here; a generic IoT deployment, being agnostic to plant-water relations, would not by design require the simultaneous acquisition of microclimate, soil moisture, and irrigation water quality parameters. At the scientific contribution level, the IoF framing shifts the core research question from whether sensors can transmit data reliably (an IoT-level concern) to whether field-embedded agrometeorological sensing can resolve the spatial ET 0 bias introduced by reliance on distant regional stations; this reframing positions the agronomic consequences of spatial data mismatch, rather than sensor engineering performance alone, as the primary scientific output of the study. To better position the proposed system within the current state of the art, representative low-cost agricultural sensing platforms reported in the recent literature are compared in [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#table_body_display_sensors-26-05154-t001).\n\n**Table 1.** Comparison of representative low-cost agricultural IoT sensing platforms with the proposed IoF framework.\n\nAs summarized in [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#table_body_display_sensors-26-05154-t001), previous low-cost agricultural IoT sensing platforms have primarily focused on individual functionalities such as microclimate monitoring, smart irrigation, greenhouse automation, or ET 0 estimation. The novelty of the proposed system does not arise from the use of individual hardware components, which are mature and commercially available, but from their functional integration into an application-specific IoF framework for precision irrigation management. In contrast, the proposed IoF framework combines autonomous solar-powered operation, real-time vineyard microclimate monitoring, FAO-56 Penman–Monteith-based ET 0 estimation, validation against TSMS, and irrigation-oriented decision support within a unified three-tier architecture, thereby providing a more comprehensive framework for precision irrigation management.\n\nTo further situate the proposed framework within domestic and international practice, recent reviews and field deployments illustrate the diversity of application-specific IoF\u002FIoT sub-implementations that have emerged internationally, including wireless energy-balance sensor networks for crop ET determination [[31](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B31-sensors-26-05154)], IoT-based smart reference-ET estimation combined with ensemble machine learning [[32](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B32-sensors-26-05154)], IoT-based microclimate monitoring for irrigation-water optimization [[33](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B33-sensors-26-05154)], IoT-driven fertigation scheduling and greenhouse microclimate monitoring [[34](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B34-sensors-26-05154)], UAV- and ground-based canopy transpiration mapping integrated with weather data [[35](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B35-sensors-26-05154)], and IoT-integrated digital-twin frameworks for controlled-environment microclimate control [[36](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B36-sensors-26-05154)] and an electronic-atmometer-based instrumentation design for evapotranspiration measurement [[37](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B37-sensors-26-05154)], alongside broader systematic reviews of IoT sensing trends in irrigation management [[29](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B29-sensors-26-05154)] and in precision agriculture more generally [[38](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B38-sensors-26-05154)]. Within the Turkish agricultural engineering literature, comparable IoT-based prototypes have similarly been reported [[17](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B17-sensors-26-05154)], though generally without the crop-specific agrometeorological integration that defines the IoF paradigm adopted here. This diversity of deployed systems underscores that IoF, unlike generic IoT, is not defined by a single hardware configuration but by the functional co-integration of agrometeorological sensing and irrigation decision logic, precisely the criterion against which the present framework is positioned in [Table 1](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#table_body_display_sensors-26-05154-t001).