Sensor Channels Research Articles

MSE-TCN: Multi-scale temporal convolutional network with channel attention for open-set gas classification

Currently, researches on closed-set gas classification tasks has achieved great success in the electronic nose (E-nose) field. E-nose, however, faces a more challenging and realistic task in open-set gas classification. To find an accurate open-set gas classification model, we proposed a MSE-TCN, which integrates squeeze-and-excitation residual network (SE-ResNet) internally into the temporal convolutional network (TCN) and expands the channels number of TCN, forming a multi-scale feature extraction encoder, and realizes open-set gas classification using the OpenMax algorithm. The underlying SE-ResNet module focuses on the relatively important sensor channels, while Multi-scale TCN thoroughly captures temporal relationships between the E-nose data from three scales, and the OpenMax identifies the unknown classes by redistributing the probability. The gas sensing performance of the sensors used in the self-sampling dataset was analyzed, demonstrating the reliability of the data acquisition. Subsequently, comparative experiments based on self-sampling dataset and public dataset were performed to determine the number of encoders and demonstrate the necessity of the SE-ResNet module. Meanwhile, ablation experiments demonstrate the effectiveness of our proposed model. In addition, the comparative experiments of the open-set classifiers show MSE-TCN achieves the highest accuracies of 0.9245 and 0.9387 among all the models on both datasets for open-set classification, respectively. As a result, this model provides an effective method for high accuracy gas classification for both closed-set and open-set in E-nose field.

A Wireless and Wearable Multimodal Sensor to Non-Invasively Monitor Transabdominal Placental Oxygen Saturation and Maternal Physiological Signals.

Poor placental development and placental defects can lead to adverse pregnancy outcomes such as pre-eclampsia, fetal growth restriction, and stillbirth. This study introduces two sensors, which use a near-infrared spectroscopy (NIRS) technique to measure placental oxygen saturation transabdominally. The first one, an NIRS sensor, is a wearable device consisting of multiple NIRS channels. The second one, a Multimodal sensor, which is an upgraded version of the NIRS sensor, is a wireless and wearable device, integrating a motion sensor and multiple NIRS channels. A pilot clinical study was conducted to assess the feasibility of the two sensors in measuring transabdominal placental oxygenation in 36 pregnant women (n = 12 for the NIRS sensor and n = 24 for the Multimodal sensor). Among these subjects, 4 participants had an uncomplicated pregnancy, and 32 patients had either maternal pre-existing conditions/complications, neonatal complications, and/or placental pathologic abnormalities. The study results indicate that the patients with maternal complicated conditions (69.5 ± 5.4%), placental pathologic abnormalities (69.4 ± 4.9%), and neonatal complications (68.0 ± 5.1%) had statistically significantly lower transabdominal placental oxygenation levels than those with an uncomplicated pregnancy (76.0 ± 4.4%) (F (3,104) = 6.6, p = 0.0004). Additionally, this study shows the capability of the Multimodal sensor in detecting the maternal heart rate and respiratory rate, fetal movements, and uterine contractions. These findings demonstrate the feasibility of the two sensors in the real-time continuous monitoring of transabdominal placental oxygenation to detect at-risk pregnancies and guide timely clinical interventions, thereby improving pregnancy outcomes.

Asynchronous event‐triggered control for switched T–S fuzzy systems under dual‐channel hybrid cyber attacks

AbstractThis article presents an asynchronous event‐triggered scheme for switched Takagi–Sugeno (T–S) fuzzy systems against dual‐channel hybrid cyber attacks. Different from existing results, both sensor and controller channels are subjected to aperiodic denial‐of‐service attacks and random false data injection attacks. To efficiently utilize dual‐channel network communication resources while resisting hybrid attacks, two resilient event‐triggered mechanisms (ETMs) are constructed. Considering asynchronous ETMs and hybrid cyber attacks, the time‐delay switched T–S fuzzy system is derived by utilizing model transformation methods. Thereby, the stability conditions are derived by utilizing multiple Lyapunov functions technique, and slack matrices are introduced to further relax the conditions. Finally, two examples are given to demonstrate the effectiveness of the developed event‐based security control strategy.

