Accuracy Of Sensor Research Articles

Adaptive temperature compensation for [formula omitted] humidity sensor in complex environments using ISSA-BP neural network

High-precision humidity detection in complex environments is essential across various fields. In this study, a high-performance MoS2 humidity sensor with a dynamic response time of less than 3 s was developed using molten salt-assisted chemical vapor deposition. To address the challenges posed by dynamic ambient temperature changes on sensor accuracy, an improved sparrow search algorithm-back propagation (ISSA-BP) neural network was constructed to mitigate temperature drift and correct nonlinear errors in the sensor output. The ISSA-BP neural network utilizes a global optimization strategy with adaptive learning, significantly enhancing accuracy and efficiency by optimizing the initialization and iterative update processes of traditional algorithms. Experimental results indicate that the proposed ISSA-BP achieves an average relative error of just 0.75% in humidity sensors across various environmental conditions, representing a 5.8-fold improvement in accuracy compared to traditional methods. Additionally, the algorithm demonstrated high robustness and accuracy across different environments, sensors, and datasets, confirming its applicability in complex and variable scenarios.

Cu-Ag nanoparticles-dispersed and microchannel textured-laser induced graphene: A chemiresistive aptasensor for thrombin

Thrombin is a key enzyme of the clotting cascade, which can convert soluble fibrinogen to insoluble fibrin. A rapid and accurate sensor is required to give easy access to patients and healthy individuals to monitor their thrombin levels. In this study, a Cu and Ag bimetal nanoparticles (NPs)-dispersed and microchannel textured-laser induced graphene (Cu-Ag-LIG)-based chemiresistive aptasensor is developed. The sensor substrate was synthesized via the suspension polymerization of phenol and formaldehyde, with the in situ inclusion of the metal salts in the reaction mixture. Laser ablation converted the carbon containing polymeric groups to sp2 hybridized graphitic carbon and the metal salts to zero-valent metal NPs. Laser ablation was also used to create microchannels on the graphene surface to ensure the uniform distribution of the analyte. Ag NPs facilitated the binding of thrombin binding aptamer (TBA) via covalent bond, while Cu NPs provided structural support. The aptasensor showed a linear response over the 0.1–50 units/mL-thrombin concentration range. The sensor response was unaffected by common interfering biomolecules such as glucose, bovine serum albumin, urea, trypsin, and cholesterol. This study showcases the broader and often overlooked significance of chemiresistive sensors not only in healthcare but also across various scientific domains, offering avenues for transformative advancements in diagnostic technologies.

A novel bulk magnetostrictive sensor for bending applications

In this paper, a bulk magnetostrictive material is used to measure bending forces. It is made of a cylindrical vanadium permendur alloy. In this sensor, two comb shape magnetic cores and excitation coils are used to create magnetic fields in the material’s surface layer. When bending force is applied to the material, created stresses causes a change in the magnetic flux density on the surface of the material. Induced voltage changes in the pickup coils are used as the output of the sensor. The range of the sensor is up to 280 Newtons. The accuracy of the bending sensor is ±5.1%FS. The bending force measurement sensitivity of the sensor is 7.5mV/N. By performing combined loading tests, it was found that the sensor is able to detect bending force in both directions. The outputs of the two directions are decoupled and only 4.7 % is affected by each other.

High-throughput phenotypic traits estimation of faba bean based on machine learning and drone-based multimodal data

Faba bean is a global food legume crop, and it is essential to accurately and timely determine its plant height, above-ground biomass (fresh and dry weight) and yield for enhancing cultivation practices and planning the next planting season. Traditional ground sampling is a time-consuming and labor-intensive approach. However, the utilization of an unmanned aerial vehicle (UAV) as a high-throughput technique offers a promising alternative strategy for estimating crop phenotypic traits. In this study, a two-year experiment was conducted from 2020 to 2022, where UAV-based multimodal data were collected using red–green–blue, multispectral and thermal infrared sensors. The variables derived from these three sensors and their combinations were used to estimate the fresh weight, dry weight and yield of faba bean based on extreme gradient boosting (XGBoost), random forest, multiple linear regression and k-nearest neighbor algorithms. The following findings were obtained: (1) The use of the maximum percentile crop surface model resulted in the highest estimation accuracy for faba bean plant height. (2) Fusion data from multiple sensors increased the estimation accuracy of faba bean fresh weight, dry weight and yield, the coefficient of determination (R2) improved by 14.22%, 1.45%, and 18.76%, respectively, compared with the best estimation accuracy of a single sensor. (3) The XGBoost algorithm outperformed the other algorithms in estimating fresh weight, dry weight and yield of faba bean. These results demonstrate that multiple sensors and appropriate algorithms can be used to effectively estimate faba bean phenotypic traits and provide valuable insights for agricultural remote sensing research.

