scholarly journals Non-Invasive Tools to Detect Smoke Contamination in Grapevine Canopies, Berries and Wine: A Remote Sensing and Machine Learning Modeling Approach

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3335 ◽  
Author(s):  
Sigfredo Fuentes ◽  
Eden Jane Tongson ◽  
Roberta De Bei ◽  
Claudia Gonzalez Viejo ◽  
Renata Ristic ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Management Practices ◽  
Near Infrared ◽  
Detection System ◽  
Screening Tools ◽  
Unmanned Aerial Systems ◽  
Non Invasive ◽  
Aerial Systems ◽  
Non Destructive

Bushfires are becoming more frequent and intensive due to changing climate. Those that occur close to vineyards can cause smoke contamination of grapevines and grapes, which can affect wines, producing smoke-taint. At present, there are no available practical in-field tools available for detection of smoke contamination or taint in berries. This research proposes a non-invasive/in-field detection system for smoke contamination in grapevine canopies based on predictable changes in stomatal conductance patterns based on infrared thermal image analysis and machine learning modeling based on pattern recognition. A second model was also proposed to quantify levels of smoke-taint related compounds as targets in berries and wines using near-infrared spectroscopy (NIR) as inputs for machine learning fitting modeling. Results showed that the pattern recognition model to detect smoke contamination from canopies had 96% accuracy. The second model to predict smoke taint compounds in berries and wine fit the NIR data with a correlation coefficient (R) of 0.97 and with no indication of overfitting. These methods can offer grape growers quick, affordable, accurate, non-destructive in-field screening tools to assist in vineyard management practices to minimize smoke taint in wines with in-field applications using smartphones and unmanned aerial systems (UAS).

scholarly journals An Overview of Near Infrared Spectroscopy and Its Applications in the Detection of Genetically Modified Organisms

2021 ◽  
Vol 22 (18) ◽  
pp. 9940
Author(s):  
Soo-In Sohn ◽  
Subramani Pandian ◽  
Young-Ju Oh ◽  
John-Lewis Zinia Zaukuu ◽  
Hyeon-Jung Kang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Food Systems ◽  
Near Infrared ◽  
Genetically Modified Organisms ◽  
Genetically Modified ◽  
Genetically Modified Crops ◽  
Non Invasive ◽  
Agriculture And Food ◽  
Non Destructive

Near-infrared spectroscopy (NIRS) has become a more popular approach for quantitative and qualitative analysis of feeds, foods and medicine in conjunction with an arsenal of chemometric tools. This was the foundation for the increased importance of NIRS in other fields, like genetics and transgenic monitoring. A considerable number of studies have utilized NIRS for the effective identification and discrimination of plants and foods, especially for the identification of genetically modified crops. Few previous reviews have elaborated on the applications of NIRS in agriculture and food, but there is no comprehensive review that compares the use of NIRS in the detection of genetically modified organisms (GMOs). This is particularly important because, in comparison to previous technologies such as PCR and ELISA, NIRS offers several advantages, such as speed (eliminating time-consuming procedures), non-destructive/non-invasive analysis, and is inexpensive in terms of cost and maintenance. More importantly, this technique has the potential to measure multiple quality components in GMOs with reliable accuracy. In this review, we brief about the fundamentals and versatile applications of NIRS for the effective identification of GMOs in the agricultural and food systems.

Near-Infrared Light Emitting Diode Based Non-Invasive Glucose Detection System

2019 ◽  
Vol 19 (10) ◽  
pp. 6187-6191 ◽  
Author(s):  
Seung Ho Lee ◽  
Min Seok Kim ◽  
Ok-Kyun Kim ◽  
Hyung-Hwan Baik ◽  
Ji-Hye Kim
Keyword(s):  
Near Infrared ◽  
Detection System ◽  
Light Emitting Diode ◽  
Infrared Light ◽  
Glucose Detection ◽  
Near Infrared Light ◽  
Light Emitting ◽  
Non Invasive

scholarly journals Machine Learning and Cognitive Ergonomics in Air Traffic Management: Recent Developments and Considerations for Certification

Aerospace ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 103 ◽  
Author(s):  
Trevor Kistan ◽  
Alessandro Gardi ◽  
Roberto Sabatini
Keyword(s):  
Machine Learning ◽  
Traffic Management ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Unmanned Aerial Systems ◽  
Research Attention ◽  
Cognitive Ergonomics ◽  
Traffic Flow Management ◽  
Recent Developments ◽  
Aerial Systems

