scholarly journals Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Etienne David ◽  
Simon Madec ◽  
Pouria Sadeghi-Tehran ◽  
Helge Aasen ◽  
Bangyou Zheng ◽  
...  
Keyword(s):  
High Resolution ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Head Orientation ◽  
Growth Stages ◽  
Maturity Stage ◽  
Genotypic Differences ◽  
Wide Range ◽  
Head Detection ◽  
Fair Principles

The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health, size, maturity stage, and the presence of awns. Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms. However, these methods have generally been calibrated and validated on limited datasets. High variability in observational conditions, genotypic differences, development stages, and head orientation makes wheat head detection a challenge for computer vision. Further, possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex. Through a joint international collaborative effort, we have built a large, diverse, and well-labelled dataset of wheat images, called the Global Wheat Head Detection (GWHD) dataset. It contains 4700 high-resolution RGB images and 190000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes. Guidelines for image acquisition, associating minimum metadata to respect FAIR principles, and consistent head labelling methods are proposed when developing new head detection datasets. The GWHD dataset is publicly available at http://www.global-wheat.com/and aimed at developing and benchmarking methods for wheat head detection.

scholarly journals Early Detection of Invasive Exotic Trees Using UAV and Manned Aircraft Multispectral and LiDAR Data

2019 ◽  
Vol 11 (15) ◽  
pp. 1812 ◽  
Author(s):  
Jonathan P. Dash ◽  
Michael S. Watt ◽  
Thomas S. H. Paul ◽  
Justin Morgenroth ◽  
Grant D. Pearse
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Laser Scanning ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Validation Dataset ◽  
Height Range ◽  
Exotic Trees ◽  
Invasive Trees ◽  
Invasive Exotic

Exotic conifers can provide significant ecosystem services, but in some environments, they have become invasive and threaten indigenous ecosystems. In New Zealand, this phenomenon is of considerable concern as the area occupied by invasive exotic trees is large and increasing rapidly. Remote sensing methods offer a potential means of identifying and monitoring land infested by these trees, enabling managers to efficiently allocate resources for their control. In this study, we sought to develop methods for remote detection of exotic invasive trees, namely Pinus sylvestris and P. ponderosa. Critically, the study aimed to detect these species prior to the onset of maturity and coning as this is important for preventing further spread. In the study environment in New Zealand’s South Island, these species reach maturity and begin bearing cones at a young age. As such, detection of these smaller individuals requires specialist methods and very high-resolution remote sensing data. We examined the efficacy of classifiers developed using two machine learning algorithms with multispectral and laser scanning data collected from two platforms—manned aircraft and unmanned aerial vehicles (UAV). The study focused on a localized conifer invasion originating from a multi-species pine shelter belt in a grassland environment. This environment provided a useful means of defining the detection thresholds of the methods and technologies employed. An extensive field dataset including over 17,000 trees (height range = 1 cm to 476 cm) was used as an independent validation dataset for the detection methods developed. We found that data from both platforms and using both logistic regression and random forests for classification provided highly accurate (kappa < 0.996 ) detection of invasive conifers. Our analysis showed that the data from both UAV and manned aircraft was useful for detecting trees down to 1 m in height and therefore shorter than 99.3% of the coning individuals in the study dataset. We also explored the relative contribution of both multispectral and airborne laser scanning (ALS) data in the detection of invasive trees through fitting classification models with different combinations of predictors and found that the most useful models included data from both sensors. However, the combination of ALS and multispectral data did not significantly improve classification accuracy. We believe that this was due to the simplistic vegetation and terrain structure in the study site that resulted in uncomplicated separability of invasive conifers from other vegetation. This study provides valuable new knowledge of the efficacy of detecting invasive conifers prior to the onset of coning using high-resolution data from UAV and manned aircraft. This will be an important tool in managing the spread of these important invasive plants.