Machine learning approach in multi-channel fiber-optic SPR sensors

Fiber-optic surface plasmon resonance (SPR) sensors have been increasingly used due to their advantages such as compact size, stable physical and chemical properties, high sensitivity, and resistant to electromagnetic interference. In this paper, a dual-channel side-polished fiber-optic SPR sensor system is established to detect the refractive index of liquids and the liquid temperature. High sensitivity of ∼2000 nm/RIU (refractive index unit) and linear sensitivity of ∼−2.0 nm/°C is achieved for two-channel SPR sensors, which are integrated for simultaneously RI temperature sensing and easily scaled to multi-channel SPR sensor array. The resonant wavelengths of the two sensors are located in the range of 600–700 nm and 700–800 nm respectively, which could avoid overlapping the resonance dips. Then, we propose an approach using five machine learning algorithms to predict the resonant wavelength of the second channel of SPR temperature sensor using the normalized transmission spectrum data with a wavelength range of 760–889 nm. After error analysis, it is found that the Categorical Boosting (CatBoost) is the best among five algorithms in terms of accuracy and resolution. The results provide clear evidence that accurate SPR resonant wavelength can be obtained with machine learning approach using a more compact spectrometer.

A cascade SPR sensor based on Ag/Au coated coreless optical fiber for RI and pH measurement

Due to the restriction of resonant wavelength, the detection range of traditional two-parameter sensors is greatly limited. To solve this problem, a cascade SPR sensor with Ag/Au coating is proposed to measure refractive index (RI) and pH value. In this paper, coreless optical fiber is used as the sensor probe, and Ag/Au are coated on its surface by a magnetron sputtering method. The two sensing channels of this cascade sensor are independent of each other and have a wide parameter detection range. The effects of sensor length and coating time on the performance of the sensor were investigated, and the optimal sensor length and coating time were determined. The experimental results show that the maximum refractive index sensitivity is 3888.6 nm /RIU in the RI range of 1.333–1.385, and the maximum pH sensitivity is 38.01 nm/pH in the pH range of 3.15–8.86. The sensor has the advantages of strong stability and high integration and has a good application prospect in the fields of biosensing and environmental detection.

Sparse Tensor Decomposition of Multi-Sensory Data for Fault Localization in Rotating Machinery Health Monitoring

Multi-sensor monitoring is prevalent in modern structural health monitoring (SHM) practice. As the number of sensors and sampling requirements increase, a monitoring sensor network can generate substantial data which are high-volume and high-dimensional, especially for large structure and machinery. In condition-based monitoring (CBM) of rotating machines, e.g., gas turbine, rotor fault diagnosis serves a significant role in the system reliability, safety, and efficiency, which helps reduce potential damage to the on-rotor structures and avoid catastrophic failures. For multi-channel vibration measurement, classical rotor diagnosis approaches typically involve data fusion techniques based on cross spectral analysis for identifying spectral correlation, e.g., cross power spectral density, and/or matrix analysis tool for dimensionality reduction, e.g., principal component analysis. The operation on vectors or matrices may limit their effectiveness for higher-order array or tensor data. To circumvent this limitation, the third order vibrational spectral tensor is generated by representing the multi-channel acceleration spectra as the three-way array. Through nonnegative Tucker decomposition (NTD), the spectral tensor is decomposed into multiple principal factors: the spectral factor, segmental factor, channel-wise factor, and the dense tensor core. The correlations across the characteristic spectral contents and the sensor channels are revealed by the factorization, which enables the diagnosis of rotor fault and facilitates fault localization by identifying the dominant channels of the characteristic spectral factor. The method is validated on an experimental rotor testbed where the faulty channel with crack, rub, or misalignment fault, is effectively localized via 4-channel vibration measurements, which presents a promising approach for multi-sensor fusion and fault diagnosis in rotating machinery health monitoring.

An Optofluidic Young Interferometer for Electrokinetic Transport-Coupled Biosensing.

Label-free optical biosensors, such as interferometers, can provide a comparable limit of detection to widely used enzyme-linked immunosorbent assays while minimizing the number of steps and reducing false positives/negatives. In 2020, the authors reported on a novel optofluidic Young interferometer (YI) that could provide real-time spatial information on refractive index changes occurring along the length of the sensor and reference channels. Herein, we exploit these features of the YI to study interactions of biomolecules with recognition elements immobilized in selected regions of agarose gel in the sensor channel. We show that the YI is well suited for the biosensing of an exemplar biomolecule, streptavidin, in the absence and presence of the bovine serum albumin interferent. Equally, we couple the YI with electrokinetic transport to reduce the time needed for biosensing.