Performing hardness classification using diffusive memristor based artificial neurons

Abstract Artificial neurons and synapses are the building blocks for constructing a neuromorphic system such as Spiking Neural Network (SNN) or Artificial Neural Network (ANN). Recently, there has been tremendous interest in using memristors to develop neuromorphic technologies that can be used in advanced SNNs and ANNs. Memristors, because of their simple device structure, easy and high-density fabrication, and integration with other semiconductor electronics are suitable candidates for the construction of neuromorphic concepts. However, not much has been discussed about using memristors for the development of sensors that can be utilized for object- classification especially their rigidity, shape and structure. In this article, we propose the application of memristors, specifically silver nanoparticle based diffusive memristor, in conjunction with a piezoelectric sensor within a robotics gripper, serving as one receptor (a tactile sensor) that triggers neuron circuitry with memristors to generate spikes. Furthermore, to perform hardness classification, we utilized various objects to collect data and generated multiple spikes corresponding to each object. This data was then utilized with a machine learning algorithm. The outcomes were compared with the accuracy of commercial FSR tactile sensors. Our approach demonstrated the capability of diffusive memristors in generating neuron spikes from tactile stimuli for hardness classification, achieving accuracy ranging from 82% to 100% during the validation of 20% test data across various algorithms, while the FSR sensors achieved an accuracy range of 95% to 98%.

Wavelength Locking and Calibration of Fiber-Optic Ultrasonic Sensors Using Single-Sideband-Modulated Laser

Implementation of edge-filter detection for interrogating optical interferometric ultrasonic sensors is often hindered by the lack of cost-effective laser sources with agile wavelength tunability and good noise performance. The detected signal can also be affected by optical power variations and locking-point drift, negatively affecting the sensor accuracy. Here, we report the use of laser single-sideband generation with a dual-parallel Mach–Zehnder interferometer (DP-MZI) for laser wavelength tuning and locking in edge-filter detection of fiber-optic ultrasonic sensors. We also demonstrate real-time in situ calibration of the sensor response to ultrasound-induced wavelength shift tuning. The DP-MZI is employed to generate a known wavelength modulation of the laser, whose response is used to gauge the sensor response to the ultrasound-induced wavelength shifts in real time and in situ. Experiments were performed on a fiber-optic ultrasonic sensor based on a high-finesse Fabry–Perot interferometer formed by two fiber Bragg gratings. The results demonstrated the effectiveness of the laser locking against laser wavelength drift and temperature variations and the effectiveness of the calibration method against optical power variations and locking-point drift. These techniques can enhance the operational robustness and increase the measurement accuracy of optical ultrasonic sensors.

Resonance-State Temperature Compensation Method for Ultrasonic Resonance Wind Speed and Direction Sensors

To achieve high-precision wind speed and direction measurements in complex environments, a resonance-state temperature compensation method is proposed based on an ultrasonic resonance principle. This method effectively addresses the issue of sound velocity compensation errors caused by the temperature difference between the internal and external environments when using an internal temperature sensor for temperature compensation. By utilizing an adaptive resonance-state tracking model, the resonance frequency shift issues under varying conditions such as altitude, pressure, and temperature are mitigated. This approach ensures that the resonance frequency is strongly correlated with temperature, enabling temperature compensation through resonance frequency alone, without the need for a temperature sensor. The experimental results indicate that the resonance frequency variation rate with temperature for the resonance-state temperature-compensated ultrasonic resonance wind speed and direction sensor is approximately 0.08 kHz/°C. The wind speed measurement accuracy is ±0.3 m/s (≤15 m/s)/±2.3% (15 m/s~50 m/s), which is superior to the measurement accuracy of traditional ultrasonic wind speed and direction sensors (±0.5 m/s (≤15 m/s)/±4% (15 m/s~50 m/s)). The consistency of wind speed measurement is ≤±0.3%, representing an improvement of approximately 3% compared to ultrasonic resonance wind speed and direction sensors without resonance-state temperature compensation.

