Installation Of Sensors Research Articles

Error correction method based on dual-beam laser for curved rail profile measurement

The current method of dynamic rail profile measurement involves the installation of a line-structured light sensor at the base of the train. The accuracy of this measurement is influenced by the vertical relationship between the laser plane of the light sensor and the longitudinal direction of the rail (LDR). When a train travels in a straight line, the normal of the laser plane aligns with the LDR. However, when the train curves, the angle at which its wheels connect with the rails causes the laser plane’s normal direction to deviate from the LDR, leading to measurement errors. To address this issue, we propose a method for curved rail profile measurement using a dual-beam laser to correct these errors. This method involves generating an auxiliary 3D rail reflecting the LDR and a virtual 3D rail reflecting the normal direction of the laser plane from the cross-section image of the dual-beam laser. An optimization function is then formulated to determine the optimal auxiliary plane (optimal-AP) by analyzing the alignment or intersection between the auxiliary and virtual 3D rails. Distorted contour points are projected onto the optimal-AP to rectify errors. Experiments validate the accuracy and effectiveness of this proposed method. The results show that, regardless of pitch or yaw movement between the laser plane and the LDR, the error in measuring corrected profile wear remains consistently below 0.10 millimeters, thereby meeting the accuracy standard for rail wear measurement. This approach rectifies measurement errors in curved rail profiles from a 3D perspective, ensuring accurate measurements even under complex working conditions. It also provides a valuable reference for error analysis and improving dynamic rail profile measurement accuracy.

Streets classification models by urban features for road traffic noise estimation

Road traffic is the primary source of environmental noise pollution in cities. This problem is also spreading due to inadequate urban expansion planning. Hence, integrating road traffic noise analysis into urban planning is necessary for reducing city noise in an effective, adaptable, and sustainable way. This study aims to develop a methodology that applies to any city for the stratification of urban roads by their functionality through only their urban features. It is intended to be a tool to cluster similar streets and, consequently, traffic noise to enable urban and transportation planners to support the reduction of people's noise exposure. Three multivariate ordered logistic regression statistical models (Model 1, 2, and 3) are presented that significantly stratify urban roads into five, four, and three categories, respectively. The developed models exhibit a McFadden pseudo-R2 between 0.5 and 0.6 (equivalent to R2 >0.8). The choice between Model 1 or 2 depends on the scale of the city. Model 1 is recommended for developed cities with an extensive road network, while Model 2 is most suitable in intermediate and growing cities. On the other hand, Model 3 could be applied at any city scale but focused on local management of transit routes and for designing acoustic sensor installations, urban soundwalks, and identification of quiet areas. Urban features related to road width and length, presence of transport infrastructure, and public transport routes are associated with increased traffic noise in all three models. These models prove useful for future action plans aimed at reducing noise through strategic urban planning.

A study of oil–water two-phase flow pattern recognition based on thermal sensors

To understand the flow characteristics and kinetic modelling of oil–water two-phase flow(OWTPF), this paper employs thermal array sensors to identify the flow pattern (FP) in OWTPF horizontal pipelines. Firstly, the thermal array sensor installation was constructed. Secondly, the time-varying characteristics of multi-feature signals were qualitatively analyzed using the adaptive optimal kernel time–frequency representation (AOK-TFR) method and unthresholded recurrence plot (URP) method. Finally, the sparrow search algorithm-support vector machine (SSA-SVM) machine learning algorithm is used for quantitative prediction of multi-feature classification, which leads to high prediction accuracy. The experiment proves that the correct rate of the thermal method in recognizing six types of OWTPF is above 91%, the correct rate of five types of flow is above 97.33%. This paper adopts the thermal method to identify the multiphase FP, which is a major innovation in this paper and also lays the foundation for recognizing the flow characteristics of multiphase flow.

Prediction of Icing on Wind Turbines Based on SCADA Data via Temporal Convolutional Network

Icing on the blades of wind turbines during winter seasons causes a reduction in power and revenue losses. The prediction of icing before it occurs has the potential to enable mitigating actions to reduce ice accumulation. This paper presents a framework for the prediction of icing on wind turbines based on Supervisory Control and Data Acquisition (SCADA) data without requiring the installation of any additional icing sensors on the turbines. A Temporal Convolutional Network is considered as the model to predict icing from the SCADA data time series. All aspects of the icing prediction framework are described, including the necessary data preprocessing, the labeling of SCADA data for icing conditions, the selection of informative icing features or variables in SCADA data, and the design of a Temporal Convolutional Network as the prediction model. Two performance metrics to evaluate the prediction outcome are presented. Using SCADA data from an actual wind turbine, the model achieves an average prediction accuracy of 77.6% for future times of up to 48 h.

