Detected Research Articles

Cultivar effect on proximal optical sensor measurements and estimation of leaf N content in muskmelon and sweet pepper

In intensive vegetable crops, there is a requirement for monitoring tools that assess crop nitrogen (N) status to reduce N use and the potential for nitrate (NO3-) leaching loss. Monitoring with proximal optical sensors can regularly provide information to vegetable growers to assist with optimal crop N management. Reference values for proximal optical sensor measurements that indicate crop N sufficiency or deficiency are increasingly available in the literature. Available values are species specific; however, their use may be problematic if proximal optical sensor measurements are affected by cultivar. Little is known on whether differences between cultivars, within a species, affect optical sensor measurements and their relationships with crop N status, particularly in vegetable crops. This study evaluated the effect of cultivar on a) measurements with the SPAD-502 chlorophyll meter, Normalized Difference Vegetation Index (NDVI) measured with the Crop Circle ACS 470 canopy reflectance sensor, and Nitrogen Balance Index (NBI-R) measured with the Multiplex fluorimeter, and b) the relationships of these measurements with leaf N content. The study was conducted with four cultivars of muskmelon (Tezac, Bosito, Magiar and Jacobo) and three of sweet pepper (Melchor, Machado and CLX PLRJ 731). In both muskmelon and sweet pepper, there were significant differences between cultivars in SPAD and in NBI-R. The range of differences between the cultivars was 2.2−9.7 and 0.6−2.8 SPAD units in muskmelon and sweet pepper, respectively, and 0−0.16 and 0.020.08 NBI-R units in muskmelon and sweet pepper, respectively. Significant differences between cultivars in the NDVI were only obtained in muskmelon; the range of differences between the four cultivars was 0.01–0.04 NDVI units. The relatively large cultivar effect with SPAD and NBI-R values with muskmelon may limit the use of reference values in this species. For avoiding cultivar effects in the two species evaluated, NDVI is preferrable to SPAD and NBI-R. In general, the slopes and intercepts of linear relationships between optical sensor measurements and leaf N content, for each cultivar of each species, were not significantly different to the slope and intercept of the relationship calculated with pooled data of all cultivars of each species. This indicated that the sensitivity of optical sensors to assess leaf N content was similar between cultivars of each species.

Application of energy sustainability model based on optical sensing technology in intelligent warehousing performance management in green manufacturing industry

As the global focus on sustainable development continues to grow, green manufacturing, as a way to optimize resource use and reduce environmental impact, needs to be combined with advanced technologies to improve its efficiency. This study aims to explore the application of energy sustainability model based on optical sensing technology in smart storage of green manufacturing industry, evaluate its impact on storage performance management, and further promote the development of green manufacturing industry. In this paper, an optical sensing system is constructed to monitor and analyze the energy consumption in storage environment in real time. In this study, a green manufacturing enterprise was selected as an example, and the optical sensor was used to monitor energy consumption, inventory status and environmental parameters in real time to ensure the real-time and accuracy of data. The collected data is analyzed to identify patterns and anomalies in energy use to optimize inventory management and logistics scheduling. Evaluate the energy efficiency of storage systems by setting energy efficiency indicators and compare them with traditional models to quantify improvements. On the basis of monitoring and analysis, the energy management strategy is continuously adjusted to enhance the adaptability and flexibility of the intelligent storage system. The research results show that the application of optical sensing technology can significantly improve the energy management performance in warehousing, through real-time monitoring and intelligent optimization, enterprises can achieve more efficient energy use, further promote the implementation of green manufacturing, and provide strong support for the realization of environmental and economic benefits.

REFRACTIVE INDEX BASED DETECTION WITH A HIGH SENSITIVITY BIOSENSOR ENHANCED BY GRAPHENE

Over the past decade, optical sensors have made significant advances. An optical sensor examines the environmental impact through the change of an optical signal and offers advantages such as low cost and label-free detection. In this study, a sensor consisting of a single graphene layer and a slit positioned on the substrate is proposed. The strip gap made to improve the excitation of graphene plasmons allowed to achieve 96.2% high transmission resonance mode. This demonstrates the ability of the sensor surface to detect changing environmental conditions. The results show that the sensitivity of the sensor is 6282 nm/RIU when the sensor surface is exposed to analytes with different refractive indices. The use of a single graphene sheet eliminates the need for a metal resonator and achieves a higher sensitivity compared to some experiments recently published in the literature. Thus, the disadvantage of significant ohmic losses in metal resonators is avoided. Furthermore, a thorough discussion of various factors, including the modification of the strip gap width on the graphene layer and electrical tunability, led to the achievement of optimal sensitivity.