Deep Learning-Enabled De-Noising of Fiber Bragg Grating-Based Glucose Sensor: Improving Sensing Accuracy of Experimental Data

This paper outlines the successful utilization of deep learning (DL) techniques to elevate data quality for assessing Au-TFBG (tilted fiber Bragg grating) sensor performance. Our approach involves a well-structured DL-assisted framework integrating a hierarchical composite attention mechanism. In order to mitigate high variability in experimental data, we initially employ seasonal decomposition using moving averages (SDMA) statistical models to filter out redundant data points. Subsequently, sequential DL models extrapolate the normalized transmittance (Tn) vs. wavelength spectra, which showcases promising results through our SpecExLSTM model. Furthermore, we introduce the AttentiveSpecExLSTM model, integrating a composite attention mechanism to improve Tn sequence prediction accuracy. Evaluation metrics demonstrate its superior performance, including a root mean square error of 1.73 ± 0.05, a mean absolute error of 1.20 ± 0.04, and a symmetric mean absolute percentage error of 2.22 ± 0.05, among others. Additionally, our novel minima difference (Min. Dif.) metric achieves a value of 1.08 ± 0.46, quantifying wavelength for the global minima within the Tn sequence. The composite attention mechanism in the AttentiveSpecExLSTM adeptly captures both high-level and low-level dependencies, refining the model’s comprehension and guiding informed decisions. Hierarchical dot and additive attention within this model enable nuanced attention refinement across model layers; dot attention focuses on high-level dependencies, while additive attention fine-tunes its focus on low-level dependencies within the sequence. This innovative strategy enables accurate estimation of the spectral width (full-width half maxima) of the Tn curve, surpassing raw data’s capabilities. These findings significantly contribute to data quality enhancement and sensor performance analysis. Insights from this study hold promise for future sensor applications, enhancing sensitivity and accuracy by improving experimental data quality and sensor performance assessment.

Ultra Enhancement of the Resolution of the Divergence Beam-Based SPR Sensor Using a Wavelet Filter

Surface Plasmon Resonance (SPR) sensors are highly sensitive to refractive index variations, making them ideal for biosensing and chemical detection applications. However, their performance can be constrained by noise, resolution limits, and signal distortions, particularly in divergence beam-based configurations. This research presents an ultra-enhancement of the performance of divergence beam-based SPR sensors by employing advanced wavelet filtering techniques. Wavelets, with their multi-resolution analysis capability, are applied to denoise and refine the sensor signal, significantly improving key performance metrics, including resolution, mean square error (MSE), root mean square error (RMSE), signal-to-noise ratio (SNR), sensitivity matrix (SM), and combined sensitivity factor (CSF).The wavelet filter effectively decomposes the SPR signal into distinct frequency bands, separating noise while retaining high-frequency components required for accurate sensing. This results in a significant reduction in MSE and RMSE of 30 and 92.26%, respectively, while simultaneously improving SNR by 54.08%, maintaining signal quality. The wavelet filter significantly improved the resolution of the SPR sensor by 99.95% (1.3772×10-7 RIU), allowing for more precise detection of refractive index changes and boosting diverge beam-based SPR sensor performance. In addition, a sensitivity matrix (SM) and combined sensitivity factor (CSF) were improved by 42 and 41.94%, respectively, allowing for a more thorough evaluation of the sensor's performance across multiple operational parameters. Simulation and experimentation show that wavelet filtering surpasses standard filtering approaches in terms of noise suppression and signal clarity. This technology has considerable potential for improving the accuracy and reliability of SPR sensors in high-sensitivity applications

Segmented Two-Dimensional Progressive Polynomial Calibration Method for Nonlinear Sensors.

Nonlinearity in sensor measurements reduces the sensor's accuracy. Therefore, accurate calibration is necessary for reliable sensor operation. This study proposes a segmented calibration method that divides the input range into multiple sections and calculates the optimized calibration functions for each one. This approach reduces the overall error rate and improves the calibration accuracy by isolating distinctive regions. The modified progressive polynomial calibration technique is used to calculate the calibration function. This algorithm addresses the computational complexity, allowing for reduced polynomial degrees and improving the accuracy. The segmented calibration method achieves a significantly lower error rate of 0.000006% compared to the original single calibration method, which has an error rate of 0.0823%, when using the same six calibration points and a fifth-degree polynomial function. This method maintains improved accuracy with fewer calibration points, and its ability to reduce the computational complexity and calculation time while using lower polynomial degrees is confirmed. Additionally, it can be extended to two dimensions to reduce the errors caused by cross-sensitivity. The results from a two-dimensional simulation show a reduction in the error rate ranging from 15.84% to 2.07% in an 8-bit signed fixed-point system. These results indicate that the segmented calibration method is an effective and scalable solution for various typical sensors.

