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.

Computer vision system and near-infrared spectroscopy for identification and classification of chicken with wooden breast, and physicochemical and technological characterization

2019 ◽  
Vol 96 ◽  
pp. 303-310 ◽  
Author(s):  
Bruna Caroline Geronimo ◽  
Saulo Martiello Mastelini ◽  
Rafael Humberto Carvalho ◽  
Sylvio Barbon Júnior ◽  
Douglas Fernandes Barbin ◽  
...  
Keyword(s):  
Computer Vision ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Vision System ◽  
Computer Vision System ◽  
Wooden Breast

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

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 Update of fNIRS as an Input to Brain–Computer Interfaces: A Review of Research from the Tufts Human–Computer Interaction Laboratory

Photonics ◽  
2019 ◽  
Vol 6 (3) ◽  
pp. 90 ◽  
Author(s):  
Bosworth ◽  
Russell ◽  
Jacob
Keyword(s):  
Human Computer Interaction ◽  
Real Time ◽  
Near Infrared ◽  
Brain Computer Interfaces ◽  
Functional Near Infrared Spectroscopy ◽  
The Past ◽  
Computer Interfaces ◽  
Brain States ◽  
Computer Interaction

Over the past decade, the Human–Computer Interaction (HCI) Lab at Tufts University has been developing real-time, implicit Brain–Computer Interfaces (BCIs) using functional near-infrared spectroscopy (fNIRS). This paper reviews the work of the lab; we explore how we have used fNIRS to develop BCIs that are based on a variety of human states, including cognitive workload, multitasking, musical learning applications, and preference detection. Our work indicates that fNIRS is a robust tool for the classification of brain-states in real-time, which can provide programmers with useful information to develop interfaces that are more intuitive and beneficial for the user than are currently possible given today’s human-input (e.g., mouse and keyboard).

Prediction of pork loin quality using online computer vision system and artificial intelligence model

Meat Science ◽  
2018 ◽  
Vol 140 ◽  
pp. 72-77 ◽  
Author(s):  
Xin Sun ◽  
Jennifer Young ◽  
Jeng-Hung Liu ◽  
David Newman
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Vision System ◽  
Computer Vision System ◽  
Pork Loin

scholarly journals Use of artificial intelligence-based computer vision system to practice social distancing in hospitals to prevent transmission of COVID-19

2020 ◽  
Vol 45 (3) ◽  
pp. 379 ◽  
Author(s):  
Vathsala Patil ◽  
BM Zeeshan Hameed ◽  
DasharathrajK Shetty ◽  
Nithesh Naik ◽  
Nikhil Nagaraj ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Vision System ◽  
Computer Vision System ◽  
Social Distancing

scholarly journals Removing Rain Component from A Color Image

2021 ◽  
Vol 9 (4) ◽  
pp. 410-415
Keyword(s):  
Color Image ◽  
Vision System ◽  
Research Topic ◽  
Single Image ◽  
Computer Vision System ◽  
The Past ◽  
Active Research ◽  
Bad Weather ◽  
Outdoor Vision

It is well-known that a bad weather, e.g. haze, rain, or snow affects severely the quality of the captured images or videos. Also raindrops adhered to a glass window or camera lens can severely affect the visibility of background scene and degrade the image quality, which consequently degrades the performance of many image processing and computer vision system algorithms. These algorithms are used in various applications such as object detection, tracking, recognition, and surveillance also in navigation. Rain removal from a video or a single image has been an active research topic over the past decade. Today, it continues to draw attentions in outdoor vision systems (e.g. surveillance) where the ultimate goal is to produce a clear and clean image or video. The most critical task here is to separate rain component from the other part. For that purpose, we are proposing an efficient algorithm to remove rain from a color image.

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