scholarly journals Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning

Biosensors ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 193
Author(s):  
Alanna V. Zubler ◽  
Jeong-Yeol Yoon
Keyword(s):  
Machine Learning ◽  
Optical Sensors ◽  
Near Infrared ◽  
Plant Stress ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Stress Detection ◽  
Recent Advances ◽  
Wide Range

Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the visible (red-green-blue or RGB), near-infrared (NIR), infrared (IR), and ultraviolet (UV) wavebands, often augmented by fluorescence imaging or fluorescence spectrometry. Imaging at multiple specific wavelengths (multi-spectral imaging) or across a wide range of wavelengths (hyperspectral imaging) can provide exceptional information on plant stress and subsequent diseases. Digital cameras, thermal cameras, and optical filters have become available at a low cost in recent years, while hyperspectral cameras have become increasingly more compact and portable. Furthermore, smartphone cameras have dramatically improved in quality, making them a viable option for rapid, on-site stress detection. Due to these developments in imaging technology, plant stresses can be monitored more easily using handheld and field-deployable methods. Recent advances in machine learning algorithms have allowed for images and spectra to be analyzed and classified in a fully automated and reproducible manner, without the need for complicated image or spectrum analysis methods. This review will highlight recent advances in portable (including smartphone-based) detection methods for biotic and abiotic stresses, discuss data processing and machine learning techniques that can produce results for stress identification and classification, and suggest future directions towards the successful translation of these methods into practical use.

scholarly journals A Method for Wheat Head Detection Based on Yolov4

2020 ◽  
Author(s):  
Bo Gong ◽  
Daji Ergu ◽  
Ying Cai ◽  
Bo Ma
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Detection Method ◽  
Computer Hardware ◽  
Detection Methods ◽  
Plant Phenotyping ◽  
Wide Range ◽  
Head Detection ◽  
Multi Level

Abstract Background: Plant phenotyping by deep learning has increased attention. The detection of wheat head in the field is an important mission for estimating the characteristics of wheat heads such as the density, health, maturity, and presence or absence of awns. Traditional wheat head detection methods have problems such as low efficiency, strong subjectivity, and poor accuracy. However, with the development of deep learning theory and the iteration of computer hardware, the accuracy of object detection method using deep neural networks has been greatly improved. Therefore, using a deep neural network method to detect wheat heads in images has a certain value. Results: In this paper, a method of wheat head detection based on deep neural network is proposed. Firstly, for improving the backbone network part, two SPP networks are introduced to enhance the ability of feature learning and increase the receptive field of the convolutional network. Secondly, the top-down and bottom-up feature fusion strategies are applied to obtain multi-level features. Finally, we use Yolov3's head structures to predict the bounding box of object. The results show that our proposed detection method for wheat head has higher accuracy and speed. The mean average precision of our method is 94.5%, and the detection speed of our proposed method is 88fps. Conclusion: The proposed deep neural network can accurately and quickly detector the wheat head in the image which is based on Yolov4. In addition, the training dataset is a wheat head dataset with accurate annotations and rich varieties, which makes the proposed method more robust and has a wide range of application values. The proposed detector is also more suitable for wheat detection task, with the deeper backbone networks. The use of spatial pyramid pooling (SPP) and multi-level features fusion, which all play a crucial role in improving detector performance. Our method provides beneficial help for the breeding of wheat

scholarly journals Time Series Segmentation Based on Stationarity Analysis to Improve New Samples Prediction

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7333
Author(s):  
Ricardo Petri Silva ◽  
Bruno Bogaz Zarpelão ◽  
Alberto Cano ◽  
Sylvio Barbon Junior
Keyword(s):  
Time Series ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Sequential Data ◽  
Short Term ◽  
Term Memory ◽  
Wide Range ◽  
Long Short Term Memory ◽  
Adf Test