Uncertainty in simulated brightness temperature due to sensitivity to atmospheric gas spectroscopic parameters from the centimeter- to submillimeter-wave range

Abstract. Atmospheric radiative transfer models are extensively used in Earth observation to simulate radiative processes occurring in the atmosphere and to provide both upwelling and downwelling synthetic brightness temperatures for ground-based, airborne, and satellite radiometric sensors. For a meaningful comparison between simulated and observed radiances, it is crucial to characterize the uncertainty in such models. The purpose of this work is to quantify the uncertainty in radiative transfer models due to uncertainty in the associated spectroscopic parameters and to compute simulated brightness temperature uncertainties for millimeter- and submillimeter-wave channels of downward-looking satellite radiometric sensors (MicroWave Imager, MWI; Ice Cloud Imager, ICI; MicroWave Sounder, MWS; and Advanced Technology Microwave Sounder, ATMS) as well as upward-looking airborne radiometers (International Submillimetre Airborne Radiometer, ISMAR, and Microwave Airborne Radiometer Scanning System, MARSS). The approach adopted here is firstly to study the sensitivity of brightness temperature calculations to each spectroscopic parameter separately, then to identify the dominant parameters and investigate their uncertainty covariance, and finally to compute the total brightness temperature uncertainty due to the full uncertainty covariance matrix for the identified set of relevant spectroscopic parameters. The approach is applied to a recent version of the Millimeter-wave Propagation Model, taking into account water vapor, oxygen, and ozone spectroscopic parameters, though the approach is general and can be applied to any radiative transfer code. A set of 135 spectroscopic parameters were identified as dominant for the uncertainty in simulated brightness temperatures (26 for water vapor, 109 for oxygen, none for ozone). The uncertainty in simulated brightness temperatures is computed for six climatology conditions (ranging from sub-Arctic winter to tropical) and all instrument channels. Uncertainty is found to be up to few kelvins [K] in the millimeter-wave range, whereas it is considerably lower in the submillimeter-wave range (less than 1 K).

Performance improvement of planar silicon nanowire field effect transistors via catalyst atom doping control

Catalytic growth of silicon nanowires (SiNWs), mediated by metallic droplet, provide ideal quasi-1D channels to construct high performance field effect transistor (FET). However, the incorporation of catalytic metal atoms into SiNWs channels has significant impact on the FET characteristics, and thus needs to be better understood and controlled to fulfil its potential for high performance electronics. In this work, we focus on the effect of the incorporation of indium (In) catalytic atoms into planar SiNWs, grown via an in-plane solid-liquid-solid (IPSLS) mechanism, on the FET device performance. It is found that the initial high concentration of p-type In dopants in the as-grown SiNWs led to an equivalent boron (B) doping of 5×1018 cm−3. However, a simple step-wise annealing in oxygen at different temperatures, varied from 620 °C to 920 °C, can help to out-diffuse the dissolved In atoms into the surface SiO2 layer, and reduce the In concentrations down to <5×1017 cm−3, as verified by atom-probe tomography (APT) and four-point-probe measurements. This effective dopant control enables a remarkable device performance improvement of the Schottky barrier FETs, built upon an orderly array of parallel SiNWs of approximately 25 nm in diameter, where the current ratio (Ion/Ioff) has been substantially boosted from 105 to 108, while the subthreshold swing (SS) reduced from 400 mV/dec down to ∼100 mV/dec, with a hole carrier mobility increased from 10 to ∼75 cm2 V−1 s−1 as extracted according to a finite element analysis. These results pave the way for the employment of the catalytic IPSLS SiNWs to serve as high quality 1D channels for high-performance SiNWs-based electronics and sensors.

Exploration of deep learning-driven multimodal information fusion frameworks and their application in lower limb motion recognition

Research on Lower Limb Motion Recognition (LLMR) based on various wearable sensors has been widely applied in exoskeleton robots, exercise rehabilitation, etc. Typically, employing multimodal information tends to yield higher accuracy and stronger robustness compared to using unimodal information. Due to the inevitable reliance on feature engineering in shallow machine learning-based LLMR methods, this study leverages the powerful non-linear feature mapping capability of deep learning (DL) to construct several end-to-end LLMR frameworks, including: Convolutional Neural Networks (CNNs), CNN-Recurrent Neural Networks (RNNs) and CNN-Graph Neural Networks (GNNs). The effectiveness of the proposed frameworks is verified in distinct tasks, including the recognition of seven types of lower limb motions in healthy subjects and three types of motions in patients with stroke, as well as the phase recognition task during the sit-to-stand (SitTS) process in patients with stroke, achieving the highest mean accuracy of 95.198 %, 99.784 %, and 99.845 %, respectively. Further research and integration of two transfer learning techniques, adaptive Batch Normalization (BN) and model fine-tuning, significantly enhance the applicability of the proposed frameworks in inter-subject prediction. Additionally, systematic analyses are conducted to assess the strengths and weaknesses of different models in terms of recognition performance, complexity, and adaptability to variations in the number of modalities and sensor channels. Experimental results indicate that the proposed frameworks hold promise in providing potential support for the development of human-robot collaborative lower limb exoskeletons or rehabilitation robots.