Resurgent interest in artificial intelligence (AI) techniques focused research attention on their application in aviation systems including air traffic management (ATM), air traffic flow management (ATFM), and unmanned aerial systems traffic management (UTM). By considering a novel cognitive human–machine interface (HMI), configured via machine learning, we examined the requirements for such techniques to be deployed operationally in an ATM system, exploring aspects of vendor verification, regulatory certification, and end-user acceptance. We conclude that research into related fields such as explainable AI (XAI) and computer-aided verification needs to keep pace with applied AI research in order to close the research gaps that could hinder operational deployment. Furthermore, we postulate that the increasing levels of automation and autonomy introduced by AI techniques will eventually subject ATM systems to certification requirements, and we propose a means by which ground-based ATM systems can be accommodated into the existing certification framework for aviation systems.

scholarly journals Cavity Ring-Down Methane Sensor for Small Unmanned Aerial Systems

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 454 ◽  
Author(s):  
Benjamin Martinez ◽  
Thomas W. Miller ◽  
Azer P. Yalin
Keyword(s):  
Near Infrared ◽  
High Sensitivity ◽  
Unmanned Aerial Systems ◽  
Test Results ◽  
Methane Sensor ◽  
Open Path ◽  
Low Mass ◽  
Aerial Systems ◽  
High Finesse ◽  
Cavity Ring Down

We present the development, integration, and testing of an open-path cavity ring-down spectroscopy (CRDS) methane sensor for deployment on small unmanned aerial systems (sUAS). The open-path configuration used here (without pump or flow-cell) enables a low mass (4 kg) and low power (12 W) instrument that can be readily integrated to sUAS, defined here as having all-up mass of <25 kg. The instrument uses a compact telecom style laser at 1651 nm (near-infrared) and a linear 2-mirror high-finesse cavity. We show test results of flying the sensor on a DJI Matrice 600 hexacopter sUAS. The high sensitivity of the CRDS method allows sensitive methane detection with a precision of ~10–30 ppb demonstrated for actual flight conditions. A controlled release setup, where known mass flows are delivered, was used to simulate point-source methane emissions. Examples of methane plume detection from flight tests suggest that isolated plumes from sources with a mass flow as low as ~0.005 g/s can be detected. The sUAS sensor should have utility for emissions monitoring and quantification from natural gas infrastructure. To the best of our knowledge, it is also the first CRDS sensor directly deployed onboard an sUAS.

Thermal Infrared Polarimetric Sensor for Automated Detection of Oil Spills

2017 ◽  
Vol 2017 (1) ◽  
pp. 2017402
Author(s):  
David B. Chenault ◽  
Justin P. Vaden ◽  
Douglas A. Mitchell ◽  
Erik D. Demicco
Keyword(s):  
Early Detection ◽  
Oil Spill ◽  
Oil Spills ◽  
Detection System ◽  
Thermal Infrared ◽  
Automated Detection ◽  
Unmanned Aerial Systems ◽  
Detection Approach ◽  
Thermal Cameras ◽  
Aerial Systems

One of the most effective ways of minimizing oil spill impact is early detection. Effective early detection requires automated detection that relies as little as possible on an operator and can operate 24/7. A new and innovative optical detection system exploits the polarization of light, the same physics used to reduce glare through the use of polarized glasses but in the thermal infrared (TIR) portion of the optical spectrum. Measuring the polarization of thermally emitted radiation from an oil spill enhances the detection over conventional thermal cameras and has the potential to provide automated day / night monitoring and surveillance. The sensors developed thus far are relatively small and inexpensive and can be easily mounted in areas that need monitoring and installed in unmanned aerial systems (UAS). Since the sensor is adapted from a conventional TIR camera, thermal imagery as currently used is collected in addition to the polarimetric imagery to further improve the detection performance. Lens options enable wide area coverage at shorter ranges and higher resolution at longer ranges from the camera position. A TIR Polarimetric camera was tested at Ohmsett to establish performance under a variety of conditions. The Polarimetric camera was tested during the day and at night, under several different wave conditions generated in the wave tank, and with oil of different compositions and thicknesses. The imagery collected was analyzed to establish the contrast improvement through the polarimetric properties of the oil and to assess the automation of the detection process. In this poster, the sensor and test setup will be briefly described with detailed description of the results and the potential of this detection approach for automated detection.