\n\nDespite these advances, existing low-cost agricultural sensing platforms generally focus on individual functionalities such as environmental monitoring, irrigation automation, or ET 0 estimation. Consequently, comprehensive IoF frameworks capable of integrating autonomous field operation, continuous microclimate monitoring, FAO-56 Penman–Monteith-based ET 0 estimation, validation against regional meteorological observations, and irrigation-oriented decision support remain scarce, particularly under commercial vineyard conditions. Much of the current literature focuses on system design, technical validation, and algorithmic efficiency rather than quantifying the agronomic impact of localized versus regional data. Few studies have systematically quantified the magnitude and seasonal dynamics of ET 0 deviations arising from spatial mismatch or translated these deviations into practical irrigation management implications. These questions are particularly vital for high-value crops like the grapevine (Vitis vinifera L.). Türkiye ranks among the top grape-producing countries globally, with a production of approximately 2.52 million tons in 2025 [[39](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B39-sensors-26-05154),[40](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B40-sensors-26-05154)]. In the Mediterranean climate of the Antalya region, characterized by hot, dry summers, efficient water management is essential, as both over-irrigation and under-irrigation adversely affect yield and quality [[41](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B41-sensors-26-05154),[42](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#B42-sensors-26-05154)].\n\nTo address this discrepancy, the present study introduces an IoF-based weather station (Field) designed to capture microclimatic variables, providing a robust framework for the systematic benchmark comparison of localized ET 0 and ETc estimations against official regional observations. To this end, the following research questions are addressed:\n\n*   What is the statistical magnitude and temporal pattern of ET 0 deviations between TSMS-derived regional data and localized sources?\n\n*   Which meteorological variables function as the primary drivers of these discrepancies?\n\n*   What are the practical implications of these variances for crop water requirement estimation and irrigation scheduling?\n\nTo achieve this, the study focuses on the following four objectives:\n\n*   System development: Design and field deployment of a low-cost, modular IoF-based sensing architecture for real-time acquisition of climatic, soil, and water quality data in a commercial vineyard, providing the localized data infrastructure necessary for all subsequent analyses.\n\n*   Statistical quantification: Systematic comparison of ET 0 estimates from localized IoF data versus regional TSMS data to answer: What is the statistical magnitude of ET 0 deviations between TSMS-derived regional data and localized sources?\n\n*   Temporal pattern analysis: Regression-based identification of seasonal deviation dynamics and their meteorological drivers, with explicit attribution of the primary physical contributors to ET 0 discrepancy, addressing: Which meteorological variables function as the primary drivers of these discrepancies ?\n\n*   Irrigation impact assessment: Translation of ET 0 deviations into ET C differences for grapevine, directly addressing: What are the practical implications of these variances for crop water requirement estimation and irrigation scheduling?\n\nThe proposed layered architecture, comprising sensing, processing, and decision-support layers—was developed for deployment in commercial vineyards. Although its transferability to other crops, climatic regions, and management systems appears promising, further validation through multi-site studies is required.\n\n## 2. Materials and Methods\n\n### 2.1. Study Area and Experimental Setup\n\nField data collection was conducted in a vineyard located in the Antalya province of Türkiye (36°53′ N, 30°38′ E, altitude 39 m) covering the full annual cycle from January to December 2021. The region experiences a Mediterranean climate, characterized by hot, dry summers and mild, wet winters. To evaluate the spatial mismatch in meteorological data, observations from the national meteorological network, managed by the Turkish State Meteorological Service (TSMS), were utilized as a regional reference. Specifically, the datasets were retrieved from the TSMS Antalya Regional Station (Station No: 17302), the nearest automated weather station located approximately 15 km away in Antalya city center. The comparative analysis in this study specifically utilizes synchronized daily datasets from both the localized IoF field station and the TSMS records for the identical 2021 calendar year. This framework serves to benchmark in situ IoF sensing capabilities against a standardised regional reference and highlights the profound impact of microclimatic variations on irrigation requirement modeling. This regional baseline provides quality-controlled datasets, including air temperature, relative humidity, wind speed, and solar radiation, standardized according to international meteorological protocols.