Optimization and effect comparison of typical gas pressure compensation model in chemical industry park

In chemical parks, the leakage of harmful gases can lead to poisoning and even explosions. Therefore, its monitoring and leakage warnings are crucial. This paper takes the harmful gases CO2, CH4, and CO as examples, and set up an infrared gas sensor pressure compensation experimental platform to carry out experiments, to solve the problem of decreased detection accuracy of gas sensors due to changes in ambient pressure during detection. The pressure compensation experimental ranges of the gas sensors are 0 ∼ 5 %, 0 ∼ 20 %, and 0 ∼ 1000 ppm, and the maximum absolute errors of the infrared gas test data obtained under different concentrations and pressures are 0.24 ∼ 0.67, 0.89 ∼ 1.12, and 45 ∼ 60 ppm, respectively. The pressure compensation model based on the least squares method was constructed, and the maximum absolute errors were obtained as 0.08 ∼ 0.19, 0.13 ∼ 0.64, and 24 ∼ 37 ppm, respectively. The pressure compensation model based on the GA-BP neural network was constructed, and the maximum absolute errors were 0.04 ∼ 0.10, 0.08 ∼ 0.10, and 0.60 ∼ 8.30 ppm, respectively. The GA-BP neural network combines the genetic algorithm and the backpropagation algorithm, which can better deal with nonlinear problems. The comparison of these two models reflects the superiority of the GA-BP neural network model in the compensation effect. The establishment of the neural network pressure compensation model optimized by the genetic algorithm can effectively improve the detection accuracy of the gas sensor, and it is expected that the results are of great practical significance to guarantee production safety and protect the environment in the enterprise chemical park.

Novel amino-functionalized MOF-based sensor for zinc ion detection in water and blood serum samples

Aquatic systems with low zinc levels can experience a significant decrease in carbon dioxide uptake and limited growth of phytoplankton species. In this study, we describe the use of a new fluorescent sensor based on NH2-MIL-53(Al), and modified with glutaraldehyde and sulfadoxine, for selectively detecting zinc ions in water and blood serum samples. Characterization of the synthesized material was performed using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), Brunauer–Emmett–Teller (BET) surface area analysis, X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM), confirming successful functionalization and preservation of the MOF structure. The sensor’s performance for Zn2+ detection was evaluated by spectrofluorometry, demonstrating a significant fluorescence enhancement upon Zn2+ binding due to the interaction between Zn2+ ions and the sulfonamide groups. With a detection limit as low as 3.14 × 10–2 ppm, the sensor demonstrates high selectivity for Zn2+ over other common metal ions. The sensor’s response is rapid, stable, and reproducible, making it suitable for practical applications. Real sample analysis was conducted in tap water and blood serum samples, with the results compared to those obtained using ICP-OES and a colorimetric test with 5-bromo-PAPS. The comparison confirmed the high accuracy and reliability of the fluorescent sensor in detecting Zn2+ ions in complex matrices. NH2-MIL-53(Al) modified with glutaraldehyde and sulfadoxine shows potential as a selective fluorescent sensor for Zn2+ detection, making it a valuable tool for monitoring the environment and biology.

Improving PM10 sensor accuracy in urban areas through calibration in Timișoara

Low-cost particulate matter sensors (LCS) are vital for improving the spatial and temporal resolution of air quality data, supplementing sparsely placed official monitoring stations. Despite their benefits, LCS readings can be biased due to the physical properties of aerosol particles and device limitations. An optimization model is essential to enhance LCS data accuracy. This paper presents a calibration study of the LCS network of Timișoara, Romania. The calibration began by selecting LCS devices near National Air Quality Monitoring Network (NAQMN) stations and developing parametric models, choosing the best for broader application. Plantower, Sensirion, and Honeywell sensors showed comparable accuracy. Calibration involved clusters within a 750 m radius around NAQMN stations. Models incorporating RH corrections and multiple linear regression (MLR) were fitted. The best model was validated against data from unseen sensors, leading to mean bias errors (MBE) within 9-17% and RMSEs of 33-35%, within sensor uncertainty margins. Applied to the city-wide LCS network, the model identified several stations regularly exceeding the EU daily PM10 threshold, unnoticed by NAQMN stations due to their limited coverage. The study highlights the necessity of granular monitoring to accurately capture urban air quality variations.