Smart-Agro: Enhancing Crop Management with Agribot

The Agri-Bot robotic system indeed characterizes a substantial advancement in modern agriculture, offering a multifaceted solution for monitoring and managing agricultural environments. By integrating various Arduino-based sensors and motor drivers, it provides a comprehensive toolkit for farmers to effectively oversee their crops' health and optimize resource usage. The inclusion of pH and moisture sensors enables real-time monitoring of soil conditions, allowing farmers to adjust irrigation and fertilizer application precisely according to the plants' needs. Additionally, the DHT11 sensor offers insights into ambient conditions crucial for plant growth, such as temperature and humidity, facilitating informed decision-making. The incorporation of the L298 motor driver further enhances the system's capabilities by enabling automation of tasks like irrigation and seed sowing with precision and efficiency. This integration of robotics and sensor technology not only streamlines agricultural processes but also empowers farmers with data-driven insights to optimize crop growth and sustainability.

Metal-Organic Frameworks as Electrode Materials for Lithium-Ion Battery

Over recent decades, Metal-Organic Frameworks (MOFs) have distinguished themselves as a unique class of porous materials due to their adaptable surface and structural properties. This versatility has made MOFs highly relevant across various fields, including drug delivery, gas separation, catalysis, and sensor technology. Additionally, their conductive properties have made them promising candidates for use in energy storage systems like high-energy-density batteries and supercapacitors. MOFs are particularly noted for their role in the development of rechargeable lithium-ion batteries (LIBs) and supercapacitors, where they serve as both anode and cathode materials. The ability to fine-tune MOFs at a molecular level allows for precise control over their structure and chemistry, enhancing their functionality in energy storage applications. This control facilitates superior electronic and ionic transport within MOFs, which is critical during the charging and discharging cycles of LIBs. This review delves into the various synthetic methods used to develop specific MOF structures, focusing on their implementation within LIBs to improve cyclic stability and discharge capacity. Recent advancements in MOF technology as anode and cathode materials are explored, providing insights into how these developments can optimize reaction conditions and design choices within the battery development community and broader electrochemical energy storage sectors. The aim is to highlight how MOFs’ inherent characteristics can be leveraged to enhance the performance and efficiency of energy storage devices.

Modeling of High-Performance Fiber Optic SPR Sensor for Colorectal Cancer Detection Designed Using Amorphous Silicon and TiO2 Layers Under Optimum Radiation Damping

Modeling of High-Performance Fiber Optic SPR Sensor for Colorectal Cancer Detection Designed Using Amorphous Silicon and TiO₂ Layers Under Optimum Radiation Damping

Correlation of palatal anatomic characteristics with dermatoglyphic heterogeneity in different growth patterns

The study aimed to explore the correlation between dermatoglyphic patterns and quantitative palatal anatomic variables in individuals with different growth patterns. A cross-sectional study was conducted involving 126 healthy patients aged 17-25 years. Participants were divided into three groups based on growth patterns: average, vertical, and horizontal. Dermatoglyphic patterns were recorded using an optical fingerprint sensor, and palatal characteristics were measured using digital software. Palatal characteristics, including intercanine width, intermolar width, and palatal depth, were measured using digital software. The results were statistically analyzed. Significant differences were observed in ridge counts among the three growth patterns. The average growth pattern showed lower ridge counts compared to the vertical and horizontal growth patterns. Dermatoglyphic patterns, such as double loops and tented arches, were significantly higher in the horizontal growth pattern. Weak correlations were found between certain dermatoglyphic patterns and palatal characteristics, with simple arch patterns showing a negative correlation with inter-canine width and symmetrical whorl patterns showing a positive correlation with palatal depth. Loop patterns, spiral patterns, double loop patterns, symmetrical whorl, and simple arch patterns were significant predictors of growth patterns. This study revealed distinct dermatoglyphic patterns and ridge counts among individuals with different growth patterns. Weak correlations were observed between dermatoglyphic patterns and palatal characteristics. However, the predictive value of dermatoglyphics for skeletal malocclusion requires further investigation. Understanding the relationships between dermatoglyphic patterns and craniofacial growth can provide valuable insights into genetic and developmental factors affecting dental and orthodontic conditions.

Carbon-Nanotube-Based Optical Fiber Sensor With Rapid Response for Human Breath Monitoring and Voiceprint Recognition

Carbon-Nanotube-Based Optical Fiber Sensor With Rapid Response for Human Breath Monitoring and Voiceprint Recognition