Research on Overload Early Warning System for Railcars

Railcars are prone to suffer from overloading problems during its operation, which seriously affects the service life of railcars and rails, while posing a serious potential safety hazard. This paper In this paper, the weight of the car load is calculated by installation of a gravity sensor on the railcar and collection of the data from the gravity sensor through applying the A/D conversion module of the MCU. The overloading warning function of railcars is realized according to the drive alarm indicator of overloading information, and displaying the overloading information on the liquid crystal display screen.

Impact assessment of mandatory safety device installation for gas stoves.

Numerous fire accidents have occurred in Japan owing to food overheating when cooking on gas stoves because users left the cooking area unattended. In response, authorities enacted a legal revision mandating temperature sensor installation on gas stove burners. Nevertheless, the actual effectiveness of this measure remains uncertain. Furthermore, prior studies have raised concerns about the efficacy of specific safety standards for consumer products owing to insufficient evidence of reduction in accidents. Consequently, this study seeks to assess the impact of the standards revision implemented in 2008, requiring the placement of safety devices on all gas stove burners. Through a comprehensive analysis, it was aimed to ascertain how these measures have precisely influenced safety outcomes in cooking-related incidents. The data in this study were subdivided to mitigate potential bias from user attributes during data collection. The analysis employed the difference-in-differences method, specifically utilizing one-burner gas stoves unaffected by the standards revision. The data used in this study include accidents associated with gas stoves, spanning the period 2007-2019. To estimate the likelihood of overheating accidents, a binomial distribution model that utilized the Markov chain Monte Carlo methods was applied. For gas stoves with two or more burners, which were affected by the standards revision, the estimated probability of overheating accidents decreased by approximately 80%. In contrast, no decreasing trend was observed for one-burner gas stoves. The analysis suggests that the mandatory installation of safety devices on gas stoves has indeed resulted in a significant reduction in overheating accidents.

AI-Driven Virtual Sensors for Real-Time Dynamic Analysis of Mechanisms: A Feasibility Study

The measurement of the ground forces on a real structure or mechanism in operation can be time-consuming and expensive, particularly when production cannot be halted to install sensors. In cases in which disassembling the parts of the system to accommodate sensor installation is neither feasible nor desirable, observing the structure or mechanism in operation and quickly deducing its force trends would facilitate monitoring activities in industrial processes. This opportunity is gradually becoming a reality thanks to the coupling of artificial intelligence (AI) with design techniques such as the finite element and multi-body methods. Properly trained inferential models could make it possible to study the dynamic behavior of real systems and mechanisms in operation simply by observing them in real time through a camera, and they could become valuable tools for investigation during the operation of machinery and devices without the use of additional sensors, which are difficult to use and install. In this paper, the idea presented is developed and applied to a simple mechanism for which the reaction forces during operating conditions are to be determined. This paper explores the implementation of an innovative vision-based virtual sensor that, through data-driven training, is able to emulate traditional sensing solutions for the estimation of reaction forces. The virtual sensor and relative inferential model is validated in a scenario as close to the real world as possible, taking into account interfering inputs that add to the measurement uncertainty, as in a real-world measurement scenario. The results indicate that the proposed model has great robustness and accuracy, as evidenced by the low RMSE values in predicting the reaction forces. This demonstrates the model’s effectiveness in reproducing real-world scenarios, highlighting its potential in the real-time estimation of ground reaction forces in industrial settings. The success of this vision-based virtual sensor model opens new avenues for more robust, accurate, and cost-effective solutions for force estimation, addressing the challenges of uncertainty and the limitations of physical sensor deployment.