Sampling volume evaluation of seven electromagnetic soil moisture sensors

The sampling volume of electromagnetic (EM) soil moisture sensors is of significant interest due to the diverse applications for which they are utilized. However, this volume can vary considerably based on multiple factors, including the number, spacing, and length of sensor rods, as well as the operational frequency. The sensor's volume of influence is dictated by electric field lines or ‘fringing fields’ that develop between pairs of powered electrodes. This study involved the development of an accurate sensor positioning system employed to characterize the EM sensor's electrical fringing field in 3-directional - x, y, and z space. Sensor outputs were recorded at mm increments beginning with the sensor positioned 20 mm above the air-water interface followed by incremental immersion into a water bath until the maximum sensor output was achieved. For each measurement cycle, the sensor was repositioned to test signal output approaching the air-water interface in −x, +x, −y, +y, −z, and +z orientations. The sensor sampling volume was determined based on the normalized 99% output value, which was assumed to represent the sensor position relative to an air-water interface where the sensor fringing field just begins to ‘sense’ or to become impacted by the air phase, thereby reducing the signal amplitude. Our results vary somewhat from other studies using a similar evaluation approach, leading us to conclude that EM-sensor sampling volume determination is both complex and imprecise as a result of the global sensor market's varied circuits and electrode configurations. EM soil moisture sensors evaluated in this study exhibited sampling volumes of 386 cm3 (TDR-315), 66 cm3 (TDR-310H), 398 cm3 (CS655), 45 cm3 (EC-5), 40 cm3 (HydraProbe II), 148 cm3 (WET150), and 49 cm3 (TEROS 12), which were smaller than manufacturer reported volumes.

Enhancing Accuracy of Nanocomposite Hydrogen Sensors in Various Environmental Situations through Machine Learning

Enhancing Accuracy of Nanocomposite Hydrogen Sensors in Various Environmental Situations through Machine Learning

Identification of characteristics of conceptual prototype of microprocessor resource-saving relay protection system

The object of the study is the conceptual prototype of a microprocessor resource-saving relay protection system. Currently, relay protection ensures electrical networks reliable and efficient work, however, the traditional architecture is proprietary, not allowing to fix and replace damaged parts without a company specialist. Therefore, the open-architecture relay protection is a very pressing issue, but the problem lies in meeting the relay protection requirements. The data transmission protocols nRF and ESP-NOW, Hall sensors evaluation for AC current measurement determination and sensor accuracy improvement was implemented. Experimental validation demonstrated that nRF and ESP-NOW protocols meet the delay and reliability requirements, however, the nRF protocol is more suitable due to its flexibility and obstacle penetration. The data demonstrated that the most effective conditions are without obstacles at 15 meters from the modem and with obstacles 5 meters from the modem. The experiment of Hall sensors characteristics determination demonstrated the accuracy of current measurement with the set values of the opening and closing currents. Nevertheless, it is not accurate (12.45 %) for the relay protection application. Therefore, the application of the changing values of the opening and closing currents is more effective and accuracy reaches 6.92 %. As a result, the service life of the Hall sensor was determined, and even after 10 million openings, the open state time remained unchanged. Therefore, the approximation function for current amplitude determination depending on open state time was found. On the other hand, Hall sensors may suffer from temperature drift and require further optimization to be fully reliable. The study limitation is the current range from 0 to 800 A

Transforming animal tracking frameworks using wireless sensors and machine learning algorithms

Conventional animal tracking systems such as physical human observation, animal ear tagging or notching raises serious concerns over the observation and animal handling techniques that may sometimes cause stress and disruptions to animal ecology. Wireless sensor networks on the other hand hold real promise for animal tracking due to their accuracy, scalability, and ethical consideration frameworks involved. To test machine learning algorithms in a wireless sensor framework, a simulation was carried out to illustrate the behavior of a Wireless sensor network to draw conclusions. Advanced data algorithms and Python features was adopted to emulate the behavior of a wireless sensor network from cattle datasets sourced from the repository of Ireland’s government Department of Agriculture, Food and Marine which contains 3,503 records of cattle in various areas in Europe. The capacities of different algorithms for location estimation and assessment of performance were also analyzed and the results demonstrates great potentials of a WSN for efficiency in farm monitoring, where parameters such as location and sensor accuracy can be monitored in real time.