\n\n### 2.2. Monitoring System Architecture\n\nSignal acquisition and electrical interfaces. As summarized in [Table 2](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#table_body_display_sensors-26-05154-t002), the proposed IoF platform integrates heterogeneous sensing devices employing different electrical output characteristics according to the measured parameter. Sensors such as air temperature, relative humidity, soil temperature, soil moisture, electrical conductivity, pH, barometric pressure, and solar radiation generate analog or conditioned analog measurement signals that are digitized by the DAQ subsystem using a 12-bit analog-to-digital converter (ADC) with 4096 discrete quantization levels. In contrast, pulse-based sensors, including the rainfall gauge and Hall-effect wind speed sensor, are acquired through digital pulse counting, while wind direction measurements are obtained from the corresponding directional sensing circuitry. Following acquisition, all sensor measurements are synchronized by the ESP32 controller, assigned timestamps, and assembled into a unified dataset prior to cloud transmission. The system utilizes a three-tier IoF architecture (perception, network, and application), as illustrated in [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#fig_body_display_sensors-26-05154-f001).\n\n**Figure 1.** Overall architecture of the proposed Internet of Farming (IoF) platform based on the perception, network, and application layers. The perception layer acquires real-time environmental, soil, and irrigation-related measurements through distributed sensors connected to the data acquisition (DAQ) subsystem. The network layer manages data aggregation, wireless communication, and transmission through the GSM\u002FGPRS cellular network using HTTP POST requests with JSON-formatted payloads. The application layer receives, stores, processes, and visualizes the transmitted data on the cloud server hosted at Akdeniz University, where measurements are archived in a MySQL relational database and made available to users through web- and mobile-based interfaces for irrigation monitoring and decision support.\n\n**Table 2.** Technical specifications, measurement ranges, accuracy levels, and resolution of the sensors integrated into the IoF field station.\n\n#### 2.2.1. Perception Layer\n\nThis physical layer serves as the primary data collection unit, consisting of a modular multi-sensor field station and a Data Acquisition (DAQ) system. The field station integrates 12 sensors mounted on a telescopic mast extending up to 3.5 m, facilitating the real-time acquisition of microclimatic, edaphic, and hydrological variables directly from the vineyard ([Figure 2](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#fig_body_display_sensors-26-05154-f002)). This layer is specifically responsible for capturing critical parameters such as air temperature, relative humidity, and solar radiation at the crop canopy level. Detailed technical specifications for the integrated sensors, including their respective measurement ranges and accuracy levels, are provided in [Table 2](https:\u002F\u002Fwww.mdpi.com\u002F1424-8220\u002F26\u002F16\u002F5154#table_body_display_sensors-26-05154-t002).\n\n**Figure 2.** Functional block diagram of the IoF field station illustrating the integration of the sensing units, DAQ subsystem, ESP32 controller, Raspberry Pi Zero Wireless module, GSM\u002FGPRS communication unit, camera module, and power management system. Analog sensor signals are digitized by the 12-bit DAQ subsystem, while digital sensors communicate directly with the controller t","Sensors","2026-08-13T22:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,20,17,13,8,1,"论文提出基于IoF的微气候监测架构，量化地中海葡萄园ET₀空间偏差，方法新颖，数据详实，对精准灌溉有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31,32,33],"农业物联网","精准农业","微气候监测","ET0偏差校正","智慧葡萄园","传感架构","空间异质性",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":42,"locked":14,"version":43,"method":44,"evidence":45},"classified","2026-10-22.2","rules",{"candidates":46,"agricultural_evidence":49},[47,55,62,69],{"code":35,"score":48,"supported":49,"title":50,"abstract":51,"tags":54},6,true,[],[52,53],"灌溉","蒸散发",[],{"code":56,"score":57,"supported":49,"title":58,"abstract":59,"tags":61},"I5",4,[],[60],"物联网",[27],{"code":63,"score":64,"supported":49,"title":65,"abstract":66,"tags":68},"B2",3,[],[67],"葡萄",[],{"code":70,"score":22,"supported":14,"title":71,"abstract":72,"tags":73},"I1",[],[],[28],[75,76],"农业物联网 微气候监测 智慧葡萄园 空间异质性","农业物联网 微气候监测","农业物联网微气候监测智慧葡萄园空间异质性-545","2026-08-15T00:02:55.881382Z",{"total":48,"page":22,"page_size":48,"items":80},[81,171,285,355,430,508],{"id":82,"title":83,"url":84,"summary":85,"summary_zh":86,"content":8,"source_name":87,"source_url":84,"published_at":88,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":89,"score_detail":90,"sources":93,"tags":95,"primary_topic":99,"secondary_topics":100,"classification":101,"search_phrases":129,"slug":132,"view_count":133,"doi":134,"paper":135,"created_at":170},2144,"Smart