A wide range of applications based on sequential data, named time series, have become increasingly popular in recent years, mainly those based on the Internet of Things (IoT). Several different machine learning algorithms exploit the patterns extracted from sequential data to support multiple tasks. However, this data can suffer from unreliable readings that can lead to low accuracy models due to the low-quality training sets available. Detecting the change point between high representative segments is an important ally to find and thread biased subsequences. By constructing a framework based on the Augmented Dickey-Fuller (ADF) test for data stationarity, two proposals to automatically segment subsequences in a time series were developed. The former proposal, called Change Detector segmentation, relies on change detection methods of data stream mining. The latter, called ADF-based segmentation, is constructed on a new change detector derived from the ADF test only. Experiments over real-file IoT databases and benchmarks showed the improvement provided by our proposals for prediction tasks with traditional Autoregressive integrated moving average (ARIMA) and Deep Learning (Long short-term memory and Temporal Convolutional Networks) methods. Results obtained by the Long short-term memory predictive model reduced the relative prediction error from 1 to 0.67, compared to time series without segmentation.

Development of a conical anode Fe-gun for low voltage SEM

1988 ◽  
Vol 46 ◽  
pp. 978-979
Author(s):  
T. Miyokawa ◽  
S. Norioka ◽  
S. Goto
Keyword(s):  
High Resolution ◽  
Field Emission ◽  
Low Voltage ◽  
Irradiation Damage ◽  
Cold Cathode ◽  
Virtual Source ◽  
Source Position ◽  
Electrostatic Lens ◽  
Electrostatic Lenses ◽  
Wide Range

Field emission SEMs (FE-SEMs) are becoming popular due to their high resolution needs. In the field of semiconductor product, it is demanded to use the low accelerating voltage FE-SEM to avoid the electron irradiation damage and the electron charging up on samples. However the accelerating voltage of usual SEM with FE-gun is limited until 1 kV, which is not enough small for the present demands, because the virtual source goes far from the tip in lower accelerating voltages. This virtual source position depends on the shape of the electrostatic lens. So, we investigated several types of electrostatic lenses to be applicable to the lower accelerating voltage. In the result, it is found a field emission gun with a conical anode is effectively applied for a wide range of low accelerating voltages.A field emission gun usually consists of a field emission tip (cold cathode) and the Butler type electrostatic lens.

Three-fold astigmatism: An important TEM aberration

1993 ◽  
Vol 51 ◽  
pp. 972-973 ◽  
Author(s):  
O.L. Krivanek ◽  
M.L. Leber
Keyword(s):  
High Resolution ◽  
Spatial Frequency ◽  
Field Emission ◽  
Azimuthal Angle ◽  
Hollow Cone ◽  
Small Probe ◽  
Wide Range ◽  
High Resolution Images ◽  
Image Position

Three-fold astigmatism resembles regular astigmatism, but it has 3-fold rather than 2-fold symmetry. Its contribution to the aberration function χ(q) can be written as:where A3 is the coefficient of 3-fold astigmatism, λ is the electron wavelength, q is the spatial frequency, ϕ the azimuthal angle (ϕ = tan-1 (qy/qx)), and ϕ3 the direction of the astigmatism.Three-fold astigmatism is responsible for the “star of Mercedes” aberration figure that one obtains from intermediate lenses once their two-fold astigmatism has been corrected. Its effects have been observed when the beam is tilted in a hollow cone over a wide range of angles, and there is evidence for it in high resolution images of a small probe obtained in a field emission gun TEM/STEM instrument. It was also expected to be a major aberration in sextupole-based Cs correctors, and ways were being developed for dealing with it on Cs-corrected STEMs.

