Real-time Data Acquisition Research Articles

Distributed wireless IoT based sensing and quality monitoring system in protection of wetlands groundwater areas

Abstract This paper presents the development and implementation of a sensor network based on the Internet of Things (IoT) to monitor key groundwater quality parameters with the goal of minimizing potential risks in the protection of biodiversity. These parameters include the water level, water temperature, pH, conductivity, and dissolved oxygen. The system prioritizes scalability, ease of use, real-time data acquisition and minimal power consumption, resulting in efficient and dependable water quality monitoring with the convenience of remote applications. The continuous measurement system was set up to be a single source of information for monitoring groundwater quality in protected wetland areas across international borders, including Tompojevacki Ritovi (Municipality of Tompojevci, Croatia) and Lake Zobnatica (Municipality of Backa Topola, Serbia). The findings of two years of Wireless Sensor Network (WSN) monitoring were compared to standard laboratory methods to demonstrate the accuracy and precision of WSN readings. Appropriate calibration and installation of the WSN give a larger volume of data and thus grow the database, allowing for a more accurate identification of water contamination and a quick response in the event of pollution. Smart IoT-based sensors help to protect water quality and, as a result, the well-being of ecosystems and human communities, which is especially important in protected areas like wetlands.

Development of a photocell-based system supported by simulations for road vehicle counting and particle emissions estimation

This work presents the design and implementation of an innovative system that utilizes photocells for road vehicle counting and computational simulations to estimate traffic-related particulate (PM10) emissions. The integration of photocell technology has resulted in a robust system with reduced counting errors, ensuring high accuracy in correlating traffic counts with emission values. The system was evaluated under various environmental conditions, demonstrating its effectiveness in providing reliable real-time data on the impact of road traffic on particle emissions. The methodology included the use of the ADMS-Urban model for emission estimation and simulations with Ansys Fluent, enabling the acquisition of dependable real-time data. The results indicate a strong agreement between measured and calculated PM10 concentrations, with the system maintaining precision comparable to direct observations. The observed values of 5.0 µg/m3 and 19.2% for absolute and relative errors, respectively, demonstrate the system's remarkable performance, supported by a robust correlation represented by R2 = 0.88. This strong correlation underscores the reliability of the employed model, suggesting its capability to capture and explain a substantial portion of the variability in PM10 concentrations. Validation campaigns conducted on a street in Portugal confirmed the system's ability to accurately count vehicles and estimate particle emissions. The analysis revealed a significant agreement between measured and calculated average PM10 concentrations, with standard deviation values of 7.0 µg/m3 for measured concentrations and 7.5 µg/m3 for calculated concentrations, suggesting consistency between the datasets. These conclusions confirm that the system is a valuable tool for researchers and policymakers, providing crucial insights for the management and improvement of urban air quality, contributing to the development of urban planning policies and environmental sustainability.

Advances, challenges, and prospects of electroencephalography-based biomarkers for psychiatric disorders: a narrative review.

Owing to a lack of appropriate biomarkers for accurate diagnosis and treatment, psychiatric disorders cause significant distress and functional impairment, leading to social and economic losses. Biomarkers are essential for diagnosing, predicting, treating, and monitoring various diseases. However, their absence in psychiatry is linked to the complex structure of the brain and the lack of direct monitoring modalities. This review examines the potential of electroencephalography (EEG) as a neurophysiological tool for identifying psychiatric biomarkers. EEG noninvasively measures brain electrophysiological activity and is used to diagnose neurological disorders, such as depression, bipolar disorder (BD), and schizophrenia, and identify psychiatric biomarkers. Despite extensive research, EEG-based biomarkers have not been clinically utilized owing to measurement and analysis constraints. EEG studies have revealed spectral and complexity measures for depression, brainwave abnormalities in BD, and power spectral abnormalities in schizophrenia. However, no EEG-based biomarkers are currently used clinically for the treatment of psychiatric disorders. The advantages of EEG include real-time data acquisition, noninvasiveness, cost-effectiveness, and high temporal resolution. Challenges such as low spatial resolution, susceptibility to interference, and complexity of data interpretation limit its clinical application. Integrating EEG with other neuroimaging techniques, advanced signal processing, and standardized protocols is essential to overcome these limitations. Artificial intelligence may enhance EEG analysis and biomarker discovery, potentially transforming psychiatric care by providing early diagnosis, personalized treatment, and improved disease progression monitoring.