Farming Cybersecurity: Key Risks and Security Principles","https:\u002F\u002Fdoi.org\u002F10.3390\u002Felectronics15184087","By combining digital sensing, network connectivity, data-driven analyses, cloud services, and automated controls, smart farming has been increasingly adopted in agricultural production. Although these technologies have improved the precision and efficiency of farm management, they also increase cybersecurity exposure as agricultural facilities are connected to external networks, platforms, and remote-control environments. This review seeks to clarify why cybersecurity should be considered a fundamental requirement in smart farming and details the major system components, cybersecurity risks, and network design considerations required for secure operation. This review first explains the concept and application scope of smart farming, and then examines how sensors, communication networks, gateways, control systems, data platforms, user interfaces, cloud infrastructure, and physical support systems contribute to farm management and cybersecurity exposure. The review also emphasizes that smart farming differs from ordinary information systems because digital data and control commands can directly affect physical processes, such as irrigation, ventilation, heating, nutrient supply, and livestock management. Based on these cyber-physical characteristics, the review summarizes the key architectural considerations for reducing cybersecurity risks, including network segmentation, data and command flow mapping, gateway and wireless security, remote access management, cloud access control, device inventory, logging, monitoring, resilience, and local fail-safe operation. Overall, ensuring cybersecurity in smart farming requires an integrated approach that protects not only data and accounts but also the reliability and continuity of agricultural production.","通过融合数字传感、网络连接、数据驱动分析、云服务和自动化控制，智慧农业（smart farming）在农业生产中日益得到采用。尽管这些技术提高了农场管理的精准性和效率，但随着农业设施与外部网络、平台和远程控制环境相连，它们也增加了网络安全暴露风险。本综述旨在阐明为何网络安全应被视为智慧农业的基本要求，并详细阐述安全运行所需的主要系统组件、网络安全风险和网络设计考量。本综述首先阐释智慧农业的概念和应用范围，继而考察传感器、通信网络、网关、控制系统、数据平台、用户界面、云基础设施和物理支撑系统如何共同作用于农场管理和网络安全暴露。本综述还强调，智慧农业不同于普通信息系统，因为数字数据和控制指令能够直接影响物理过程，如灌溉、通风、加热、养分供给和畜牧管理。基于这些信息物理特征，本综述总结了降低网络安全风险的关键架构考量，包括网络分段、数据和指令流映射、网关与无线安全、远程访问管理、云访问控制、设备清单、日志记录、监控、韧性和本地故障安全运行。总体而言，确保智慧农业的网络安全需要一种综合性方法，不仅保护数据和账户，还要保护农业生产的可靠性和连续性。","Electronics","2026-09-10T00:00:00Z",76,{"impact":91,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":92},18,"系统梳理智慧农业网络安全的架构性风险与防护原则，对农业信息化建设有实质参考价值，但属综述性论文，产业影响偏间接。",[94],{"name":87,"url":84},[96,27,28,97,98],"智慧农业","农业网络安全","数据安全",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":102},{"candidates":103,"agricultural_evidence":49},[104,111,115,120,124],{"code":105,"score":48,"supported":49,"title":106,"abstract":107,"tags":110},"C",[],[108,109],"畜牧","livestock",[],{"code":35,"score":48,"supported":49,"title":112,"abstract":113,"tags":114},[],[52,36],[],{"code":70,"score":116,"supported":49,"title":117,"abstract":118,"tags":119},5,[],[96],[96,28],{"code":56,"score":22,"supported":14,"title":121,"abstract":122,"tags":123},[],[],[27],{"code":125,"score":22,"supported":14,"title":126,"abstract":127,"tags":128},"I6",[],[],[98],[130,131],"农业网络安全 农业物联网 数据安全 智慧农业","农业网络安全 农业物联网","农业网络安全农业物联网数据安全智慧农业-2144",2,"10.3390\u002Felectronics15184087",{"doi":134,"openalex_id":136,"authors":137,"venue":87,"cited_by_count":162,"oa_url":84,"card":163,"direction":167,"ingested_from":169},"W7212146111",[138,140,142,145,148,151,153,155,157,159],{"name":139,"orcid":8},"Sunmi Kong",{"name":141,"orcid":8},"Chang Ha Park",{"name":143,"orcid":144},"Kyung Jun Lee","https:\u002F\u002Forcid.org\u002F0000-0002-8972-0391",{"name":146,"orcid":147},"Tae‐Su Kim","https:\u002F\u002Forcid.org\u002F0000-0002-6402-8139",{"name":149,"orcid":150},"Yeong‐Seon Won","https:\u002F\u002Forcid.org\u002F0009-0000-0950-7834",{"name":152,"orcid":8},"Min-Ho Jo",{"name":154,"orcid":8},"SongYi Han",{"name":156,"orcid":8},"Ju Eun Ko",{"name":158,"orcid":8},"Hyeon Ju Nam",{"name":160,"orcid":161},"Hyeon Ji Yeo","https:\u002F\u002Forcid.org\u002F0000-0002-5474-8204",0,{"tldr":164,"method":165,"finding":166,"direction":167,"opportunity":168},"综述智慧农业网络安全风险，提出保障生产可靠性的架构原则。","文献综述，分析传感器、网关、云平台等系统组件与网络设计。","智慧农业网络安全需保护物理生产过程，而非仅数据与账户。","智慧农业 \u002F 