A data-driven online calibration method for enhancing accuracy in biomedical tactile sensing

Soft tactile sensors have been used to acquire biomedical signals for a variety of applications. Lack of proper calibration can lead to data discrepancies and, in some cases, misdiagnoses. This study introduces a channel-specific calibration technique using an enhanced stacked Simple Recurrent Unit (SRU) network for real-time calibration of each sensor channel. The network is designed to correct deviations due to sensor characteristics or operational pressures through a dynamic weighting mechanism that adjusts to each channel’s energy level. A pneumatic-controlled platform was developed to ensure quality training data for the deep learning model, facilitating a robust public dataset. For pulse wave signals, our approach significantly outperformed existing methods, reducing the Root Mean Square Error (RMSE) by 68.21% over raw signals and 25.68% compared to LSTM-based calibration. It also improved the accuracy of radial augmentation index (radial-AIx) measurements from −3.19% to −0.03%, making biomedical sensors more reliable.

Development of multi-channel magnetic flux leakage testing system for bridge cables

Magnetic flux leakage (MFL) is one of the most popular techniques for detecting broken wire in bridge cables. MFL sensor array has attracted a lot of attention in recent years due to its capacity to measure the spatial distribution of the magnetic field of bridge cable. More sensors in the array lead to increased useful information extraction from the MFL field, allowing for more precise quantitative evaluation of bridge cable damage. This study reports the development of a cable MFL testing system capable of synchronous acquiring up to 512 channels of MFL sensors signal. Radial sensor arrays (RSA) composed of vertically placed magnetic sensors spaced at equal intervals are developed to be arranged around the bridge cable. They record the spatial distribution of the magnetic leakage field of the broken wire by collecting the MFL signal at various lift-offs. To solve the problem of multi- channel MFL signal synchronous acquisition, this study develops a MFL signal acquisition card based on multiplexing technology, capable of synchronous acquisition of up to 512 channels of MFL signal. Multiplexers are used to switch sensor channels, followed by multi-channel A/D conversion chips to convert the transmitted data. The converted data are temporally stored in the First-In-First-Out (FIFO) buffer of the Field Programmable Gate Array (FPGA) and transferred to the computer through the network port for display, storage, and signal processing. Finally, the motion control circuit, mechanical structure, and control software of the bridge cable MFL testing system are fully customized. Experiment results reveal that the system can be used in multi-channel MFL testing, laying the foundation for further multi-channel signal processing and bridge cable health evaluation.

Spatiotemporal polynomial graph neural network for anomaly detection of complex systems

Internet of Things systems are developed rapidly, there is a need for anomaly detection to identify malicious data to guarantee that the systems perform under healthy condition. However, the dynamic system state is not only influenced by sequential patterns in the time dimension but also affected by other sensor channels in the spatial dimension. Although many successful models have been established in past research to handle multivariate time series data, most of these models exhibit shortcomings in capturing spatiotemporal dependencies. The Internet of Things offers a promising platform for advanced data analysis, but its unique characteristics present challenges for anomaly detection. This paper introduces a novel spatiotemporal polynomial graph neural network (STPGNN) is proposed for anomaly detection of complex systems within the Internet of Things (IoT) framework. Specifically, we propose an adaptive spatiotemporal feature modeling module that includes two developed adaptive modules: the Adaptive Temporal Module and the Spatial Module. These modules are designed to capture dynamic spatial dependencies across multiple time steps and temporal dependencies over time, respectively. The results are then output through a decoder. Importantly, the multi-head attention mechanism employed in both sub-modules can effectively explore potential spatiotemporal dependency patterns within different subspaces. The proposed model, utilizing the innovative approach of STPGNN, demonstrates superior performance compared to existing techniques, as evidenced by experimental results on the real-world test run dataset. This marks a significant advancement in the field of anomaly detection, particularly for complex systems.