Evaluating metagenomics and targeted approaches for diagnosis and surveillance of viruses

BackgroundMetagenomics is a powerful approach for the detection of unknown and novel pathogens. Workflows based on Illumina short-read sequencing are becoming established in diagnostic laboratories. However, high sequencing depth requirements, long turnaround times, and limited sensitivity hinder broader adoption. We investigated whether we could overcome these limitations using protocols based on untargeted sequencing with Oxford Nanopore Technologies (ONT), which offers real-time data acquisition and analysis, or a targeted panel approach, which allows the selective sequencing of known pathogens and could improve sensitivity.MethodsWe evaluated detection of viruses with readily available untargeted metagenomic workflows using Illumina and ONT, and an Illumina-based enrichment approach using the Twist Bioscience Comprehensive Viral Research Panel (CVRP), which targets 3153 viruses. We tested samples consisting of a dilution series of a six-virus mock community in a human DNA/RNA background, designed to resemble clinical specimens with low microbial abundance and high host content. Protocols were designed to retain the host transcriptome, since this could help confirm the absence of infectious agents. We further compared the performance of commonly used taxonomic classifiers.ResultsCapture with the Twist CVRP increased sensitivity by at least 10–100-fold over untargeted sequencing, making it suitable for the detection of low viral loads (60 genome copies per ml (gc/ml)), but additional methods may be needed in a diagnostic setting to detect untargeted organisms. While untargeted ONT had good sensitivity at high viral loads (60,000 gc/ml), at lower viral loads (600–6000 gc/ml), longer and more costly sequencing runs would be required to achieve sensitivities comparable to the untargeted Illumina protocol. Untargeted ONT provided better specificity than untargeted Illumina sequencing. However, the application of robust thresholds standardized results between taxonomic classifiers. Host gene expression analysis is optimal with untargeted Illumina sequencing but possible with both the CVRP and ONT.ConclusionsMetagenomics has the potential to become standard-of-care in diagnostics and is a powerful tool for the discovery of emerging pathogens. Untargeted Illumina and ONT metagenomics and capture with the Twist CVRP have different advantages with respect to sensitivity, specificity, turnaround time and cost, and the optimal method will depend on the clinical context.

Do Audible Sounds during a Lumbar Spine Thrust Manipulation Have an Impact on Brainwave Activity?

To manage pain and stiffness of the lumbar spine, thrust manipulation is commonly used. High-velocity, small-amplitude thrust manipulation often elicits audible sounds. What causes this audible sound remains unclear, and its clinical significance has not been shown. This study aimed to identify how audible sound affects brainwave activity following a side-lying right rotatory thrust manipulation in a group of healthy individuals. This was a quasi-experimental repeated measures study design in which 44 subjects completed the study protocol. A portable Bluetooth EEG device was used to capture brainwave activity. The environment was controlled during testing to minimize any factors influencing the acquisition of real-time EEG data. After a short accommodation period, initial brainwaves were measured. Following this, each subject underwent a lumbar 4-5 side-lying right rotatory thrust manipulation, immediately followed by a second brainwave measurement. A third measurement took place one minute later, followed by a fourth one at the three-minute mark. 21 subjects did not experience audible sounds, while 23 subjects experienced audible sounds. Both groups had significant changes measured by the 14 electrodes (p < 0.05). The audible group had more significant changes, which lasted only two minutes. The lack of brainwave response differences between the audible and non-audible groups implies no direct, measurable placebo or beneficial effect from the audible sound. This study could not identify a benefit from the audible sound during an HVLA manipulation of the subjects.