农业物联网","可研究面向灌溉、通风等物理过程的轻量级入侵检测与本地失效安全机制。","openalex","2026-09-11T23:30:10.445737Z",{"id":172,"title":173,"url":174,"summary":175,"summary_zh":176,"content":8,"source_name":177,"source_url":174,"published_at":178,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":179,"score_detail":180,"sources":184,"tags":186,"primary_topic":189,"secondary_topics":195,"classification":201,"search_phrases":260,"slug":263,"view_count":162,"doi":264,"paper":265,"created_at":284},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)","2026-10-21T00:00:00Z",80,{"impact":91,"substance":181,"depth":91,"authority":20,"freshness":182,"relevant":22,"comment":183},22,9,"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[185],{"name":177,"url":174},[96,187,60,28,188],"农业人工智能","气候韧性",{"code":190,"slug":191,"name":192,"path":193,"parent_code":194},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[196,200],{"code":56,"slug":197,"name":198,"path":199,"parent_code":194},"agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":42,"locked":14,"version":43,"method":44,"evidence":202},{"candidates":203,"agricultural_evidence":49},[204,215,221,228,233,241,248,254],{"code":190,"score":91,"supported":49,"title":205,"abstract":207,"tags":214},[206],"artificial intelligence",[208,209,210,211,212,213],"深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[187],{"code":70,"score":216,"supported":49,"title":217,"abstract":219,"tags":220},16,[218],"precision agriculture",[28,218],[96,28],{"code":56,"score":13,"supported":49,"title":222,"abstract":223,"tags":227},[],[60,224,225,226],"边缘计算","IoT","edge computing",[60],{"code":35,"score":182,"supported":49,"title":229,"abstract":230,"tags":232},[],[52,231,36],"水资源",[],{"code":234,"score":235,"supported":49,"title":236,"abstract":237,"tags":240},"H7",7,[],[238,239],"气候变化","climate change",[188],{"code":242,"score":48,"supported":49,"title":243,"abstract":244,"tags":247},"H2",[],[245,246],"土壤","soil",[],{"code":249,"score":64,"supported":49,"title":250,"abstract":251,"tags":253},"A7",[],[252],"间作",[],{"code":255,"score":64,"supported":49,"title":256,"abstract":257,"tags":259},"F3",[],[258],"养分管理",[],[261,262],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":264,"openalex_id":266,"authors":267,"venue":177,"cited_by_count":162,"oa_url":174,"card":278,"direction":167,"ingested_from":169},"W7223121456",[268,271,273,275],{"name":269,"orcid":270},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":272,"orcid":8},"Oluwadare OYEBODE",{"name":274,"orcid":8},"Iretiayo Adelaiye",{"name":276,"orcid":277},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":279,"method":280,"finding":281,"direction":282,"opportunity":283},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","农业人工智能与决策模型","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z",{"id":286,"title":287,"url":288,"summary":289,"summary_zh":290,"content":8,"source_name":291,"source_url":288,"published_at":178,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":292,"score_detail":293,"sources":295,"tags":297,"primary_topic":301,"secondary_topics":302,"classification":309,"search_phrases":339,"slug":342,"view_count":162,"doi":343,"paper":344,"created_at":354},5552,"INTEGRATING IoT, REMOTE SENSING, AND ARTIFICIAL INTELLIGENCE FOR SMART IRRIGATION: A REVIEW OF ARCHITECTURES, METHODS, AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531020","This research paper reviews the integration of Internet of Things (IoT), remote sensing, and Artificial Intelligence (AI) in smart irrigation systems. It discusses system architectures, intelligent irrigation techniques, automated water management, key challenges, and future research opportunities to improve water-use efficiency and promote sustainable agriculture.","本文综述了物联网（Internet of Things, IoT）、遥感与人工智能（Artificial Intelligence, AI）在智能灌溉系统中的融合应用，探讨了系统架构、智能灌溉技术、自动化水资源管理、关键挑战及未来研究方向，以提升用水效率并促进可持续农业发展。","Zenodo (CERN European Organization for Nuclear Research)",75,{"impact":91,"substance":18,"depth":216,"authority":17,"freshness":182,"relevant":22,"comment":294},"系统综述物联网、遥感与AI融合的智能灌溉架构与方法，属智慧农业细分领域有实质增量的国际综述，值得入选。",[296],{"name":291,"url":288},[187,27,298,299,300],"遥感","智慧灌溉","节水农业",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[303,304],{"code":56,"slug":197,"name":198,"path":199,"parent_code":194},{"code":305,"slug":306,"name":307,"path":308,"parent_code":194},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":42,"locked":14,"version":43,"method":44,"evidence":310},{"candidates":311,"agricultural_evidence":49},[312,317,322,328,332],{"code":35,"score":313,"supported":49,"title":314,"abstract":315,"tags":316},19,[36],[52,231,36],[299,300],{"code":56,"score":91,"supported":49,"title":318,"abstract":319,"tags":321},[225],[60,320,225],"internet of things",[27],{"code":305,"score":323,"supported":49,"title":324,"abstract":326,"tags":327},15,[325],"remote sensing",[298,325],[298],{"code":190,"score":17,"supported":49,"title":329,"abstract":330,"tags":331},[206],[206],[187],{"code":333,"score":48,"supported":49,"title":334,"abstract":335,"tags":338},"H8",[],[336,337],"可持续农业","sustainable agriculture",[],[340,341],"IoT 遥感 AI 智能灌溉","农业人工智能 农业物联网 智慧灌溉 