scholarly journals A Computer Vision System for the Automatic Classification of Five Varieties of Tree Leaf Images

Computers ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 6 ◽  
Author(s):  
Sajad Sabzi ◽  
Razieh Pourdarbani ◽  
Juan Ignacio Arribas
Keyword(s):  
Neural Network ◽  
Vision System ◽  
Eucalyptus Camaldulensis ◽  
Prunus Armeniaca ◽  
Malus Pumila ◽  
Computer Vision System ◽  
Cydonia Oblonga ◽  
River Red Gum ◽  
Hybrid Artificial Neural Network

A computer vision system for automatic recognition and classification of five varieties of plant leaves under controlled laboratory imaging conditions, comprising: 1–Cydonia oblonga (quince), 2–Eucalyptus camaldulensis dehn (river red gum), 3–Malus pumila (apple), 4–Pistacia atlantica (mt. Atlas mastic tree) and 5–Prunus armeniaca (apricot), is proposed. 516 tree leaves images were taken and 285 features computed from each object including shape features, color features, texture features based on the gray level co-occurrence matrix, texture descriptors based on histogram and moment invariants. Seven discriminant features were selected and input for classification purposes using three classifiers: hybrid artificial neural network–ant bee colony (ANN–ABC), hybrid artificial neural network–biogeography based optimization (ANN–BBO) and Fisher linear discriminant analysis (LDA). Mean correct classification rates (CCR), resulted in 94.04%, 89.23%, and 93.99%, for hybrid ANN–ABC; hybrid ANN–BBO; and LDA classifiers, respectively. Best classifier mean area under curve (AUC), mean sensitivity, and mean specificity, were computed for the five tree varieties under study, resulting in: 1–Cydonia oblonga (quince) 0.991 (ANN–ABC), 95.89% (ANN–ABC), 95.91% (ANN–ABC); 2–Eucalyptus camaldulensis dehn (river red gum) 1.00 (LDA), 100% (LDA), 100% (LDA); 3–Malus pumila (apple) 0.996 (LDA), 96.63% (LDA), 94.99% (LDA); 4–Pistacia atlantica (mt. Atlas mastic tree) 0.979 (LDA), 91.71% (LDA), 82.57% (LDA); and 5–Prunus armeniaca (apricot) 0.994 (LDA), 88.67% (LDA), 94.65% (LDA), respectively.

scholarly journals A Computer Vision System Based on Majority-Voting Ensemble Neural Network for the Automatic Classification of Three Chickpea Varieties

Foods ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 113 ◽  
Author(s):  
Razieh Pourdarbani ◽  
Sajad Sabzi ◽  
Davood Kalantari ◽  
José Luis Hernández-Hernández ◽  
Juan Ignacio Arribas
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Artificial Neural Network ◽  
Vision System ◽  
Color Space ◽  
Majority Voting ◽  
Hsi Color Space ◽  
Artificial Neural ◽  
Hybrid Artificial Neural Network

Since different varieties of crops have specific applications, it is therefore important to properly identify each cultivar, in order to avoid fake varieties being sold as genuine, i.e., fraud. Despite that properly trained human experts might accurately identify and classify crop varieties, computer vision systems are needed since conditions such as fatigue, reproducibility, and so on, can influence the expert’s judgment and assessment. Chickpea (Cicer arietinum L.) is an important legume at the world-level and has several varieties. Three chickpea varieties with a rather similar visual appearance were studied here: Adel, Arman, and Azad chickpeas. The purpose of this paper is to present a computer vision system for the automatic classification of those chickpea varieties. First, segmentation was performed using an Hue Saturation Intensity (HSI) color space threshold. Next, color and textural (from the gray level co-occurrence matrix, GLCM) properties (features) were extracted from the chickpea sample images. Then, using the hybrid artificial neural network-cultural algorithm (ANN-CA), the sub-optimal combination of the five most effective properties (mean of the RGB color space components, mean of the HSI color space components, entropy of GLCM matrix at 90°, standard deviation of GLCM matrix at 0°, and mean third component in YCbCr color space) were selected as discriminant features. Finally, an ANN-PSO/ACO/HS majority voting (MV) ensemble methodology merging three different classifier outputs, namely the hybrid artificial neural network-particle swarm optimization (ANN-PSO), hybrid artificial neural network-ant colony optimization (ANN-ACO), and hybrid artificial neural network-harmonic search (ANN-HS), was used. Results showed that the ensemble ANN-PSO/ACO/HS-MV classifier approach reached an average classification accuracy of 99.10 ± 0.75% over the test set, after averaging 1000 random iterations.

scholarly journals Application of Artificial Intelligence in Food Industry—a Guideline

2021 ◽  
Author(s):  
Nidhi Rajesh Mavani ◽  
Jarinah Mohd Ali ◽  
Suhaili Othman ◽  
M. A. Hussain ◽  
Haslaniza Hashim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Systems ◽  
Food Industry ◽  
Near Infrared ◽  
Vision System ◽  
Electronic Tongue ◽  
World Population ◽  
Computer Vision System ◽  
The Past

AbstractArtificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.

scholarly journals Study and classification of the Crystallographic Orientation Distribution Function of a non-grain oriented electrical steel using computer vision system

2019 ◽  
Vol 8 (1) ◽  
pp. 1070-1083
Author(s):  
Roberto Fernandes Ivo ◽  
Douglas de Araújo Rodrigues ◽  
José Ciro dos Santos ◽  
Francisco Nélio Costa Freitas ◽  
Luis Flaávio Gaspar Herculano ◽  
...  
Keyword(s):  
Computer Vision ◽  
Distribution Function ◽  
Orientation Distribution Function ◽  
Crystallographic Orientation ◽  
Electrical Steel ◽  
Vision System ◽  
Orientation Distribution ◽  
Computer Vision System

A computer vision system for the detection and classification of vehicles at urban road intersections

2005 ◽  
Vol 8 (1-2) ◽  
pp. 17-31 ◽  
Author(s):  
Stefano Messelodi ◽  
Carla Maria Modena ◽  
Michele Zanin
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Computer Vision System ◽  
Urban Road ◽  
Road Intersections
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