Quality in beans: tracking and tracing coffee through automation and machine learning

PurposeCoffee is one of the most consumed beverages in the world and the global coffee industry is worth over $100bn. However, the industry faces significant sustainability challenges. Developing a quality traceability system to select the coffee beans and to ensure their authentication would result in economic advantages, because it allows for fraud to be avoided and increases consumer confidence.Design/methodology/approachTraceability is one of the key elements of sustainability in the coffee sector. The literature reveals that near-infrared (NIR) approaches have a huge potential for gaining rapid information about the origin and properties of coffee beans, without invasive procedures. This study demonstrates the scalability potential of automated methods of manipulation and image acquisition of coffee beans, from experimental scale to industrial lines.FindingsA solution based on the interaction of a manipulation system, a NIR spectrometer acquisition station integrated with a machine learning infrastructure and a compressed air classifier allows for the automatic separation of coffee beans into different classes of origin.Originality/valueApart from traceability, the wide industrialization of this system offers further advantages, including reduced workforce, decreased subjectivity in the evaluation and the acquisition of real-time data for labeling.

Fiber-integrated quantum microscopy system for cells.

Quantum entanglement serves as an essential resource across various fields, including quantum communication, quantum computing, and quantum precision measurement. Quantum microscope, as one of the significant applications in quantum precision measurement, could bring revolutionary advancements in both signal-to-noise ratio (SNR) and spatial resolution of imaging. Here, we present a quantum microscopy system that relies on a fully fiber-integrated high-performance energy-time entangled light source operating within the near-infrared II (NIR-II) window. Complemented by tailored real-time data acquisition and processing software, we successfully demonstrate the quantum imaging of a standard target, achieving a SNR of 131.51 ± 6.74 and a spatial resolution of 4.75 ± 0.27 µm. Furthermore, we showcase quantum imaging of cancer cells, unveiling the potential of quantum entanglement in biomedical applications. Our fiber-integrated quantum microscope, characterized by high imaging SNR, instantaneous image capture, and analysis capabilities, marks an important step toward the practical application in life sciences.

NO sensing properties of MoS2/WSe2 heterostructure at room temperature under UV light irradiation

Transition metal dichalcogenides (TMDs) have recently sparked interest in gas-sensing applications. Generally, the kinetics of gases show minimal reactivity when the sensor is operable at room temperature (RT). Photon excitation is a potential solution for enhancing gas kinetics at RT. The present study delves gas sensing properties of MoS2/WSe2 heterostructure, emphasizing its effectiveness in detecting a broad range of NO concentration (0.1 ppm to 10 ppm) at room temperature using UV-light (266 nm & 355 nm) as a catalyst. The results reveal a remarkable 13x & 5x amplification in NO detection at 10 ppm for MoS2/WSe2 heterostructure compared to pristine WSe2 and MoS2, which were primarily tested. The highest relative sensor response of 95.4 % is reported for 10 ppm of NO gas at RT under 266 nm illumination. The sensing results revealed a substantial reduction in the limit of detection (LOD) to around 16 ppb, followed by a swift response speed of 10.31/14.38 seconds. The synergistic combination of WSe2 and MoS2 and their high specific surface area enhance NO sensitivity under UV exposure. The fabricated device holds substantial importance in environmental monitoring and healthcare, paving the way for real-time data acquisition in gas sensing.

