Plants Species Identification Using Computer Vision Techniques = Identification des Especes de Plante a l'Aide de Techniques de Vision par Ordinateur

2017 ◽  
Vol 7 (1) ◽  
pp. 113-120
Author(s):  
O. Aiadi ◽  
M. L. Kherfi ◽  
L. Hamrouni
Keyword(s):  
Computer Vision ◽  
Species Identification

Image based species identification of Globodera quarantine nematodes using computer vision and deep learning

2021 ◽  
pp. 106058
Author(s):  
Romain Thevenoux ◽  
Van Linh LE ◽  
Heloïse Villessèche ◽  
Alain Buisson ◽  
Marie Beurton-Aimar ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Species Identification

LeafNet: A computer vision system for automatic plant species identification

2017 ◽  
Vol 40 ◽  
pp. 50-56 ◽  
Author(s):  
Pierre Barré ◽  
Ben C. Stöver ◽  
Kai F. Müller ◽  
Volker Steinhage
Keyword(s):  
Computer Vision ◽  
Species Identification ◽  
Plant Species ◽  
Vision System ◽  
Computer Vision System ◽  
Plant Species Identification

scholarly journals Computer-assisted timber identification based on features extracted from microscopic wood sections

IAWA Journal ◽  
2020 ◽  
Vol 41 (4) ◽  
pp. 660-680 ◽  
Author(s):  
Frederic Lens ◽  
Chao Liang ◽  
Yuanhao Guo ◽  
Xiaoqin Tang ◽  
Mehrdad Jahanbanifard ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Species Identification ◽  
Wood Anatomy ◽  
Reference Database ◽  
Computer Assisted ◽  
Correct Identification ◽  
Timber Species ◽  
Exploratory Research ◽  
Neotropical Tree

Abstract Wood anatomy is one of the most important methods for timber identification. However, training wood anatomy experts is time-consuming, while at the same time the number of senior wood anatomists with broad taxonomic expertise is declining. Therefore, we want to explore how a more automated, computer-assisted approach can support accurate wood identification based on microscopic wood anatomy. For our exploratory research, we used an available image dataset that has been applied in several computer vision studies, consisting of 112 — mainly neotropical — tree species representing 20 images of transverse sections for each species. Our study aims to review existing computer vision methods and compare the success of species identification based on (1) several image classifiers based on manually adjusted texture features, and (2) a state-of-the-art approach for image classification based on deep learning, more specifically Convolutional Neural Networks (CNNs). In support of previous studies, a considerable increase of the correct identification is accomplished using deep learning, leading to an accuracy rate up to 95.6%. This remarkably high success rate highlights the fundamental potential of wood anatomy in species identification and motivates us to expand the existing database to an extensive, worldwide reference database with transverse and tangential microscopic images from the most traded timber species and their look-a-likes. This global reference database could serve as a valuable future tool for stakeholders involved in combatting illegal logging and would boost the societal value of wood anatomy along with its collections and experts.

scholarly journals Analysis of color feature extraction techniques for Fish Species Identification

2020 ◽  
Author(s):  
Uéliton Freitas ◽  
Marcio Pache ◽  
Wesley Gonçalves ◽  
Edson Matsubara ◽  
José Sabino ◽  
...  
Keyword(s):  
Computer Vision ◽  
Feature Extraction ◽  
Species Identification ◽  
Fish Species ◽  
Good Alternative ◽  
Support Vector ◽  
Extraction Techniques ◽  
K Nearest Neighbors ◽  
Visual Words ◽  
Fish Species Identification

Color recognition is an important step for computer vision to be able to recognize objects in the most different environmental conditions. Classifying objects by color using computer vision is a good alternative for different color conditions such as the aquarium. In which it is possible to use resources of a smartphone with real-time image classification applications. This paper presents some experimental results regarding the use of five different feature extraction techniques to the problem of fish species identification. The feature extractors tested are the Bag of Visual Words (BoVW), the Bag of Colors (BoC), the Bag of Features and Colors (BoFC), the Bag of Colored Words (BoCW), and the histograms HSV and RGB color spaces. The experiments were performed using a dataset, which is also a contribution of this work, containing 1120 images from fishes of 28 different species. The feature extractors were tested under three different supervised learning setups based on Decision Trees, K-Nearest Neighbors, and Support Vector Machine. From the attribute extraction techniques described, the best performance was BoC using the Support Vector Machines as a classifier with an FMeasure of 0.90 and AUC of 0.983348 with a dictionary size of 2048.

scholarly journals An Overview of Computer Vision in iNaturalist

2020 ◽  
Vol 4 ◽  
Author(s):  
Ken-ichi Ueda
Keyword(s):  
Computer Vision ◽  
Social Network ◽  
Species Identification ◽  
The Future

iNaturalist is a social network of people who record and share observations of biodiversity. For several years, iNaturalist has been employing computer vision models trained on iNaturalist data to provide automated species identification assistance to iNaturalist participants. This presentation offers an overview of how we are using this technology, the data and tools we used to create it, challenges we have faced in its development, and ways we might apply it in the future.

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