Scanning tunneling microscopy of polymers: A status report

1989 ◽  
Vol 47 ◽  
pp. 330-331
Author(s):  
P.E. Russell ◽  
I.H. Musselman
Keyword(s):  
High Resolution ◽  
Scanning Tunneling Microscopy ◽  
Sample Surface ◽  
Atomic Scale ◽  
Constant Current ◽  
Scanning Tunneling ◽  
Status Report ◽  
Tunneling Microscopy ◽  
Research Issues ◽  
Wide Range

Scanning tunneling microscopy (STM) has evolved rapidly in the past few years. Major developments have occurred in instrumentation, theory, and in a wide range of applications. In this paper, an overview of the application of STM and related techniques to polymers will be given, followed by a discussion of current research issues and prospects for future developments. The application of STM to polymers can be conveniently divided into the following subject areas: atomic scale imaging of uncoated polymer structures; topographic imaging and metrology of man-made polymer structures; and modification of polymer structures. Since many polymers are poor electrical conductors and hence unsuitable for use as a tunneling electrode, the related atomic force microscopy (AFM) technique which is capable of imaging both conductors and insulators has also been applied to polymers.The STM is well known for its high resolution capabilities in the x, y and z axes (Å in x andy and sub-Å in z). In addition to high resolution capabilities, the STM technique provides true three dimensional information in the constant current mode. In this mode, the STM tip is held at a fixed tunneling current (and a fixed bias voltage) and hence a fixed height above the sample surface while scanning across the sample surface.

RepPer: Perception of Psychiatric Disorders on Twitter in French (Preprint)

2020 ◽  
Author(s):  
Sarah Delanys ◽  
Farah Benamara ◽  
Véronique Moriceau ◽  
François Olivier ◽  
Josiane Mothe
Keyword(s):  
Social Media ◽  
Psychiatric Disorders ◽  
Digital Technology ◽  
Psychotic Disorders ◽  
Negative Polarity ◽  
Machine Learning Algorithms ◽  
Annotation Scheme ◽  
Word Use ◽  
Wide Range ◽  
Initial Dataset

BACKGROUND With the advent of digital technology and specifically user generated contents in social media, new ways emerged for studying possible stigma of people in relation with mental health. Several pieces of work studied the discourse conveyed about psychiatric pathologies on Twitter considering mostly tweets in English and a limited number of psychiatric disorders terms. This paper proposes the first study to analyze the use of a wide range of psychiatric terms in tweets in French. OBJECTIVE Our aim is to study how generic, nosographic and therapeutic psychiatric terms are used on Twitter in French. More specifically, our study has three complementary goals: (1) to analyze the types of psychiatric word use namely medical, misuse, irrelevant, (2) to analyze the polarity conveyed in the tweets that use these terms (positive/negative/neural), and (3) to compare the frequency of these terms to those observed in related work (mainly in English ). METHODS Our study has been conducted on a corpus of tweets in French posted between 01/01/2016 to 12/31/2018 and collected using dedicated keywords. The corpus has been manually annotated by clinical psychiatrists following a multilayer annotation scheme that includes the type of word use and the opinion orientation of the tweet. Two analysis have been performed. First a qualitative analysis to measure the reliability of the produced manual annotation, then a quantitative analysis considering mainly term frequency in each layer and exploring the interactions between them. RESULTS One of the first result is a resource as an annotated dataset . The initial dataset is composed of 22,579 tweets in French containing at least one of the selected psychiatric terms. From this set, experts in psychiatry randomly annotated 3,040 tweets that corresponds to the resource resulting from our work. The second result is the analysis of the annotations; it shows that terms are misused in 45.3% of the tweets and that their associated polarity is negative in 86.2% of the cases. When considering the three types of term use, 59.5% of the tweets are associated to a negative polarity. Misused terms related to psychotic disorders (55.5%) are more frequent to those related to mood disorders (26.5%). CONCLUSIONS Some psychiatric terms are misused in the corpora we studied; which is consistent with the results reported in related work in other languages. Thanks to the great diversity of studied terms, this work highlighted a disparity in the representations and ways of using psychiatric terms. Moreover, our study is important to help psychiatrists to be aware of the term use in new communication media such as social networks which are widely used. This study has the huge advantage to be reproducible thanks to the framework and guidelines we produced; so that the study could be renewed in order to analyze the evolution of term usage. While the newly build dataset is a valuable resource for other analytical studies, it could also serve to train machine learning algorithms to automatically identify stigma in social media.