Pipeline Rupture Detection Based on Machine Learning and Pattern Recognition

2016 ◽  
Author(s):  
Martin Di Blasi ◽  
Zhan Li
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Task Force ◽  
Detection System ◽  
Leak Detection ◽  
Guidance Document ◽  
Performance Expectations ◽  
Detection Systems ◽  
Short Period ◽  
Recognition And Response

Pipeline ruptures have the potential to cause significant economic and environmental impact in a short period of time, therefore it is critical for pipeline operators to be able to promptly detect and respond to them. Public stakeholder expectations are high and an evolving expectation is that the response to such events be automated by initiating an automatic pipeline shutdown upon receipt of rupture alarm. These types of performance expectations are challenging to achieve with conventional, model-based, leak-detection systems (i.e. CPM–RTTMs) as the reliability measured in terms of the false alarm rate is typically too low. The company has actively participated on a pipeline-industry task force chaired by the API Cybernetics committee, focused on the development of best practices in the area of Rupture Recognition and Response. After API’s release of the first version of a Rupture Recognition and Response guidance document in 2014, the company has initiated development of its own internal Rupture Recognition Program (RRP). The RRP considers several rupture recognition approaches simultaneously, ranging from improvements to existing CPM leak detection to the development of new SCADA based rupture detection system (RDS). This paper will provide an overview of a specific approach to rupture detection based on the use of machine learning and pattern recognition techniques applied to SCADA data.

scholarly journals Stress Recognition Using Facial Landmarks and Cnn (Alexnet)

2021 ◽  
Vol 2089 (1) ◽  
pp. 012039
Author(s):  
P Ramesh Naidu ◽  
S Pruthvi Sagar ◽  
K Praveen ◽  
K Kiran ◽  
K Khalandar
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Head Movements ◽  
Psychological Disorder ◽  
Cognitive Stress ◽  
Facial Landmarks ◽  
Non Invasive ◽  
Visual Technology ◽  
Machine Learning Model ◽  
Stress Recognition

Abstract Stress is a psychological disorder that affects every aspect of life and diminishes the quality of sleep. The strategy presented in this paper for detecting cognitive stress levels using facial landmarks is successful. The major goal of this system was to employ visual technology to detect stress using a machine learning methodology. The novelty of this work lies in the fact that a stress detection system should be as non-invasive as possible for the user. The user tension and these evidences are modelled using machine learning. The computer vision techniques we utilized to extract visual evidences, the machine learning model we used to forecast stress and related parameters, and the active sensing strategy we used to collect the most valuable evidences for efficient stress inference are all discussed. Our findings show that the stress level identified by our method is accurate is consistent with what psychological theories predict. This presents a stress recognition approach based on facial photos and landmarks utilizing AlexNet architecture in this research. It is vital to have a gadget that can collect the appropriate data. The use of a biological signal or a thermal image to identify stress is currently being investigated. To address this limitation, we devised an algorithm that can detect stress in photos taken with a standard camera. We have created DNN that uses facial positions points as input to take advantage of the fact that when a person is worried their eye, mouth, and head movements differ from what they are used to. The suggested algorithm senses stress more efficiently, according to experimental data.

Soil moisture estimation from high-resolution UASs imagery based on machine learning approaches for land cover classification

2021 ◽  
Author(s):  
Teresa Pizzolla ◽  
Silvano Fortunato Dal Sasso ◽  
Ruodan Zhuang ◽  
Alonso Pizarro ◽  
Salvatore Manfreda
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Thermal Inertia ◽  
Bare Soil ◽  
Unmanned Aerial Systems ◽  
Learning Approaches ◽  
Land Use Classification ◽  
Aerial Systems