Fiber Optic Wearable Curvature Sensor Based on Taper-Assisted Microcavity

Fiber Optic Wearable Curvature Sensor Based on Taper-Assisted Microcavity

The 2023 Mw 6.8 Morocco earthquake induced atmospheric and ionospheric anomalies

Earth observations through Global Navigation Satellite System (GNSS) and Remote Sensing (RS) technologies play a significant role in natural hazard surveillance, particularly in the context of earthquake prediction and detection. This study introduces a distinctive Deep Learning (DL) based approach to identifying ionospheric and atmospheric precursors, utilizing data from multiple satellite sources, and provides a comprehensive analysis of spatiotemporally varying precursors, contributing to the understanding and monitoring of seismic activity in earthquake-prone regions. In our investigation of the Morocco earthquake on September 08, 2023 (Mw 6.8), we analyzed various precursors including Total Electron Content (TEC), Air Pressure (AP), Relative Humidity (RH), Outgoing Longwave Radiation (OLR), and Air Temperature (AT). Our study aims to identify a synchronized anomalous window of potential earthquake precursors using Standard Deviation (STDEV), Continuous Wavelet Transform (CWT), and Long Short-Term Memory Inputs (LSTM) network. Both statistical and deep learning methods revealed abnormal fluctuations as precursors occurring within 8 to 9 days before the earthquake near the epicenter. Additionally, we detected geomagnetic anomalies in the ionosphere 6 days prior to and 4 days after the earthquake, coinciding with active geomagnetic storm days. This research underlines the importance of combining multiple earthquake precursors using statistical and deep learning approaches to support the understanding of the Lithosphere-Atmosphere-Ionosphere-Coupling (LAIC) phenomena.

Geometric vs spectral content of Remotely Piloted Aircraft Systems images in the Precision agriculture context

In the last years the agricultural sector has been evolving and new technologies, like Unmanned Aerial Vehicles (UAV) and satellites, were introduced to increase crop management efficiency, reducing environmental costs and improving farmers’ income. MAIA-S2 sensor is presently one of the most performing optical sensors operating on a Remotely Piloted Aircraft Systems (RPAS); given its spectral features, it aims at supporting a scaling process where monoscopic satellite data (namely Copernicus S2) with high temporal and limited geometric resolution can be integrated with stereoscopic data from RPAS having a very high spatial resolution. In this work, data from MAIA-S2 sensor were used to detect the effects of different fertilization types on corn with reference to a test field located in Carignano (Piemonte region, NW-Italy). Different amounts of top dressing fertilization were applied on corn and an RPAS acquisition operated on 14th June 2021 (corresponding date to the corn stem elongation stage) to explore if any effects could be detectable. Three spectral indices, namely Normalized Difference Vegetation Index, Normalized Difference Red Edge index and Canopy Height Model, computed from at-the-ground reflectance calibrated MAIA-S2 data, were compared to evaluate the correspondent response to the different fertilization rates. Results show that: (i) NDVI poorly detect N-related differences zones; (ii) NDRE and CHM reasonably reflect the different N fertilization doses; (iii) Only CHM proved to be able to detect crop height and, consequently, biomass differences that are known to be induced by different rates of fertilization.

Internet of Thing (IOT) Based Sensor Technologies and Smart Irrigation System: An Analysis of Critical Success Factors in Emerging Markets

ABSTRACT: This study explores how smart irrigation systems can be improved by introducing advanced technologies and reducing water consumption to fight climate change. This study aims to identify the factors affecting the success of smart irrigation systems based on the Internet of Things with the mediation of optimal consumption management. This is an applied research employing mixed methodologies (qualitative-quantitative). Initially, the semi-structured interviews were carried out to obtain the effective success factors of the smart irrigation system. Analysis in the qualitative section has been conducted using the Maxqda software. For the quantitative study, a 25-statement questionnaire was applied according to the selected codes obtained with a five-point Likert scale to validate the obtained model. The questionnaire was distributed online in January 2023, and analysis was conducted using SmartPLS3 software. The technical aspects of the questionnaire were assessed using validity and reliability criteria, which can guarantee the precision of the research findings. Construct and content validity were employed in this study to examine the questionnaire’s validity. The researchers employed Cronbach’s alpha coefficient to evaluate the reliability of the measurement instrument. Besides, it can be said that the research instrument has acceptable reliability because obtained Cronbach’s alpha for all variables was greater than 0.70 and the questionnaire’s overall alpha was determined to be 0.810. Furthermore, the t-statistics for all research hypotheses is higher than 1.96 at the 95% confidence level. In this sense, the hypotheses of the research were confirmed. The Internet of Things and will change the nature of activities and services in the market. In fact, for emerging markets, the untapped and growing investment opportunities and the economic leap against the Internet of Things are so enticing and facilitating that it is impossible to resist. and neglecting it will cause irreparable damage to agility. Therefore, the success of the Internet of Things for emerging markets depends on the preconditions of integration of activities carried out by decision-making organizations, facilitation of regulations, and communication and interaction between relevant sectors, which should be considered a priority through cross-sectoral research.