scholarly journals Detectivity optimization to detect of ultraweak light fluxes with an EM-CCD as binary photon counter array

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ibtissame Khaoua ◽  
Guillaume Graciani ◽  
Andrey Kim ◽  
François Amblard
Keyword(s):  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Photon Counting ◽  
Detection Methods ◽  
Strong Nonlinearity ◽  
Wide Range ◽  
Depth Analysis ◽  
Spatially Extended ◽  
Extended Sources ◽  
Photon Counting Mode

AbstractFor a wide range of purposes, one faces the challenge to detect light from extremely faint and spatially extended sources. In such cases, detector noises dominate over the photon noise of the source, and quantum detectors in photon counting mode are generally the best option. Here, we combine a statistical model with an in-depth analysis of detector noises and calibration experiments, and we show that visible light can be detected with an electron-multiplying charge-coupled devices (EM-CCD) with a signal-to-noise ratio (SNR) of 3 for fluxes less than $$30\,{\text{photon}}\,{\text{s}}^{ - 1} \,{\text{cm}}^{ - 2}$$ 30 photon s - 1 cm - 2 . For green photons, this corresponds to 12 aW $${\text{cm}}^{ - 2}$$ cm - 2 ≈ $$9{ } \times 10^{ - 11}$$ 9 × 10 - 11 lux, i.e. 15 orders of magnitude less than typical daylight. The strong nonlinearity of the SNR with the sampling time leads to a dynamic range of detection of 4 orders of magnitude. To detect possibly varying light fluxes, we operate in conditions of maximal detectivity $${\mathcal{D}}$$ D rather than maximal SNR. Given the quantum efficiency $$QE\left( \lambda \right)$$ Q E λ of the detector, we find $${ \mathcal{D}} = 0.015\,{\text{photon}}^{ - 1} \,{\text{s}}^{1/2} \,{\text{cm}}$$ D = 0.015 photon - 1 s 1 / 2 cm , and a non-negligible sensitivity to blackbody radiation for T > 50 °C. This work should help design highly sensitive luminescence detection methods and develop experiments to explore dynamic phenomena involving ultra-weak luminescence in biology, chemistry, and material sciences.

scholarly journals Defects and uncertainties of adhesively bonded composite joints

2021 ◽  
Vol 3 (9) ◽  
Author(s):  
Sadik Omairey ◽  
Nithin Jayasree ◽  
Mihalis Kazilas
Keyword(s):  
Composite Materials ◽  
Composite Structures ◽  
Production Efficiency ◽  
Adhesive Bonding ◽  
Detection Methods ◽  
Bonded Joints ◽  
Adhesively Bonded ◽  
Adhesively Bonded Joints ◽  
Wide Range ◽  
Bonded Composite

AbstractThe increasing use of fibre reinforced polymer composite materials in a wide range of applications increases the use of similar and dissimilar joints. Traditional joining methods such as welding, mechanical fastening and riveting are challenging in composites due to their material properties, heterogeneous nature, and layup configuration. Adhesive bonding allows flexibility in materials selection and offers improved production efficiency from product design and manufacture to final assembly, enabling cost reduction. However, the performance of adhesively bonded composite structures cannot be fully verified by inspection and testing due to the unforeseen nature of defects and manufacturing uncertainties presented in this joining method. These uncertainties can manifest as kissing bonds, porosity and voids in the adhesive. As a result, the use of adhesively bonded joints is often constrained by conservative certification requirements, limiting the potential of composite materials in weight reduction, cost-saving, and performance. There is a need to identify these uncertainties and understand their effect when designing these adhesively bonded joints. This article aims to report and categorise these uncertainties, offering the reader a reliable and inclusive source to conduct further research, such as the development of probabilistic reliability-based design optimisation, sensitivity analysis, defect detection methods and process development.

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