&lt;p&gt;Soil moisture (SM) is an essential variable in the earth system as it influences water, energy and, carbon fluxes between the land surface and the atmosphere. The SM spatio-temporal variability requires detailed analyses, high-definition optics and fast computing approaches for near real-time SM estimation at different spatial scales. Remote Sensing-based Unmanned Aerial Systems (UASs) represents the actual solution providing low-cost approaches to meet the requirements of spatial, spectral and temporal resolutions [1; 3; 4]. In this context, a proper land use classification is crucial in order to discriminate the behaviors of vegetation and bare soil in such high-resolution imagery. Therefore, high-resolution UASs-based imagery requires a specific images classification approach also considering the illumination conditions. In this work, the land use classification was carried out using a methodology based on a combined machine learning approaches: k-means clustering algorithm for removing shadow pixels from UASs images and, binary classifier for vegetation filtering. This approach led to identifying the bare soil on which SM estimation was computed using the Apparent Thermal Inertia (ATI) method [2]. The estimated SM values were compared with field measurements obtaining a good correlation (R&lt;sup&gt;2&lt;/sup&gt; = 0.80). The accuracy of the results shows good reliability of the procedure and allows extending the use of UASs also in unclassified areas and ungauged basins, where the monitoring of the SM is very complex.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;[1] Manfreda, S., McCabe, M.F., Miller, P.E., Lucas, R., Pajuelo Madrigal, V., Mallinis, G., Ben Dor, E., Helman, D., Estes, L., Ciraolo, G., et al. On the Use of Unmanned Aerial Systems for Environmental Monitoring, Remote Sensing, 2018, 10, 641.&lt;/p&gt;&lt;p&gt;[2] Minacapilli, M., Cammalleri, C., Ciraolo, G., D&amp;#8217;Asaro, F., Iovino, M., and Maltese, A. Thermal Inertia Modeling for Soil Surface Water Content Estimation: A Laboratory Experiment. Soil. Sci. Soc. Amer. J. 2012, vol.76, n.1, pp. 92&amp;#8211;100&lt;/p&gt;&lt;p&gt;[3] Paruta, A., P. Nasta, G. Ciraolo, F. Capodici, S. Manfreda, N. Romano, E. Bendor, Y. Zeng, A. Maltese, S. F. Dal Sasso and R. Zhuang, A geostatistical approach to map near-surface soil moisture through hyper-spatial resolution thermal inertia, IEEE Transactions on Geoscience and Remote Sensing, 2020.&lt;/p&gt;&lt;p&gt;[4] Petropoulos, G.P., A. Maltese, T. N. Carlson, G. Provenzano, A. Pavlides, G. Ciraolo, D. Hristopulos, F. Capodici, C. Chalkias, G. Dardanelli, S. Manfreda, Exploring the use of UAVs with the simplified &amp;#8220;triangle&amp;#8221; technique for Soil Water Content and Evaporative Fraction retrievals in a Mediterranean setting, International Journal of Remote Sensing, 2020.&lt;/p&gt;

scholarly journals Classification of Smoke Contaminated Cabernet Sauvignon Berries and Leaves Based on Chemical Fingerprinting and Machine Learning Algorithms

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5099
Author(s):  
Vasiliki Summerson ◽  
Claudia Gonzalez Viejo ◽  
Colleen Szeto ◽  
Kerry L. Wilkinson ◽  
Damir D. Torrico ◽  
...  
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Detection System ◽  
Nir Spectroscopy ◽  
Machine Learning Algorithms ◽  
Anthocyanin Content ◽  
Sample Collection ◽  
Neural Network Models ◽  
Field Detection ◽  
Chemical Fingerprinting

Wildfires are an increasing problem worldwide, with their number and intensity predicted to rise due to climate change. When fires occur close to vineyards, this can result in grapevine smoke contamination and, subsequently, the development of smoke taint in wine. Currently, there are no in-field detection systems that growers can use to assess whether their grapevines have been contaminated by smoke. This study evaluated the use of near-infrared (NIR) spectroscopy as a chemical fingerprinting tool, coupled with machine learning, to create a rapid, non-destructive in-field detection system for assessing grapevine smoke contamination. Two artificial neural network models were developed using grapevine leaf spectra (Model 1) and grape spectra (Model 2) as inputs, and smoke treatments as targets. Both models displayed high overall accuracies in classifying the spectral readings according to the smoking treatments (Model 1: 98.00%; Model 2: 97.40%). Ultraviolet to visible spectroscopy was also used to assess the physiological performance and senescence of leaves, and the degree of ripening and anthocyanin content of grapes. The results showed that chemical fingerprinting and machine learning might offer a rapid, in-field detection system for grapevine smoke contamination that will enable growers to make timely decisions following a bushfire event, e.g., avoiding harvest of heavily contaminated grapes for winemaking or assisting with a sample collection of grapes for chemical analysis of smoke taint markers.

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