The high-temperature gas soft measurement method based on the temperature attenuation patterns of flowing argon gas inside a cavity

The device for generating the airflow temperature signal is employed in aircraft ground simulation. This process involves heating the argon gas by the arc discharge to generate high-temperature gas, which subsequently flows into the cavity and mixes with cold gas to produce the temperature signal. However, the temperature of argon gas at the entrance of the cavity is excessively high, and sensor installation is challenging, making direct measurement difficult. This paper introduces a high-temperature gas soft measurement method based on the temperature attenuation patterns of flowing argon gas inside the cavity, and establishes a mathematical model for the flow and temperature distribution of argon gas within the cavity. The method calculates the target temperature at the entrance of the cavity by measuring the lower-temperature argon gas at the outlet of the cavity. Furthermore, a simulation and experimental platform were set up to validate the proposed method. The experimental results indicate that there is a 7.9% deviation between the soft measurement values and the directly measured values of argon gas temperature in the middle of the cavity. At the outlet of the cavity, the maximum deviation between the directly measured argon gas temperature and the simulation calculation results based on the soft measurement values at the entrance temperature is 5.2%. The paper rigorously validates the accuracy of the soft measurement method from various perspectives. Notably, this method offers the advantage of remote indirect measurement, and indirectly expanding the upper limit of the temperature sensor.

Venous thromboembolism in patients with isolated moderate to severe traumatic brain injury

Background. Traumatic brain injury (TBI) remains a big problem of modern neurosurgery, accompanied by high rates of disability and lethality. Venous thromboembolism (VTE) including venous thrombosis and pulmonary embolism (PE) plays a significant role in the structure of mortality in this pathology. Regimens and schemes of pharmacological prevention of VTE in TBI as well as corresponding preventative measures are not yet determined completely.Aim. To identify the frequency of VTE in patients with isolated moderate and severe TBI, and to evaluate the results of prevention and treatment of these complications in patients of this category.Materials and methods. Over a 3‑year period (from 2020 to 2023) 73 patients with isolated moderate and severe TBI (51 men and 22 women, mean age 61.0 ± 13.0 years) were treated in the V. M. Buyanov City Clinical Hospital. Of these, 31 patients received only conservative therapy, and 42 patients underwent surgery (craniotomy with hematoma removal, closed external hematoma drainage, and installation of intracranial pressure sensor). Ultrasound examination of the lower limb veins was performed at patient admission, then every 7 days until discharge from the hospital. Pharmacological prevention of VTE using low molecular weight heparin in non‑surgical patients was started after 1–2 days if computed tomography of the brain 24 h after admission showed no negative hematoma dynamics. Surgical patients were prescribed preventive medications 24 h after surgery if computed tomography confirmed intracranial hemostasis.Results. Venous thrombosis was detected in 22 (30.1 %) of 73 patients. PE complicated the underlying disease in 1 (1.4 %) case and was non‑fatal. There was no fatal PE in the study group. An increase of the initial intracranial hematoma volume occurred in 3 (4.1 %) patients, in 2 (2.8 %) patients the recurrence of hemorrhage occurred before the beginning of heparin administration, and in 1 (1.4 %) case against the background of therapeutic doses of anticoagulants prescribed for venous thrombosis. In the majority of cases (82.0 %; 18 of 22 patients) thromboses were localized in deep veins of the lower leg and were asymptomatic. Intrahospital mortality was 23.3 % (17 patients), all lethal outcomes were due to the course of traumatic brain disease.Conclusion. VTE is a frequent complication of TBI with intracranial hemorrhage. Regular ultrasound diagnostics makes it possible to diagnose asymptomatic distal venous thrombosis in a timely manner and to prescribe therapeutic doses of low molecular weight heparin in time which in turn allows to avoid fatal PE. Currently, there are no clear domestic recommendations for the prevention and, most importantly, for the treatment of these complications in patients with isolated moderate and severe TBI, which requires further active study of this problem.

Damage Detection in Bridge Structures through Compressed Sensing of Crowdsourced Smartphone Data

Traditional bridge health monitoring methods that necessitate sensor installation are not only costly but also time-consuming. In contrast, utilizing smartphone data collected from vehicles as they traverse bridges offers an efficient and cost-effective alternative. This paper introduces a cutting-edge damage detection framework for indirect monitoring of bridge structures, leveraging a substantial volume of acceleration data collected from smartphones in vehicles passing over the bridge. Our innovative approach addresses the challenge of collecting and transmitting high-frequency data while preserving smartphone battery life and data plans through the integration of compressed sensing (CS) into the crowdsensing-based monitoring framework. CS employs random sampling and signal recovery from a significantly reduced number of samples compared to the requirements of the Nyquist–Shannon sampling theorem. In the proposed framework, acceleration signals from vehicles are initially acquired using smartphone sensors, undergo compression, and are then transmitted for signal reconstruction. Subsequently, feature extraction and dimensionality reduction are performed using Mel-frequency cepstral coefficients and principal component analysis. Damage indexes are computed based on the dissimilarity between probability distribution functions utilizing the Wasserstein distance metric. The efficacy of the proposed methodology in bridge monitoring has been substantiated through the utilization of numerical models and a lab-scale bridge. Furthermore, the feasibility of implementing the framework in a real-world application has been investigated, leveraging the smartphone data from 102 vehicle trips on the Golden Gate Bridge. The results demonstrate that damage detection using the reconstructed signals obtained through compressed sensing achieves comparable performance to that obtained with the original data sampled at the Nyquist measurement sampling rate. However, it is observed that to retain severity information within the signals for accurate damage severity identification, the compression level should be limited to 20%. These findings affirm that compressed sensing significantly reduces the data collection requirements for crowdsensing-based monitoring applications, without compromising the accuracy of damage detection while preserving essential damage-sensitive information within the dataset.