A deep learning-assisted skin-integrated pulse sensing system for reliable pulse monitoring and cardiac function assessment

Long-term pulse monitoring plays a vital role in the prevention and diagnosis of cardiovascular diseases since pulse signals can provide rich characteristics of cardiac conditions. Flexible sensors mounting on the wrist for pulse monitoring have drawn great attention due to the advantages of soft, skin-interfaced and non-invasive features. However, slight wrist movement and transposition of the sensor on skin would dramatically impact the signal quality because of motion artifacts and interfacial properties. Here, we report an intelligent skin-integrated system by combining a sensitive pressure sensor array and a signal enhancement algorithm to provide reliable pulse signal monitoring. The sensors have the advantages of low cost, simple fabrication, reusability and high sensitivity. The multiple sensor channels offer rich information of vital signals, which can facilitate deep learning-based algorithms to denoise and reconstruct pulses more accurately and thus enable precise heart rate monitoring and heart variability assessments. The reported technology suggests a novel approach for pulse waveform detection in static and dynamic conditions, allowing users to more easily achieve reliable pulse monitoring.

Machine learning assisted classification between diabetic polyneuropathy and healthy subjects using plantar pressure and temperature data: a feasibility study

Automated and early detection of diabetics with polyneuropathy in an ambulatory health monitoring setup may reduce the major risk factors for diabetic patients. Increased and localized plantar pressure associated with impaired pain and temperature is a combination of developing foot ulcers in subjects with polyneuropathy. Although many interesting research works have been reported in this area, most of them emphasize on signal acquisition process and plantar pressure distribution in the foot region. In this work, a machine learning assisted low complexity technique was developed using plantar pressure and temperature signals which will classify between diabetic polyneuropathy and healthy subjects. Principal component analysis (PCA) and maximum relevance minimum redundancy (mRMR) methods were used for feature extraction and selection respectively followed by k-NN classifier for binary classification. The proposed technique was evaluated with 100 min of publicly available annotated data from 43 subjects and provides blind test accuracy, sensitivity, precision, F1-score, and area under curve (AUC) of 99.58%, 99.50%, 99.44%, 99.47% and 99.56% respectively. A low resource hardware implementation in ARM v6 controller required an average memory usage of 81.2 kB and latency of 1.31 s to process 9 s pressure and temperature data collected from 16 sensor channels for each of the foot region.

Gait-based identification using wearable multimodal sensing and attention neural networks

Advances in sensor technology have sparked a wave of innovation and progress in the domain of wearable devices. However, ensuring the security of these devices remains a critical concern for their users. This study proposes a novel identification framework that employs custom multimodal sensing insoles and deep learning techniques to contribute to the data security of wearable devices in a user-transparent manner. The insole incorporates an inertial sensing unit and ten force sensors to capture kinematic and kinetic information. For this implementation, we propose a lightweight multiclass classification deep model based on depthwise separable convolution and an enhanced attention mechanism, which operates over multiple signal channels. In designing the recognition model, we focused on two pivotal factors: (1) achieving an optimal balance between computational complexity and recognition accuracy, which is essential for models that operate on devices with limited computational power, and (2) introducing an enhanced attention mechanism that ensures high recognition accuracy without the stringent requirement for precise temporal alignment between sensor channels, which can be technically challenging to attain. We conducted extensive experiments to validate the proposed approach, utilizing a walking dataset collected from 82 young subjects. The results indicate that when using data from the multimodal sensing insole, a high recognition accuracy of 97.96 % for person identification is achieved, with a notably low model parameter count of 22,462. The results support the feasibility and effectiveness of sensing footwear-based gait identification. The proposed approach holds significant potential for enhancing user authentication methods across a broad range of scenarios while ensuring transparency and user-friendliness.

Multi-sensor data fusion-enabled lightweight convolutional double regularization contrast transformer for aerospace bearing small samples fault diagnosis

Aiming at the problems of low information utilization and lack of feature mining capability in multi-sensor fusion networks, this study presents a multi-sensor data fusion-enabled lightweight convolutional double regularization contrast transformer for aerospace bearing small samples fault diagnosis. Firstly, a metric termed integrated cliff entropy is devised to assign weights to vibration signals from diverse sensor channels. It aims to enhance the cyclic impulse characteristics within the fused signals, thereby facilitating more precise fault identification. Secondly, a lightweight Diwaveformer architecture is constructed as the backbone of contrast learning. It enables the global and local features of faulty signals to be comprehensively extracted with less computational effort. Finally, a double contrast loss is constructed to optimize the distribution of intra-class and inter-class features to improve the fault identification ability of the network with small samples. Additionally, a discard regularization method is designed to remove the projection head during the contrast learning process, further advancing the model lightweight. Our method achieved accuracies of 95.54% and 92.56% on two aerospace bearing datasets with extremely sparse training samples, which proved its superior performance.