Field Evaluation and Application of Intelligent Quality Control Systems

During road construction, the accuracy of compaction work is critical for the structural stability and maintenance of the road. Although the plate load test (PLT) is commonly used for quality inspections, it is impractical to test every section due to time and cost constraints. Therefore, simpler testing methods are being extensively developed. This study compared quality inspection results using the commonly used PLT, the relatively simple dynamic cone penetrometer test (DCPT), the lightweight deflectometer (LWD) test, and an intelligent quality control system equipped with accelerometer and global positioning system (GPS) sensors in intelligent compaction (IC) rollers. The results showed a strong correlation between the conventional tests (PLT, DCPT, and LWD) and the values obtained from the intelligent quality control system. The correlation analysis between the intelligent quality control system and PLT, LWDT, and DCPT yielded R-square values of 0.69, 0.91, and 0.95, respectively, indicating significantly high correlations. The implementation of intelligent quality management systems in earthwork construction projects will facilitate a thorough verification of the compaction quality throughout all construction segments, ensuring consistent compaction across the project. By enabling real-time data acquisition and analysis, these systems differ markedly from traditional methods, reducing the frequency and necessity of manual inspections. This approach not only streamlines construction processes, but also enhances operational efficiency. As a result, integrating these intelligent systems is anticipated to significantly increase productivity by optimizing the workflow and resource utilization in earthwork construction.

Dynamometer card generation for pumping units based on CNN and electrical parameters

In the actual production process of oil fields, the real-time and accurate acquisition of dynamic recorder data from the pumping unit is of great significance for the diagnosis of well failures. The traditional method of obtaining the card of the dynamometer usually includes installing a load sensor on the auxiliary head of the pumping unit. However, due to the harsh environment of the oil field production site, these load sensors often suffer from damage, distortion, and aging, resulting in large measurement errors and low reliability. This paper proposes a mixed model of pumping based on motor electrical parameter data and CNN convolutional neural network, which has good consistency with actual data in terms of predictive performance. Thus, the highlights of this paper can be summed up in two points: (1) Based on the mathematical model of the AC motor, the speed of the motor and the torque output of the motor are accurately estimated. (2) The convolutional neural network is introduced to compensate for the errors caused by the defects of the pumping unit mechanism model.

Leveraging IoT, AI, and ML for Enhanced Decision-Making in Karnataka’s Smart Citie

The rapid urbanization in Karnataka, characterized by increasing population and infrastructure demands, necessitates innovative solutions to ensure sustainable and efficient urban management. Leveraging the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) offers significant potential to enhance the decision-making capabilities of policy makers in Karnataka’s smart cities. This research paper investigates the effectiveness of these technologies in improving urban governance, focusing on real-time data acquisition, predictive analytics, and informed policy decisions. AI and ML are crucial in the analysis and interpretation of the vast amounts of data generated by IoT devices. AI algorithms process this data to identify patterns, anomalies, and trends, while ML models predict future scenarios based on historical data. For instance, predictive analytics can forecast traffic congestion, energy demand, and potential public health crises, allowing policy makers to deploy preemptive measures. In smart city initiatives, AI-driven insights ensure that resources are allocated efficiently, urban planning is optimized, and public services are enhanced. In conclusion, the integration of IoT, AI, and ML holds transformative potential for enhancing decision-making processes in Karnataka’s smart cities. By providing real-time data, predictive insights, and efficient resource management tools, these technologies enable policy makers to address urban challenges proactively and sustainably.

Design of an Embedded Test Bench for Organic Photovoltaic Module Testing

In this article, a multipurpose embedded system for testing organic photovoltaic modules is presented. It is designed to include all the features for real-time monitoring, data acquisition, and power conversion based on a Ćuk converter, providing useful data for scientific investigation of the outdoor operation of organic photovoltaic modules. The embedded system allows both the scan of the I–V curve and the continuous operation of the organic photovoltaic module, such as at its maximum power. Voltage and current at the terminals of the organic photovoltaic module under test and up to four temperatures are continuously measured and stored on a Secure Digital card. The communication interface allows the embedded system to connect with other instruments, such as irradiance sensors, with digital serial output. The embedded system is designed both for laboratory and in-the-field use: it can be powered either by the AC electrical grid or a battery, which can also operate as a backup battery. Galvanic isolation divides the embedded system into the field-side and the logic-side functional sections, providing improved noise immunity and safe operation. The main power distribution system within the embedded system is a +9 V bus; ultra-low-noise linear low dropout regulators provide the +3.3 V and +5 V regulated voltages to supply the analog and digital circuits within the logic-side section, and a flyback converter supplies the field-side section of the board. The proposed embedded solution is validated using an experimental setup built at SolarTechLab, Politecnico di Milano. The experimental results report the feasibility of the proposed embedded system.

