scholarly journals Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Sung-Wook Hwang ◽  
Junji Sugiyama
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Research Field ◽  
Machine Learning Techniques ◽  
Wood Identification ◽  
Wood Industry ◽  
Scientific Disciplines ◽  
Learning Techniques ◽  
New Research ◽  
Wood Science

AbstractThe remarkable developments in computer vision and machine learning have changed the methodologies of many scientific disciplines. They have also created a new research field in wood science called computer vision-based wood identification, which is making steady progress towards the goal of building automated wood identification systems to meet the needs of the wood industry and market. Nevertheless, computer vision-based wood identification is still only a small area in wood science and is still unfamiliar to many wood anatomists. To familiarize wood scientists with the artificial intelligence-assisted wood anatomy and engineering methods, we have reviewed the published mainstream studies that used or developed machine learning procedures. This review could help researchers understand computer vision and machine learning techniques for wood identification and choose appropriate techniques or strategies for their study objectives in wood science.

scholarly journals Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4776
Author(s):  
Seyed Mahdi Miraftabzadeh ◽  
Michela Longo ◽  
Federica Foiadelli ◽  
Marco Pasetti ◽  
Raul Igual
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Smart Grids ◽  
State Of The Art ◽  
Smart Cities ◽  
Power Grids ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
New Research ◽  
Traditional Approaches

The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models.

Drought Stress Detection Using Low-Cost Computer Vision Systems and Machine Learning Techniques

2020 ◽  
Vol 22 (3) ◽  
pp. 27-29 ◽  
Author(s):  
Paula Ramos-Giraldo ◽  
Chris Reberg-Horton ◽  
Anna M. Locke ◽  
Steven Mirsky ◽  
Edgar Lobaton
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Drought Stress ◽  
Low Cost ◽  
Machine Learning Techniques ◽  
Stress Detection ◽  
Vision Systems ◽  
Learning Techniques ◽  
Computer Vision Systems

scholarly journals Recognition of Soybean Diseases Using Machine Learning Techniques Based on Segmentation of Images Captured By UAVs

2020 ◽  
Author(s):  
Gercina Da Silva ◽  
Alessandro Ferreira ◽  
Denilson Guilherme ◽  
José Fernando Grigolli ◽  
Vanessa Weber ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Computer Program ◽  
Random Forests ◽  
Machine Learning Techniques ◽  
Target Spot ◽  
Learning Techniques ◽  
Image Dataset ◽  
Segmentation Of Images ◽  
Soybean Diseases

Soybean is an important product for the Brazilian economy, however it has factors that can limit its productive income, like the diseases that are generally difficult to control. Thus, this article aims to use a computer program to recognize diseases in images obtained by a UAV in a soybean plantation. The program is based on computer vision and machine learning, using the SLIC algorithm to segment the images into superpixels. To achieve the objective, after the segmentation of the images, an image dataset was created with the following classes: mildew, target spot, Asian rust, soil, straw and healthy leaves, totaling 22,140 images. Diagrammatic scales were used to assess disease severity. The disease recognition computer program explored four supervised learning techniques: SVM, J48, Random Forest and KNN. The techniques that obtained the best performance were SVM and Random Forests, taking into account the results obtained with all the evaluation metrics used. It was found that the program is efficient to differentiate the classes of diseases treated in this article.

scholarly journals Non Verbal Communication for Analyzing the Malpractice in Examinations– An Overview

2018 ◽  
Vol 7 (2.20) ◽  
pp. 227
Author(s):  
Kujani T ◽  
Sathya T ◽  
Bhuvanya R ◽  
Uma S
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Interpersonal Communication ◽  
Nonverbal Communication ◽  
Current Situation ◽  
Verbal Communication ◽  
Machine Learning Techniques ◽  
The People ◽  
Learning Techniques

Nonverbal communication can specify the psychosomatic behavior of people involved in interpersonal communication. Many researchers have specified the importance of gesture intimation. In the paper, we shall apply the previously used computer vision hardware and Machine Learning techniques for capturing the postures students undergoing examination in the classroom atmosphere. The main       intention is to classify the people who involved in misbehavior such as copying, prompting answers, sharing the answer scripts and any other such practices. Current situation prevailing is though a physical monitor, invigilator is available in the exam hall the students     attempt to misbehave in various ways mentioned above. We discuss about the techniques to be employed to get an analysis of the       behavior of each student involved in exam. 

A Scientometric Analysis and a Review on Current Literature of Computer Vision Applications

2020 ◽  
pp. 104-117
Author(s):  
Osman Hürol Türkakın
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Computer Vision ◽  
Current Literature ◽  
Machine Learning Techniques ◽  
Scientometric Analysis ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Computer Vision Applications ◽  
Processing Techniques

Computer vision methods are wide-spread techniques mostly used for detecting cracks on structural components, extracting information from traffic flows, and analyzing safety in construction processes. In recent years, with increasing usage of machine learning techniques, computer vision applications are supported by machine learning approaches. So, several studies were conducted using machine learning techniques to apply image processing. As a result, this chapter offers a scientometric analysis for investigating current literature of image processing studies for civil engineering field in order to track the scientometric relationship between machine learning and image processing techniques.

Sign in / Sign up

Export Citation Format

Share Document