Monitoring For Strain-Based Railway Structure Assessment Approach Using Optical Fibre Sensor

Railway transportation is essential and continually evolving with new technologies to achieve high efficiency and passenger safety. Monitoring railways can be challenging, especially when dealing with delicate equipment. This study outlines a basic guideline and real-time monitoring process for railway tracks, serving as precautionary measures. The research employs Fiber Bragg Grating (FBG) sensors directly attached to the railway track’s web surface, which have the potential to cause failures. Before sensor installation, several critical steps were taken to ensure the fragile FBG sensors remained intact. These steps involved preparing sensor arrangements and positioning them before installation. Additionally, a calibration test for FBG is performed using the Universal Tensile Machine (UTM) with a mild steel sample, achieving a measurement of almost 1.2 picometers/micro-strain. As a result, a shift in wavelength was successfully measured and collected, enabling the calculation of strain-stress data for the rail structure during operation. The collected data exhibited no irregularities or anomalies data, indicating the success of the monitoring process. The data obtained from a well-prepared installation greatly differs from that of an underprepared one. This study underscores the necessity of thorough preparation when handling delicate equipment like FBG sensor. Therefore, the FBG sensor has the capability to measure the strain-stress behavior of rail structures, allowing for more accurate predictive assessments to prevent rail failures.

Research on Calibration Method of Six Dimensional Force Sensor at the End of Robot

A six-axis force sensor calibration method for industrial robots is proposed to meet the demand for precise force measurement of end effectors in industrial robots. Firstly, establish a sensor data relationship calculation model, and based on this, focus on considering the interference caused by sensor "zero drift"; Then, the least squares method is used to determine the inclination angle between the robot's end effector and the installation of the six-axis force sensor, the gravity of the end effector, and the coordinate parameters of the center of gravity. Finally, a CR5 robotic arm experimental platform was built to verify the feasibility of the algorithm. The experiment showed that the calibrated sensor maintained a stable force value of 0N without external force, verifying the accuracy of the calibration algorithm.

Error Analysis of Normal Surface Measurements Based on Multiple Laser Displacement Sensors.

The robotic drilling of assembly holes is a crucial process in aerospace manufacturing, in which measuring the normal of the workpiece surface is a key step to guide the robot to the correct pose and guarantee the perpendicularity of the hole axis. Multiple laser displacement sensors can be used to satisfy the portable and in-site measurement requirements, but there is still a lack of accurate analysis and layout design. In this paper, a simplified parametric method is proposed for multi-sensor normal measurement devices with a symmetrical layout, using three parameters: the sensor number, the laser beam slant angle, and the laser spot distribution radius. A normal measurement error distribution simulation method considering the random sensor errors is proposed. The measurement error distribution laws at different sensor numbers, the laser beam slant angle, and the laser spot distribution radius are revealed as a pyramid-like region. The influential factors on normal measurement accuracy, such as sensor accuracy, quantity and installation position, are analyzed by a simulation and verified experimentally on a five-axis precision machine tool. The results show that increasing the laser beam slant angle and laser spot distribution radius significantly reduces the normal measurement errors. With the laser beam slant angle ≥15° and the laser spot distribution radius ≥19 mm, the normal measurement error falls below 0.05°, ensuring normal accuracy in robotic drilling.