Cross-Attention Enhanced Pyramid Multi-Scale Networks for Sensor-Based Human Activity Recognition.

Human Activity Recognition (HAR) has recently attracted widespread attention, with the effective application of this technology helping people in areas such as healthcare, smart homes, and gait analysis. Deep learning methods have shown remarkable performance in HAR. A pivotal challenge is the trade-off between recognition accuracy and computational efficiency, especially in resource-constrained mobile devices. This challenge necessitates the development of models that enhance feature representation capabilities without imposing additional computational burdens. Addressing this, we introduce a novel HAR model leveraging deep learning, ingeniously designed to navigate the accuracy-efficiency trade-off. The model comprises two innovative modules: 1) Pyramid Multi-scale Convolutional Network (PMCN), which is designed with a symmetric structure and is capable of obtaining a rich receptive field at a finer level through its multiscale representation capability; 2) Cross-Attention Mechanism, which establishes interrelationships among sensor dimensions, temporal dimensions, and channel dimensions, and effectively enhances useful information while suppressing irrelevant data. The proposed model is rigorously evaluated across four diverse datasets: UCI, WISDM, PAMAP2, and OPPORTUNITY. Additional ablation and comparative studies are conducted to comprehensively assess the performance of the model. Experimental results demonstrate that the proposed model achieves superior activity recognition accuracy while maintaining low computational overhead.

Stealthy attacks against distributed state estimation of stochastic multi-agent systems under composite attack detection mechanisms

This paper is concerned with the security problem of distributed state estimation for stochastic multi-agent systems (MASs) under directed topology. First, in the absence of attacks, an optimal distributed filter is proposed to reconstruct the states of MASs by minimizing the mean square error, which makes full use of the measurable local output information and relative state estimates of agents. Moreover, instead of using a single detection mechanism to measure the stealthiness of attacks, based on the structure of the derived filter, a composite attack detection mechanism capable of simultaneously detecting anomalies in the sensor channels of the agents themselves and the network topologies between different agents is utilized in this paper. Then, from the perspective of potential adversaries, two stealthy attack strategies are established through a joint design of sensor and topology attacks, enabling attackers to fulfill diverse attack objectives while evading the given composite detection mechanisms. Finally, simulation results are provided to verify the availability of the designed filter and attack generation schemes.

A new automatic sugarcane seed cutting machine based on internet of things technology and RGB color sensor.

Egypt is among the world's largest producers of sugarcane. This crop is of great economic importance in the country, as it serves as a primary source of sugar, a vital strategic material. The pre-cutting planting mode is the most used technique for cultivating sugarcane in Egypt. However, this method is plagued by several issues that adversely affect the quality of the crop. A proposed solution to these problems is the implementation of a sugarcane-seed-cutting device, which incorporates automatic identification technology for optimal efficiency. The aim is to enhance the cutting quality and efficiency of the pre-cutting planting mode of sugarcane. The developed machine consists of a feeding system, a node scanning and detection system, a node cutting system, a sugarcane seed counting and monitoring system, and a control system. The current research aims to study the pulse widths (PW) of three-color channels (R, G, and B) of the RGB color sensors under laboratory conditions. The output PW of red, green, and blue channel values were recorded at three color types for hand-colored nodes [black, red, and blue], three speeds of the feeding system [7.5 m/min, 5 m/min, and 4.3 m/min], three installing heights of the RGB color sensors [2.0 cm, 3.0 cm, and 4.0 cm], and three widths of the colored line [10.0 mm, 7.0 mm, and 3.0 mm]. The laboratory test results s to identify hand-colored sugarcane nodes showed that the recognition rate ranged from 95% to 100% and the average scanning time ranged from 1.0 s to 1.75 s. The capacity of the developed machine ranged up to 1200 seeds per hour. The highest performance of the developed machine was 100% when using hand-colored sugarcane stalks with a 10 mm blue color line and installing the RGB color sensor at 2.0 cm in height, as well as increasing the speed of the feeding system to 7.5 m/min. The use of IoT and RGB color sensors has made it possible to get analytical indicators like those achieved with other automatic systems for cutting sugar cane seeds without requiring the use of computers or expensive, fast industrial cameras for image processing.