Development of an Angular Stiffness Sensor to Measure Dental Implant Stability In Vitro.

This investigation aims to develop an angular stiffness sensor intended for measuring dental implant stability in bone. The sensor hardware included a tiny eccentric motor and an accelerometer to measure a flex constant of an implant with its abutment. The sensor software included a mechanics-based model to convert the flex constant to angular stiffness at the implant/abutment junction to indicate the stability. The sensor's accuracy and effectiveness are demonstrated through use of Sawbones slab models that mimic a mandibular premolar section. The models include a Branemark Mk III implant inserted into Sawbones slabs of 5 different densities with a locator abutment. An incremental insertion torque was first recorded while the implant was placed in the Sawbones models. Then benchtop experiments were conducted to measure resonance frequencies and angular stiffness. Results indicated that angular stiffness increased with Sawbones density, showing high correlation with the measured resonance frequency (R=0.977) and the incremental insertion torque (R=0.959). Finally, accuracy of the angular stiffness sensor is calibrated in light of the resonance frequency. Angular stiffness scores 99% and 95% accuracy for Sawbones models mimicking medium cancellous bones with and without a cortical layer, respectively.

Investigation on Effect of Nonlinear Parameters in torque accuracy of Permanent Magnet Synchronous Machines

EVs and PHEVs are mainly driven by inverter control topologies which will be driving permanent magnet synchronous machines. PMSM is widely used for high drive performance. However, due to parameter variations high torque accuracy is not achieved. The performance of the PMSM drives is widely affected which is caused mainly due to environmental factors and aging. This would include component tolerance, input signal tolerance, machine parameters, software algorithms, sensor accuracy and calibration. It is important to understand these factors and make the tuning throughout the lifecycle since it may show performance deviations over the time and may lead to field complaints. As a step-by-step procedure, the factors which affects the torque accuracy are identified as part of the literature review. This is given as input to a cause effect relationship model which could demonstrate the deviation points. Simulation is performed by varying the operating points at different regions in the e-drive system modeled in Matlab/Simulink. Simulation results are used with different operating points to find out the influential parameters contributing to the deviation. The outcomes derived from the simulations are analyzed and dominant factors are determined. It is observed that this behavior will vary according to the changes in speed and torque. Correlation coefficient is derived from the statistical analysis. The future scope will be applying the optimization techniques and verification of improvements.

Design and development of machine vision robotic arm for vegetable crops in hydroponics

Modern agricultural machinery such as harvesting robotic arms are necessary to optimize the semi-automated agricultural processes to meet the ever-growing food needs of the population of the world. Production of low cost and highly efficient robotic arms are needed to meet the needs of the small-scale growers of underdeveloped countries. In this regard, mechanical design, sensor placement, and object identification are essential to optimize the performance of agricultural robots. This research is conducted to design a fully automated robotic arm for fruit picking in hydroponics system and research farm using machine vision to help the smallholders. For this purpose, a robotic arm with 1.3 m in height is designed using aluminum (18-gauge) and mild steel (MS-16) as fabrication material for the mechanical structure. The prototype design is tested for stress estimation at various payloads using SolidWorks simulations. The proposed robotic arm is based on a four degrees of freedom (4-DoF) design with the gripper having an opening range of 5.5 to 12 cm depending upon the task to handle various objects. The robotic arm is installed on an expandable vertical mobile platform that enables an expanded operational workspace with lower energy consumption. The total weight of the robotic arm is 60 kgs housing a payload capacity ranging between 8 and 10 kgs in accordance with industry-standard load-bearing specifications. The inverse kinematic algorithm using DH-table is computed with an accuracy of up to 95 % and target height of gripper is cross checked by mounting the ultrasonic sensor at the base of robotic arm. The YOLOv8 algorithm is used for object detection using the depth sense camera having angle range of 0.3 m to 3 m with the precision of up to 96 %. The time needed for recognition of fruits and pitching was about 15 s, with a success rate of up to 90 %. This research produces a low-cost and efficient solution for stallholders by developing a fully automated robotic arm for fruit picking. It optimizes agricultural productivity through improved mechanical design, sensor accuracy, and object detection which reduces labor costs by increasing efficiency of semi-automated farming systems. Further research is required to optimize the overall weight of the system and automation of the movement of robotic arm system between the consecutive rows to build a fully autonomous fruit picking operation in hydroponics.