Research on monitoring model for maize entrainment loss sensor applicable to different operating parameters

The entrainment loss monitoring sensor plays a very important role in the intelligent development process of maize combine harvester. However, traditional entrainment loss sensor does not adjust its monitoring ratio according to different working conditions of the harvester, resulting in poor adaptability of the sensor to different working environments. To address the above problems and to improve the applicability of the entrainment loss monitoring sensor, this study proposed a maize entrainment loss sensor monitoring model based on the optimal sensor installation location. The distribution law of threshed material below the threshing drum was investigated, and the optimal installation location of the two symmetrical sensors was determined to be x = 1.7 m along the axial direction of the drum and r1 = 0.1 m and r2 = 0.7 m along the radial direction of the drum. Based on the above, the threshing process was simulated and analyzed using the discrete element method (DEM). A proportional model between the monitoring values and the true values of entrainment loss under different working parameters was established using multiple linear regression algorithms, and the determination coefficients (R2) of the sensor monitoring model on both sides was 0.813 and 0.876, respectively. Finally, the proposed monitoring models were validated through tests, the results show that the accuracy of the entrainment loss sensors can reach above 85 % under different working parameter conditions of the threshing system. The research findings of this paper are of great significance to improve the applicability of maize entrainment loss sensors, which can provide reference for the intelligent control system of combine harvester and provide technical support to enhance maize yield.

Post-tensioning tendon force estimation of in-service prestressed concrete structure using cylindrical lamb waves

This paper proposes an ultrasonic-based force estimation technique for the post-tensioning (PT) tendon of an in-service prestressed concrete structure. First, three macro fiber composite transducers are installed on the surface of an in-service PT tendon subjected to an unknown tensile force for the generation and measurement of cylindrical Lamb waves. Then, the velocities of the longitudinal and shear waves are calculated using the measured cylindrical Lamb waves at two ultrasonic input frequencies. Subsequently, the Poisson’s ratio, elastic modulus, and strain induced by the PT tendon force are estimated from the longitudinal and shear wave velocities. Finally, the PT tendon force is obtained from the estimated mechanical property values. The experimental validation was performed using a mono-strand PT tendon under various temperature conditions, and the developed technique estimated the unknown PT tendon force in the range of 10–100 kN with a 6.22 kN maximum error and a 2.99 kN root-mean-square error. The uniqueness of this paper includes (1) no requirement for temperature compensation for field deployment, (2) PT tendon force estimation without calibration of the PT tendon force and cylindrical Lamb wave relationship, and (3) easy sensor installation and no interference with the PT construction process.

Classification of set-net fish catch volumes in Iwate Prefecture, Japan using machine learning with water temperature and current distribution images at migration depth

Efficient management of set-net fisheries can be achieved by predicting fish catches in advance. We aimed to estimate fish catch volumes using machine learning based on the data obtained from past set-net fisheries. We also suggested that the estimation can be conducted without the installation of special sensors, such as fish finders, sonars, and cameras, in the set nets. Therefore, a special feature of this study is the construction of a machine learning model using marine environmental images around the set net. In particular, the distribution images of water temperature and current at a certain water depth were applied to the model. In set-nets where migratory fish are caught, ocean data related to migration routes can be useful for estimation. The images used in this study included the water temperature at a depth of 100 m, absolute velocity at a depth of 50 m, and sea surface temperature for comparison. We then classified the fish catch into three classes as follows: chum salmon, yellowtail, and Japanese common squid in Iwate Prefecture, Japan. Consequently, our results achieved an accuracy of approximately 70–80% compared with the validation and test dataset with the water temperature at depths of 100 m. They showed higher accuracy than the sea surface temperature and absolute velocity at a depth of 50 m. This study indicates the usefulness of image-based classification for predicting the volumes of fish caught by set nets.

Исследование способа измерения артериального давления ёмкостным интегрированным датчиком

A method of wireless measurement of blood flow parameters is investigated using an implantable passive capacitive sensor and an external reader. This device is designed to measure the parameters of blood flow in the human body, through the installation of a passive capacitive sensor and inductive coupling with an external reader device. The method of wireless measurement is based on the connection of resonant circuits at an operating frequency of 10 MHz. The mathematical calculation of the device circuit is presented. The result of the calculation was the dependence of the output voltage of the external reader on the change of capacitance in the implanted passive sensor. The values of the potential sensitivity of the device are obtained. The manufactured layout of the device and its parameters are presented. The mock-up allowed to investigate the dependences of the coupling factor of the circuits when using coils of an external reader of different sizes. Also the measurement of the output voltage of the external reader from the value of the passive sensor capacitance was made. The value of real sensitivity of the device layout was measured.

