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May 2024 DOI 10.14302/issn.2998-1506.jpa-24-5058
Shrestha SwatiCorresponding author
Wheat is a staple grain crop in the United States and around the world. Weed infestation, particularly grass weeds, poses significant challenges to wheat production, competing for resources and reducing grain yield and quality. Effective weed management practices, including early identification and targeted herbicide application are essential to avoid economic losses. Recent advancements in unmanned aerial vehicles (UAVs) and artificial intelligence (AI), offer promising solutions for early weed detection and management, improving efficiency and reducing negative environment impact. The integration of robotics and information technology has enabled the development of automated weed detection systems, reducing the reliance on manual scouting and intervention. Various sensors in conjunction with proximal and remote sensing techniques have the capability to capture detailed information about crop and weed characteristics. Additionally, multi-spectral and hyperspectral sensors have proven highly effective in weed vs crop detection, enabling early intervention and precise weed management. The data from various sensors consecutively processed with the help of machine learning and deep learning models (DL), notably Convolutional Neural Networks (CNNs) method have shown superior performance in handling large datasets, extracting intricate features, and achieving high accuracy in weed classification at various growth stages in numerous crops. However, the application of deep learning models in grass weed detection for wheat crops remains underexplored, presenting an opportunity for further research and innovation. In this review we underscore the potential of automated grass weed detection systems in enhancing weed management practices in wheat cropping systems. Future research should focus on refining existing techniques, comparing ML and DL models for accuracy and efficiency, and integrating UAV-based mapping with AI algorithms for proactive weed control strategies. By harnessing the power of AI and machine learning, automated weed detection holds the key to sustainable and efficient weed management in wheat cropping systems.
Sep 2020 DOI 10.14302/issn.2641-5526.jmid-20-3505
Raghul V.Corresponding author
Department of Mathematics, Loyola College, Chennai
Testing people for COVID-19 in a country like India with a huge population is a near impossible task therefore the government is using body temperature as a testing parameter to cover the whole population. Infrared thermometers are used to find the temperature because it is a cheaper and faster way. This testing rate can be done even faster, without the need of manpower and with far more accuracy using smart watches and bands. These wrist-wearables are mostly used for fitness purposes which have more measuring equipment that is used for preliminary testing done for COVID. This equipment’s are in the form of electric sensors which are small enough to be used in wearables. So we can get even more insight and accuracy compared to the standard method. In this study an application is created to use an array of sensors (Pulse sensor, Pulse oximeter, Accelerometer and temperature sensor) are being used in these wearables to find the chance, that a person is affected due to COVID-19 and the information can be seen real time in mobile phone through the application. All the information can be sent to the health organization’s if required.
Nov 2019 DOI 10.14302/issn.2639-3166.jar-19-3065
Marcelo Scavuzzo CarlosCorresponding author
Comision Nacional de Actividades Espaciales, CONAE. Gulich Institute, R. C45km8 Cordoba, Argentine
The importance of horticulture around the large cities, called green belt (GB), or proximity food production area is related to its contribution to the provision of food as well as its role on social, cultural and ecological aspects. Geoscience and Remote sensing (GRS) are tools that should aid in gathering and updating the information to develop science-based management plans of this areas. Recently, the improvement in terms of spatial, temporal and radiometric resolutions has changed the performance and the approach to the horticulture remote sensing. In this work, we make a brief review on the literature exploring the use of GRS techniques in horticulture, and future trends in order to exploit the available techniques for efficient crop management in the way to improve territorial planning and management. Specifically we found a lack of academic production in this area. In addition we examine the importance of this landscape areas from different points of view (food security, health, ecology, etc.). A systematic revision of published studies on remote sensing on horticulture including different platforms, sensors and methodologies are briefly presented. Finally some aspect related with future trends are discussed.
Jul 2019 DOI 10.14302/issn.2694-2283.jsem-19-2938
Hinzpeter C JaimeCorresponding author
Medical Doctor, University of Chile, Clinical Hospital, Santiago Chile.
An anterior cruciate ligament (ACL) injury is an important cause of rest in athletes. In most cases, ACL injuries do not require external contact and they are associated with biomechanical risk factors that increase ACL tension. The increase of the hip flexion angle (HF) is included within these. The ACL requires cooperation of the periarticular musculature of the knee, muscle groups, hip stabilizers and CORE muscles; consequently, fatigue caused by exercise would alter the balance and put this ligament at risk. The objective of the study is to determine the angular behavior for HF before and after a physical load (a standardized training) in children between 11 and 12 years old. A non-randomized clinical study was carried out. The sample consisted of 50 soccer school students born between 11 and 12 years old. The angular behavior of HF was compared before and after performing a training session. The angular behavior was measured through the Drop Jump test (DJ), with data obtained by inertial sensors. After the exercise, there was a significant increase in HF. It was concluded that the angular behavior of HF increases significantly in both extremities after training and that preventive measures must be applied for neuromuscular control of the hip.