节水农业","IoT遥感AI智能灌溉-5552","10.5281\u002Fzenodo.23531020",{"doi":343,"openalex_id":345,"authors":346,"venue":291,"cited_by_count":162,"oa_url":288,"card":349,"direction":167,"ingested_from":169},"W7223090389",[347],{"name":348,"orcid":8},"Patil",{"tldr":350,"method":351,"finding":352,"direction":167,"opportunity":353},"综述物联网、遥感与人工智能融合的智能灌溉系统架构、方法与挑战。","文献综述，梳理IoT、遥感与AI集成的系统架构与智能灌溉技术。","融合三者可提升用水效率并推动可持续农业，但仍面临关键挑战。","可针对多源数据融合的实时灌溉决策模型与边缘部署开展实证研究。","2026-10-22T11:20:18.028252Z",{"id":356,"title":357,"url":358,"summary":359,"summary_zh":360,"content":8,"source_name":291,"source_url":358,"published_at":361,"category":12,"cover_url":8,"hotness":362,"is_selected":14,"score":363,"score_detail":364,"sources":367,"tags":371,"primary_topic":374,"secondary_topics":375,"classification":377,"search_phrases":407,"slug":410,"view_count":162,"doi":411,"paper":412,"created_at":429},5474,"Real-World Impact: Transforming Agriculture with Technology A Multi-Stakeholder Review of Smart Monitoring, Precision Irrigation, and Digital Value-Chain Integration","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23510500","There's a deliberate and significant transition from input to information. This article combines peer-reviewed evidence and public-sector data on six interrelated technology clusters that are fundamentally changing on-farm and off-farm decision making artificial intelligence (AI) based Farm Management Platforms, Internet of Things (IoT) smart irrigation, aerial and satellite crop monitoring, blockchain-enabled supply-chain digitisation, sustainability and carbon-tracking instruments, and farm data analytics dashboards. In the literature examined, precision tools have been correlated with 15% to 30% gains in crop yields, 30% to 55% of water saved during irrigation, and 10% to 20% of operational costs reduced, all with a range of variability based on the crop, the geographical region, and the size of the farm. It examines the mechanisms of these effects on the one hand and the differential rates of benefit, on the other, between large commercial farms and smallholders, and the policy and infrastructural conditions that drive actual adoption. It also highlights areas of current evidence which are lacking, such as limited long horizon field trials, poor interoperability standards, and lack of social outcome data. The article ends with a discussion on how to turn pilots into lasting, system-level change in food production, both from the government and enterprise side, as well as from the perspective of small and medium producers.","从投入要素到信息的转变是有意为之且意义重大的。本文结合经同行评审的证据与公共部门数据，聚焦六个相互关联、正在从根本上改变农场内外决策方式的技术集群：基于人工智能（AI）的农场管理平台、物联网（IoT）智能灌溉、航空与卫星作物监测、区块链赋能的供应链数字化、可持续性与碳追踪工具，以及农场数据分析仪表盘。在所考察的文献中，精准工具与作物产量提升15%至30%、灌溉用水节约30%至55%、运营成本降低10%至20%相关联，且这些效果因作物、地理区域和农场规模而存在不同程度的差异。本文一方面考察这些效应的作用机制，另一方面分析大型商业农场与小农户之间受益程度的差异，以及驱动实际采用的政策与基础设施条件。文章还指出了当前证据的薄弱之处，如缺乏长期田间试验、互操作性标准不完善以及社会成效数据缺失。文章最后讨论了如何从政府与企业两侧，以及中小生产者视角出发，将试点项目转化为粮食生产中持久、系统层面的变革。","2026-11-25T00:00:00Z",25,73,{"impact":181,"substance":365,"depth":19,"authority":20,"freshness":162,"relevant":22,"comment":366},21,"多利益相关方综述整合六类技术集群的量化效益与采纳差异，信息增量足、产业参考价值高，但发布于一个月后，时效性不足，暂不宜进入每日精选。",[368,369],{"name":291,"url":358},{"name":291,"url":370},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23527988",[96,187,27,372,373],"区块链溯源","精准灌溉",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[376],{"code":56,"slug":197,"name":198,"path":199,"parent_code":194},{"status":42,"locked":14,"version":43,"method":44,"evidence":378},{"candidates":379,"agricultural_evidence":49},[380,384,388,392,398,403],{"code":35,"score":323,"supported":49,"title":381,"abstract":382,"tags":383},[36],[52,36],[373],{"code":56,"score":13,"supported":49,"title":385,"abstract":386,"tags":387},[],[60,320,225],[27],{"code":190,"score":57,"supported":49,"title":389,"abstract":390,"tags":391},[],[206],[187],{"code":393,"score":64,"supported":49,"title":394,"abstract":395,"tags":397},"G2",[],[396],"小农户",[],{"code":399,"score":22,"supported":14,"title":400,"abstract":401,"tags":402},"E4",[],[],[372],{"code":70,"score":22,"supported":14,"title":404,"abstract":405,"tags":406},[],[],[96],[408,409],"Zenodo 智慧农业 多利益相关方综述","精准灌溉 物联网 区块链 供应链","Zenodo智慧农业多利益相关方综述-5474","10.5281\u002Fzenodo.23510500",{"doi":411,"openalex_id":413,"authors":414,"venue":291,"cited_by_count":162,"oa_url":358,"card":423,"direction":167,"ingested_from":169},"W7223034914",[415,418,420],{"name":416,"orcid":417},"A. Shaji George","https:\u002F\u002Forcid.org\u002F0000-0002-8677-3682",{"name":419,"orcid":8},"Dr.T.Baskar",{"name":421,"orcid":422},"P. Balaji Srikaanth","https:\u002F\u002Forcid.org\u002F0000-0003-4717-7367",{"tldr":424,"method":425,"finding":426,"direction":427,"opportunity":428},"综述六大农业数字技术集群对农场决策的变革及实际效益与采纳差异。","综合同行评议证据与公共部门数据，多利益相关方综述六类技术。","精准技术可增产15-30%、节水30-55%、降本10-20%，但小农获益不均。","数字乡村与农业信息化","缺乏长期田间试验、互操作标准与社会效益数据，小农采纳机制是研究空白。","2026-10-21T23:30:08.684798Z",{"id":431,"title":432,"url":433,"summary":434,"summary_zh":435,"content":8,"source_name":291,"source_url":433,"published_at":436,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":437,"score_detail":438,"sources":440,"tags":442,"primary_topic":447,"secondary_topics":453,"classification":455,"search_phrases":492,"slug":495,"view_count":162,"doi":496,"paper":497,"created_at":507},5381,"Climate-Smart