A novel microwave Doppler method for mass estimation of machine-picked seed cotton

Obtaining information on cotton yields and creating a spatial map of cotton production is the starting point for implementing precision agriculture. Microwave technology has been used for yield estimation in China for crops such as corn and soybeans and has shown strong applicability. However, research on cotton yield estimation using microwave technology is weak. Therefore, this paper proposes a method for seed cotton mass estimation based on the microwave Doppler method and designs bench tests to prove the feasibility of the method. First, a seed cotton speed estimation model was established based on the microwave Doppler effect and fast Fourier transform. On this basis, a seed cotton velocity estimation experiment was designed to study the effects of sensor installation angle and position, pipe collision friction, air resistance, and the interaction force between the seed cotton on the seed cotton movement velocity. The power spectral density (PSD) of the acquired microwave data is then estimated using the modified periodogram method. The least squares method was used to establish a linear regression model between seed cotton mass and the corresponding PSD, and finally the free-fall mass experiment and pneumatic conveying mass experiment yielded that the estimation errors of seed cotton mass were 7.30% and 6.17%, respectively. The experimental results show that the estimation error is small when using microwave technology for seed cotton mass estimation, which meets the requirements of actual operation.

The Impact of Diabetes Education on Continuous Glucose Monitoring in SUS-Dependent Patients in a Northeastern Brazilian City.

Diabetes Mellitus (DM) is an important chronic disease that occurs worldwide. This study aims to investigate how the use of the FreeStyle® Libre system in Unified Health System (SUS) patients impacts diabetes parameters in patients who receive education on proper insulin administration and the use of the continuous monitoring device, as well as how this affects patients without any concomitant multidisciplinary support in Sergipe, Brazil. We conducted a prospective randomized study in a diabetes clinic in Sergipe, Brazil, using the flash method FreeStyle® Libre (Abbott). The participants were divided into two groups: one receiving diabetes education on CGM (continuous glucose monitoring), while the other did not. Before the intervention, the patient's treatment motivation and quality of life were assessed using a questionnaire, and baseline levels of glycated hemoglobin were measured using high-performance liquid chromatography (HPLC) and the point of care AlereTM Afinion with boronate fixation. We compared first- and second-phase data with respect to glycated hemoglobin, mean interstitial blood glucose, time on and above target for hypoglycemic and hyperglycemic events, and mean hypoglycemic duration. In group A, which received the diabetes education intervention, there was a significant reduction in average HbA1c levels from 8.6% to 7.9% after 3 months (p = 0.001). However, there was no significant difference in average glycemic values. Time above target decreased significantly from 50.62% to 29.43% (p = 0.0001), while time below target decreased from 22.90% to 20.21% (p = 0.002). There was no significant change in the number of hypoglycemic events, but the duration of hypoglycemia decreased significantly from 130.35 min to 121.18 min after 3 months (p = 0.0001). In Group B, there was no significant difference in mean HbA1c levels before (7.07%) and after (7.28%) sensor installation. This group maintained lower HbA1c levels compared to the other group. Average blood glucose levels also remained similar before (148.37 mg/dL) and after (154.65 mg/dL) the intervention. Although the time above the target glucose level increased significantly from 35.94% to 48.17%, the time at target decreased from 50.40% to 37.97%. No significant changes were observed in the time below target, the number of hypoglycemic events, or the duration of hypoglycemia. Our findings indicate that utilizing continuous glucose monitoring technology can enhance glycemic control, particularly in motivated, educated, low-income patients dependent on the SUS. To achieve positive results with FreeStyle Libre, it is imperative to allocate resources for multidisciplinary support.

Implementasi Sistem Irigasi Smart Garden IoT pada Perkebunan Stroberi

In this research, the Smart Garden IoT system is implemented to enhance the efficiency of strawberry plant management, utilizing Ds18B20 temperature sensor, soil moisture sensor, water pH sensor, and a Flowmeter. Real-time data will be transmitted to the Blynk software for analysis. The research methodology involves the installation of sensors and the use of an ESP32 microcontroller in the strawberry cultivation area. The objectives of the study include improving monitoring and control, as well as supporting timely and efficient irrigation and fertilization of strawberry plants. This smart garden system operates according to predefined time settings and is also adjusted based on soil moisture sensor readings. The watering schedule is set for morning, afternoon, and evening. However, if the soil moisture remains around 80-90%, watering will be withheld. The research conclusion supports the concept of Smart Garden as an innovative solution to enhance efficiency in plant care through IoT technology