Precision Fertilization: A critical review analysis on sensing technologies for nitrogen, phosphorous and potassium quantification

Fertilization is paramount for agriculture productivity and food security. Plant nutrition pre-established recipes and nutrient uptake are rarely managed by changing the fertilizer composition at the different stages of the plant life cycle. Herein we perform a literature review analysis – since the year 2000 and onwards – of the state-of-the-art capabilities of Nitrogen, Phosphorous, and Potassium (NPK) sensors for liquid fertilizers (e.g., hydroponics). From the initial search hits of 1660 results, only 53 publications had relevant information for this topic; from these, only 9 had NPK quantitative information. Qualitative analysis was performed by determining the number of publications for each nutrient, according to sample complexity and existing single, multiplexed or hybrid technologies. Quantitative assessment was performed by extracting the bias and linearity, the limit of detection and concentration ranges of sensor operation, framed into the context of the sensor technology development stage and sample compositional complexity. The most common technologies are colorimetry, ion-selective electrodes, optrodes, chemosensors, and optical spectroscopy. The most abundant technologies are for nitrate quantification, from which ion-selective electrodes are the most widely used technology, and sensors for phosphate quantification are the less developed. Most are at low technological levels of development, not dealing with the complexity of agriculture samples due to matrix effects and interference. Measuring the fertilizer composition, nutrient uptake, the state of the chemical network, and controlling the release of nutrients using new functional materials, is one of the most important challenges ahead for the existence of precision fertilization. Intelligent sensing and smart materials are today the most successful strategy for dealing with matrix effects and interferences, being led by ion-selective electrodes and spectroscopy technologies.

Achieving precise multiparameter measurements with distributed optical fiber sensor using wavelength diversity and deep neural networks.

The development of advanced distributed optical fiber sensing systems that are capable of performing accurate and spatially resolved multiparameter measurements is of great interest to a wide range of scientific and industrial applications. Here, we propose and experimentally demonstrate a wavelength diversity based advanced distributed optical fiber sensor system to accomplish multiparameter sensing while greatly enhancing measurement accuracy. A suite of deep neural network (DNN) algorithms are developed and verified for data denoising, rapid Brillouin frequency shift estimation, and vibration data event classification. As a proof-of-concept, we demonstrate the effectiveness of the proposed advanced wavelength diversity distributed fiber sensor system assisted by DNN for simultaneous, independent measurements of static strain, temperature, and acoustic vibrations over a 25 km long sensing fiber at 3 m spatial resolution. These results suggest the potential for an intelligent multiparameter monitoring system with enhanced performance in advanced structural health monitoring applications.

IoT Wearable Machine Devices Based on Optical Sensors and Wireless Networks Application in Community Fitness Data Analysis

IoT Wearable Machine Devices Based on Optical Sensors and Wireless Networks Application in Community Fitness Data Analysis

The Fundamental of Fiber Bragg Grating (FBG) Sensor

An optical sensor known as an FBG is produced by laterally exposing a single mode fiber core to a strong UV laser light pattern on a regular basis. The exposure causes a sustained increase in the refractive index (ncore) of the fiber's core, resulting in fixed index modulation called grating (λ). The grating inside the fiber optic core must reflect a certain wavelength of incoming light while transmitting all other wavelengths. This wavelength is referred to as the Bragg wavelength or Bragg linked to grating period. FBG functions as intended when a Bragg reflector is built on an optical fiber by taking use of the regular fluctuations in the refractive index of the single mode fiber core. When light travels through the FBG, certain wavelengths will be transmitted and others will be reflected. When the strain or temperature around the grating varies, a shift in the wavelength of the light reflected is observed.

Deep Convolutional Neural Network Algorithm Based on Optical Sensors and Wireless Mobile Networks for Real time Monitoring of Physical Health

Deep Convolutional Neural Network Algorithm Based on Optical Sensors and Wireless Mobile Networks for Real time Monitoring of Physical Health

Application of Wireless Network Data Collection Based on Optical Topology Sensors in Sports Technology Evaluation

Application of Wireless Network Data Collection Based on Optical Topology Sensors in Sports Technology Evaluation

Color tunable Er-Eu co-doped double perovskite La2MgSnO6 phosphor for optical temperature sensor

Color tunable Er-Eu co-doped double perovskite La2MgSnO6 phosphor for optical temperature sensor

Optical Fiber Bragg Grating as a Strain Sensor

Abstract: The sensor technology industry has come to recognize and value the optical fibre sensor (OFS). It is frequently used to detect changes in the environment and to respond to outputs from other systems, such as those in industrial, monitoring, and chemical analysis applications. While a brief segment of optical fiber with a particular wavelength is reflected with light in a device known as a Fibre Bragg Grating, a Bragg reflector begins to grow and transmit all other wavelengths. Through software modeling, the current study focuses on the changing traits and behaviors of temperature and strain sensors that are used with fiber bragg gratings (FBG). The main objective of this work is to study the properties and behavior of strain sensors utilized in conjunction with fiber bragg gratings. A strain sensor can be used to determine the strain in an object whose resistance changes when force is applied