Sep 2018 DOI 10.14302/issn.2574-4518.jsdr-18-2302
Liberati DiegoCorresponding author
National Research Council of Italy
A methodological overview of objective sleep quality measurement compares actigraphy, polysomnography, and novel sensors, with notes on validity and real‑world use.
Oct 2016 DOI 10.14302/issn.2377-2549.jndc-16-1212
Babaee SaeedCorresponding author
Faculty of Chemistry and Chemical Engineering, Malek Ashtar University of Technology, Lavizan Avenue, Tehran, P.O. Box 16765/3454, Iran
Application of nickel in different industries has been developed and so contamination of natural water is a great concern due to its potentially toxic effects on living beings. Therefore, fast monitoring of Ni2+ in aqueous samples is important. In this work, we fabricated a sensitive optical sensor for determination of nickel in mineral water samples and hydrogen peroxide solutions. The optode was prepared by incorporation of 1-(2-pyridylazo)-2-naphthol and sodium tetraphenylborate in a plasticized poly (vinyl chloride) membranes containing dioctyladipate as a plasticizer. The influence of several parameters such as pH, base matrix, solvent mediator and ligand concentration were optimized. Comparison the obtained results with previously reported sensors revealed that the proposed method, in addition to fast and simplicity, provided good linear range (1.70–85.20 µmol L-1) and low detection limit (0.17 µmol L-1). The precision (relative standard deviation) was better than 1.55% for 7 replicate determinations of 17.10 µmol L-1 of Ni in various membranes.
Jun 2016 DOI 10.14302/issn.2374-9431.jbd-15-647
Nawaz AsmatCorresponding author
Dep of Micro and Nano Systems Technology, University South East Norway, 3184, Raveien Borre.
Diabetes is a metabolic disorder that results in human body due to insulin deficiency, insulin resistance or both. In the management of diabetes, glucose monitoring technology has been used for the last three decades. The aim of this review article is to describe concise and organized information about different techniques of non-invasive continuous blood glucose monitoring. Many research groups have been working to develop wearable sensors for continuous blood glucose monitoring, but at present, there are to our knowledge no commercially successful non-invasive glucose monitors on the market. To achieve an acceptable sensor system, a glucose sensor should have accuracy better than 15mg/dl (0.8 mmol/l). In future development, continuous glucose sensor systems may become predictable, selective, reliable and acceptable for patient use.
May 2014 DOI 10.14302/issn.2374-9431.jbd-13-283
Mei YongCorresponding author
Department of Chemical and Biological Engineering, Iowa State University, Ames, Iowa 50011
Continuous-time glucose monitoring (CGM) effectively improves glucose control, as oppose to infrequent glucose measurements (i.e. using Lancet Meters), by providing frequent blood glucose concentration (BGC) to better associate this variation with changes in behavior. Currently, the most widely used CGM devices rely on a sensor that is inserted invasively under the skin. Because of the invasive nature and also the replacement cost of sensors, the primary users of current CGM devices are insulin dependent people (type 1 and some type 2 diabetics). Most non-insulin dependent diabetics use only lancet glucose measurements. The ultimate goal of this research is the development of CGM technology that overcomes these limitations (i.e. invasive sensors and their cost) in an effort to increase CGM applications among non-insulin dependent people. To meet this objective, this preliminary work has developed a methodology to mathematically infer BGC from measurements of non-invasive input variables which can be thought of as a “virtual” or “soft” sensor approach. In this work virtual sensors are developed and evaluated on 20 subjects using four BGC measurements per day and eight input variables representing meals, activity, stress, and clock time. Up to four weeks of data are collected for each subject. One evaluation consists of 3 days of training and up to 25 days of testing data. The second one consists of one week of training, one week of validation, and 2 weeks of testing data. The third one consists two weeks of training, one week of validation and one week of testing data. Model acceptability is determined on an individual basis based on the fitted correlation to CGM testing data. For 3 day, 1 week, and 2 weeks training studies, 35%, 55% and 65% of the subjects, respectively, met the Acceptability Criteria that we established based on the concept of usefulness.