Affordable Green IoT-Based System for Remote Sensing of PM1, PM2.5 and PM10 Particulate Matter

Particulate matter (PM) possesses the capacity to intrude deeply into the respiratory system, even infiltrating the bloodstream. Prolonged exposure to heightened PM concentrations has been causally associated with a spectrum of respiratory ailments, encompassing asthma, bronchitis, and chronic obstructive pulmonary disease (COPD). This paper endeavours to introduce a remote sensing system tailored for the quantification of particulate matter, underpinned by the principles of the Green Internet of Things (GioT). This framework is rooted in the pursuit of an ecologically harmonious infrastructure and energy sustainability. The devised architecture integrates PM 1, PM 2.5, and PM 10 sensors, seamlessly interfaced with Arduino microcontrollers, thereby facilitating real-time data acquisition. To sustainably energize this system, solar cells are harnessed to furnish a reliable power source. Augmenting this configuration is a data communication infrastructure established upon LoRa (Long Range) technology, a hallmark of the Green Internet of Things. Concomitantly, empirical investigations have been conducted to illuminate system performance. These inquiries were conducted in conditions mirroring low particle deposition scenarios, culminating in an observed average error spectrum spanning 5.64% to 6.95%. Further examinations scrutinized the data transmission process through LoRa, unveiling an impressive maximal transmission range of 281 m while conserving data transmission viability. Additionally, an exploration of energy utilization encompassed both empty and comprehensive data transmissions, divulging a marginal disparity of 0.1 Watt per data transmission event. In the final analysis, the conducted assessments affirm the system's fidelity, disclosing an error quotient below the 10% threshold. Most notably, the discerned operational efficiency of the energy supply substantiates its synergy with sustainable energy paradigms. In summation, this study proffers an innovative remote sensing system, harmonizing the nuanced demands of PM quantification with the virtuous principles of the Green Internet of Things. By merging the acumen of PM sensors, solar-driven power provisions, and the efficiency of LoRa communication, the proposed framework establishes a salient benchmark for integrative sensor networks with ecological resonance.

Enhanced throughput message streaming methods for DAQ systems of physics experiments

In nuclear and particle experiments, to achieve a real-time software-based data acquisition (DAQ) system, a message streaming platform is adopted as a solution for real-time data streaming transmission, facilitating a decoupled and scalable architecture. As increasing data rate in recent experiments demands higher and higher throughput, existing message streaming platforms lack of high single-node throughput, guarantees of reliability, and sequentiality for high-throughput transmission applications. This paper introduces enhanced throughput message streaming methods for DAQ systems, named DAQMQ. DAQMQ utilizes in-memory storage to store messages during transmission, offering high throughput performance and configurable message retention capability. Reliability in high-throughput transmission is strictly guaranteed by employing a two-phased acknowledgment mechanism. By fast reorganizing message streams in real-time, DAQMQ achieves high-throughput non-blocking sequential transmission. Evaluations conducted on ordinary PCs with 10-Gigabit network connections show that DAQMQ achieves a stable single-node throughput of 9.0 Gbit/s even when handling up to 1024 topics. Furthermore, DAQMQ achieves sequential transmission throughput close to that of non-sequential transmission. Tests with a broker or consumer breakdown during high-throughput transmission demonstrate DAQMQ's reliability and asynchronous transmission ability.

Arduino-based real-time data acquisition systems: boosting STEM career interest

Arduino-based real-time data acquisition systems: boosting STEM career interest

Ultra-Low-Power Sensor Nodes for Real-Time Synchronous and High-Accuracy Timing Wireless Data Acquisition.