Floriculture: Strategies for Sustainable Flower Production Under a Changing Climate","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23467834","Floriculture is a high-value horticultural sector comprising cut flowers, potted flowering plants, foliage plants, ornamental bulbs and nursery plants. The sector is increasingly exposed to climate variability through rising temperature, drought, irregular rainfall, extreme weather events, elevated atmospheric CO₂ and changing pest and disease pressure. These stresses can influence vegetative growth, flowering, flower quality, production scheduling and postharvest performance, thereby affecting the economic value of ornamental crops. Climate-smart floriculture provides an integrated framework for improving resilience while maintaining productivity and resource-use efficiency. Key strategies include climate-resilient cultivar selection, efficient irrigation, soil-moisture conservation, protected cultivation, microclimate regulation, integrated pest management and precision technologies. Genetic improvement offers a long-term approach for developing cultivars capable of maintaining desirable ornamental traits under environmental stress, whereas protected cultivation can buffer crops against external climatic fluctuations. Efficient irrigation, rainwater harvesting and improved soil management can reduce vulnerability to water scarcity, while integrated pest management can address climate-associated changes in pest and disease pressure. Precision agriculture, controlled-environment agriculture and machine-learning-based decision systems offer additional opportunities for real-time monitoring and resource optimisation. However, technological adoption is influenced by investment costs, knowledge availability, extension services, market access and supportive policies. This article discusses the major climate-related risks to floriculture and evaluates integrated strategies for developing productive, resource-efficient and climate-resilient flower production systems.","花卉产业是一个高附加值的园艺部门，包括切花、盆栽开花植物、观叶植物、观赏球根植物和苗木。该产业日益受到气候变率的威胁，表现为气温升高、干旱、降雨不规律、极端天气事件、大气CO₂浓度升高以及病虫害压力变化。这些胁迫可影响营养生长、开花、花卉品质、生产调度和采后性能，进而影响观赏作物的经济价值。气候智慧型花卉产业提供了一个综合框架，可在保持生产力和资源利用效率的同时提高韧性。关键策略包括气候韧性品种选择、高效灌溉、土壤水分保持、设施栽培、微气候调控、病虫害综合治理和精准技术。遗传改良为培育能够在环境胁迫下保持理想观赏性状的品种提供了长期途径，而设施栽培可缓冲作物免受外部气候波动的影响。高效灌溉、雨水收集和改善的土壤管理可降低对水资源短缺的脆弱性，而病虫害综合治理可应对气候相关的病虫害压力变化。精准农业、受控环境农业和基于机器学习的决策系统为实时监测和资源优化提供了额外机会。然而，技术采用受到投资成本、知识可得性、推广服务、市场准入和支持性政策的影响。本文讨论了花卉产业面临的主要气候相关风险，并评估了发展高产、资源高效和气候韧性花卉生产系统的综合策略。","2026-10-19T00:00:00Z",77,{"impact":91,"substance":18,"depth":19,"authority":20,"freshness":182,"relevant":22,"comment":439},"系统综述花卉业气候风险与综合应对策略，具行业参考价值，但属综述性论文、非突破性成果。",[441],{"name":291,"url":433},[28,443,444,445,446],"病虫害防控","气候智慧农业","花卉产业","设施园艺",{"code":448,"slug":449,"name":450,"path":451,"parent_code":452},"B4","flowers","花卉与观赏植物","\u002Ftopic\u002Fhorticulture\u002Fflowers","B",[454],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":42,"locked":14,"version":43,"method":44,"evidence":456},{"candidates":457,"agricultural_evidence":49},[458,465,469,475,479,483,487],{"code":448,"score":91,"supported":49,"title":459,"abstract":461,"tags":464},[460],"floriculture",[462,463,460],"花卉","ornamental",[445],{"code":35,"score":182,"supported":49,"title":466,"abstract":467,"tags":468},[],[52,231,36],[],{"code":234,"score":235,"supported":49,"title":470,"abstract":471,"tags":474},[],[472,473],"干旱","drought",[444],{"code":70,"score":235,"supported":49,"title":476,"abstract":477,"tags":478},[],[28,218],[28],{"code":242,"score":48,"supported":49,"title":480,"abstract":481,"tags":482},[],[245,246],[],{"code":190,"score":64,"supported":49,"title":484,"abstract":485,"tags":486},[],[209],[],{"code":488,"score":22,"supported":14,"title":489,"abstract":490,"tags":491},"B7",[],[],[446],[493,494],"花卉 可持续生产 气候变化","气候智慧农业 病虫害防控 精准农业 花卉产业","花卉可持续生产气候变化-5381","10.5281\u002Fzenodo.23467834",{"doi":496,"openalex_id":498,"authors":499,"venue":291,"cited_by_count":162,"oa_url":433,"card":502,"direction":167,"ingested_from":169},"W7222973900",[500],{"name":501,"orcid":8},"Shriya Gupta1 and Harish Kumar2",{"tldr":503,"method":504,"finding":505,"direction":167,"opportunity":506},"综述气候智慧型花卉生产策略，评估气候变化风险与综合适应措施。","文献综述，整合抗逆品种、灌溉、保护地栽培、IPM与精准技术。","综合策略可提升花卉气候韧性，但技术采纳受成本、知识与政策制约。","花卉领域精准农业与机器学习决策应用少，可研究低成本智能环境调控与品种-技术协同。","2026-10-20T23:30:08.681802Z",{"id":509,"title":510,"url":511,"summary":512,"summary_zh":513,"content":8,"source_name":87,"source_url":511,"published_at":436,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":514,"score_detail":515,"sources":517,"tags":519,"primary_topic":522,"secondary_topics":523,"classification":525,"search_phrases":544,"slug":547,"view_count":162,"doi":548,"paper":549,"created_at":567},5378,"IoT-Enabled