This paper presents an energy-efficient and high-accuracy sampling synchronization approach for real-time synchronous data acquisition in wireless sensor networks (saWSNs). A proprietary protocol based on time-division multiple access (TDMA) and deep energy-efficient coding in sensor firmware is proposed. A real saWSN model based on 2.4 GHz nRF52832 system-on-chip (SoC) sensors was designed and experimentally tested. The obtained results confirmed significant improvements in data synchronization accuracy (even by several times) and power consumption (even by a hundred times) compared to other recently reported studies. The results demonstrated a sampling synchronization accuracy of 0.8 μs and ultra-low power consumption of 15 μW per 1 kb/s throughput for data. The protocol was well designed, stable, and importantly, lightweight. The complexity and computational performance of the proposed scheme were small. The CPU load for the proposed solution was <2% for a sampling event handler below 200 Hz. Furthermore, the transmission reliability was high with a packet error rate (PER) not exceeding 0.18% for TXPWR ≥ -4 dBm and 0.03% for TXPWR ≥ 3 dBm. The efficiency of the proposed protocol was compared with other solutions presented in the manuscript. While the number of new proposals is large, the technical advantage of our solution is significant.

Design of a Real-Time Monitoring System for Electroencephalogram and Electromyography Signals in Cerebral Palsy Rehabilitation via Wearable Devices

Cerebral palsy is a disorder of central motor and postural development, resulting in limited mobility. Cerebral palsy is often accompanied by cognitive impairment and abnormal behavior, significantly impacting individuals and society. Time, energy, and economic investment in the rehabilitation process is substantial, yet the rehabilitation outcomes often remain unsatisfactory. Additionally, some patients have limited sensory perception during rehabilitation training, making it challenging to effectively regulate exercise intensity. Traditional evaluation methods are mostly based on recovery performance, lack guidance at the neurophysiological level, and have an unequal distribution of medical rehabilitation resources, which pose great challenges to the rehabilitation of patients. Based on the issues mentioned above, this paper proposes a real-time cerebral signal monitoring system based on wearable devices. This system can monitor and store blood oxygen, heart rate, myoelectric, and EEG signals during cerebral palsy rehabilitation, and it can track and monitor signals during the rehabilitation treatment process. The system includes two parts: hardware design and software design. The hardware design includes a data signal acquisition module, a main control chip (ESP32), a muscle electrical sensor module, a brain electrical sensor module, a blood/heart rate acquisition module, etc. It is primarily for real-time signal data acquisition, processing, and uploading to the cloud server. The software design includes functions such as data receiving, data processing, data storage, network configuration, and remote communication and enables the visual monitoring of data signals. The system can achieve real-time monitoring of electromyography, electroencephalography, and blood oxygen levels, as well as the heart rate of patients with cerebral palsy, and adjust rehabilitation training in real-time during the rehabilitation process. At the same time, based on the real-time storage of the original electromyography and electroencephalography data, it can provide auxiliary guidance for later rehabilitation evaluation and effective data support for the entire rehabilitation treatment process.

Sensor based interactive digital entertainment and gamified training to alleviate basketball player fatigue

In recent years, due to the increasing number of basketball games and training activities, basketball players often face the problem of fatigue and injury. This study aims to develop an interactive digital entertainment and gamification training method to provide basketball players with more efficient training methods and enhance their training fun. Sensors can be installed in various key parts of the athlete’s body, through the use of high-precision sensor equipment, real-time acquisition of athlete action data, the use of machine learning and data mining technology, real-time analysis and modeling of sensor data to extract key information and movement patterns. Using gamified training theory to design interactive digital entertainment system, develop a series of training scenarios and gamified tasks for different technical elements, combine sensor data and motion analysis results, and provide athletes with personalized training plans and feedback mechanisms to help them improve their technical level. Through an interactive digital entertainment system, basketball players can train and compete in a virtual environment. They can complete various training tasks by interacting with the system, which will give real-time evaluation and guidance based on the athlete’s performance to help them correct mistakes, improve technique and improve training results.

Multistep-Ahead Prediction of Logging-While-Drilling Resistivity Curves Based on Seismic-Guided Seq2Seq-Long Short-Term Memory

Summary High-temperature and high-pressure reservoirs in complex geological conditions present primary targets and significant challenges in deepsea oil and gas exploration. Limited offshore drilling operations and lack of detailed geological data hinder accurate formation pressure prediction using geoguided and offset imaging, increasing drilling hazards. Logging-while-drilling (LWD) technology provides timely and accurate subsurface information. Resistivity closely correlates with formation pressure and lithology, aiding pressure prediction. Therefore, in this study, we developed an ahead prediction workflow for LWD curves using the resistivity curve (RD) as an example. A seismic-guided sequence-to-sequence framework with the long short-term memory model (Seq2Seq-LSTM) is used to predict the RD curve at a constant depth ahead of the drill bit, utilizing the RD curve of drilled sections and seismic attributes. The network structure incorporates a direct-recursive hybrid multistep prediction strategy based on update threshold control (Dir-Rec-Update), aligning with real-time LWD data acquisition for ahead curve prediction. Using real well data cross-prediction, baseline models such as multilayer perceptron (MLP) and extreme gradient boosting (XGBoost) were compared while also investigating the impact of different configurations on the proposed Seq2Seq-LSTM. The results demonstrate that the method outperforms conventional models, with an average performance across multiple wells under a 5-m update threshold: root mean square error (RMSE) of 0.15, correlation coefficient of 0.88, and coefficient of determination of 0.77. The Seq2Seq-LSTM model and Dir-Rec-Update strategy provide accurate LWD curves ahead of the drill bit, enabling advanced drilling decisions and preventing hazards. Advanced technologies such as empirical wavelet transform (EWT) and feature selection enhance the method’s potential for curve ahead-of-time prediction.

A handheld rapid detector of soil total nitrogen based on phase-locked amplification technology

Soil total nitrogen (STN) serves as a critical indicator for agricultural productivity. Rapid STN detection is crucial for nutrient monitoring in farmland and for guiding precision fertilization. Traditional near-infrared spectroscopy-based STN detectors necessitate darkrooms or subterranean settings to negate ambient light interference, restricting their practical use. This study develops a handheld rapid STN detector employing phase-locked amplification technology to address this issue. The device comprises optical, circuit, and control units. The optical unit features a ring-shaped split fiber structure to mitigate soil surface irregularities’ effects on the signal, with a central InGaAs photodiode for signal reception. Eight wavelengths (970 nm, 1050 nm, 1085 nm, 1200 nm, 1300 nm, 1450 nm, 1550 nm, and 1600 nm) are selected for the light sources through an information theory optimized genetic algorithm. The circuit unit employs direct digital synthesis and operational amplifiers for light signal generation and modulation. Reflected soil light signals are demodulated using an InGaAs photodiode and a phase-locked amplifier, effectively filtering ambient light noise. The control unit integrates an STM32F103C8T6 microcontroller and a Raspberry Pi 4B. The STM32 microcontroller manages signal generation, channel selection, frequency control, A/D conversion, and display, while the Raspberry Pi handles data processing, uploading, and transmission. A BP neural network model optimized by genetic algorithms is embedded for real-time STN data acquisition. Cloud-based data storage and visualization are facilitated through the Alibaba Cloud IoT platform. Performance testing reveals the device’s stability and interference resistance, with a standard deviation of electrical signal under varying light conditions below 3 mV. Compared to commercial spectrometers, the correlation coefficient (r) for reflectance data at eight wavebands exceeds 0.9. Accuracy testing shows an R2 above 0.8 and an RMSE of 0.30 g/kg when comparing detector-obtained STN values with standard chemical methods. These results demonstrate the detector’s efficacy in ambient light isolation, stability, and accuracy in agricultural settings, offering technical support for variable fertilization in precision farming.