Smart Irrigation Using ESP32 and LoRa for Precision Water Management in Connectivity-Limited Regions","https:\u002F\u002Fdoi.org\u002F10.3390\u002Felectronics15204831","In this paper, a new method for irrigation systems in agriculture has been reported. This work aims to design and implement a smart irrigation system based on the ESP32 platform and Internet of Things (IoT) technologies for optimizing water usage management. With the rapid growth of Internet of Things (IoT) technologies, where devices are connected and can be controlled through the internet, it is now feasible to design smart irrigation systems based on real-time environmental data to deliver optimal water use with minimal human intervention. In Sahara areas, where communication networks and internet connections are lacking, alternative communication protocols are necessary for data transfer. LoRa technology based on the SX1278 transceiver provides long-range, low-power communication that can achieve multi-kilometer ranges (up to 10–15 km in rural areas) under ideal conditions: line-of-sight, high antenna height, low data rate, and open rural environments between distributed sensor nodes and a central gateway. However, this theoretical range cannot be assumed in practice. Field tests under standard farm deployment conditions (ground-level placement and vegetation interference) showed that the link was lost at approximately 120 m, which validates the system at the scale of a field plot. This project used Arduino (version 2.3.7) software to program the NodeMCU (ESP32) board to link the board with the Blynk cloud application. The project is used to monitor the temperature, humidity, and soil moisture and do the command for the irrigation system, with master and slave cards using LoRa technology as a communication tool to replace communication systems like WiFi or cellular networks in deserted or remote areas, which might not offer sufficient coverage or could be too expensive to build. To address this challenge, this paper presents the design and implementation of a smart irrigation system based on the ESP32 microcontroller and LoRa (SX1278) technology, with the long-term objective of helping in the modernization of traditional agriculture and environmental sustainability. Extending the coverage to larger fields and Sahara areas would require elevated antennas, optimized LoRa parameters (spreading factor and bandwidth), multi-hop relay nodes, or multiple gateways; these strategies are proposed as future work.","本文报道了一种农业灌溉系统的新方法。本工作旨在设计和实现一种基于ESP32平台和物联网（Internet of Things, IoT）技术的智能灌溉系统，以优化用水管理。随着物联网技术的快速发展，设备得以互联并可通过互联网进行控制，如今基于实时环境数据设计智能灌溉系统，以在最少人工干预下实现最优用水已成为可能。在撒哈拉地区，由于缺乏通信网络和互联网连接，数据传输需要采用替代通信协议。基于SX1278收发器的LoRa技术可提供远距离、低功耗通信，在理想条件下（视距传输、天线高度较高、低数据速率以及分布式传感器节点与中心网关之间开阔的农村环境），其通信距离可达数公里（农村地区可达10–15 km）。然而，这一理论距离在实际中并不能假定成立。在标准农田部署条件下（地面放置和植被干扰）进行的现场测试表明，链路在约120 m处即发生中断，这验证了系统在田块尺度上的可行性。本项目使用Arduino（版本2.3.7）软件对NodeMCU（ESP32）开发板进行编程，将开发板与Blynk云应用相连接。该项目用于监测温度、湿度和土壤水分，并向灌溉系统发送指令，主从卡采用LoRa技术作为通信工具，以替代WiFi或蜂窝网络等通信系统——这些系统在荒漠或偏远地区可能覆盖不足，或建设成本过高。为应对这一挑战，本文提出了基于ESP32微控制器和LoRa（SX1278）技术的智能灌溉系统的设计与实现，其长期目标是助力传统农业的现代化和环境可持续性。要将覆盖范围扩展至更大田块和撒哈拉地区，需要架高天线、优化LoRa参数（扩频因子和带宽）、采用多跳中继节点或部署多个网关；这些策略被提出作为未来工作。",67,{"impact":17,"substance":91,"depth":216,"authority":20,"freshness":21,"relevant":22,"comment":516},"面向无网络覆盖地区的ESP32+LoRa智能灌溉系统，含实地链路测试数据，方法有参考价值但属细分技术进展，未达每日精选门槛。",[518],{"name":87,"url":511},[96,60,28,520,521],"节水灌溉","LoRa",{"code":56,"slug":197,"name":198,"path":199,"parent_code":194},[524],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":42,"locked":14,"version":43,"method":44,"evidence":526},{"candidates":527,"agricultural_evidence":49},[528,532,536,540],{"code":56,"score":91,"supported":49,"title":529,"abstract":530,"tags":531},[225],[60,320,225],[60],{"code":35,"score":323,"supported":49,"title":533,"abstract":534,"tags":535},[36],[52,36],[520],{"code":242,"score":48,"supported":49,"title":537,"abstract":538,"tags":539},[],[245,246],[],{"code":70,"score":133,"supported":14,"title":541,"abstract":542,"tags":543},[],[],[96,28],[545,546],"ESP32 LoRa 智能灌溉","SX1278 精准灌溉","ESP32LoRa智能灌溉-5378","10.3390\u002Felectronics15204831",{"doi":548,"openalex_id":550,"authors":551,"venue":87,"cited_by_count":162,"oa_url":511,"card":562,"direction":167,"ingested_from":169},"W7222959710",[552,554,556,559],{"name":553,"orcid":8},"Hanfoug Salah",{"name":555,"orcid":8},"Riad Saidi",{"name":557,"orcid":558},"Abdelaziz Aouiche","https:\u002F\u002Forcid.org\u002F0000-0003-1378-7941",{"name":560,"orcid":561},"Yazeed Alkhrijah","https:\u002F\u002Forcid.org\u002F0000-0001-7352-003X",{"tldr":563,"method":564,"finding":565,"direction":167,"opportunity":566},"基于ESP32与LoRa构建智能灌溉系统，在无网络覆盖区实现精准用水管理。","ESP32采集温湿度和土壤水分，经LoRa(SX1278)传输至网关并接入Bly","实际农田条件下LoRa链路仅约120米即中断，远低于理论公里级。","可研究多跳中继、天线高度与扩频参数优化，提升偏远农田LoRa组网覆盖与可靠性。","2026-10-20T23:30:08.464791Z"]