scholarly journals Quality Inspection of Food and Agricultural Products using Artificial Intelligence

2021 ◽  
Author(s):  
Maimunah Mohd Ali ◽  
Norhashila Hashim ◽  
Samsuzana Abd Aziz ◽  
Ola Lasekan
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Food Quality ◽  
High Performance ◽  
Agricultural Products ◽  
Special Focus ◽  
Quality Inspection ◽  
Big Data Technologies ◽  
Artificial Neural Network Ann ◽  
Non Destructive

A rising awareness for quality inspection of food and agricultural products has generated a growing effort to develop rapid and non-destructive techniques. Quality detection of food and agricultural products has prime importance in various stages of processing due to the laborious processes and the inability of the system to measure the whole of the food production. The detection of food quality has previously depended on various destructive techniques that require sample destruction and a large amount of postharvest losses. Artificial Intelligence (AI) has emerged with big data technologies and high-performance computation to create new opportunities in the multidisciplinary agri-food domain. This review presents the key concepts of AI comprising an expert system, artificial neural network (ANN), and fuzzy logic. A special focus is laid on the strength of AI applications in determining food quality for producing high and optimum yields. It was demonstrated that ANN provides the best result for modelling and effective in real-time monitoring techniques. The future use of AI for assessing quality inspection is promising which could lead to a real-time as well as rapid evaluation of various food and agricultural products.

scholarly journals Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study

2020 ◽  
Author(s):  
Yuchen Luo ◽  
Yi Zhang ◽  
Ming Liu ◽  
Yihong Lai ◽  
Panpan Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
High Performance ◽  
Detection System ◽  
Random Order ◽  
Colon Polyps ◽  
Clinical Environment ◽  
Polyp Detection ◽  
Link Type ◽  
Polyp Detection Rate

Abstract Background and aims Improving the rate of polyp detection is an important measure to prevent colorectal cancer (CRC). Real-time automatic polyp detection systems, through deep learning methods, can learn and perform specific endoscopic tasks previously performed by endoscopists. The purpose of this study was to explore whether a high-performance, real-time automatic polyp detection system could improve the polyp detection rate (PDR) in the actual clinical environment. Methods The selected patients underwent same-day, back-to-back colonoscopies in a random order, with either traditional colonoscopy or artificial intelligence (AI)-assisted colonoscopy performed first by different experienced endoscopists (> 3000 colonoscopies). The primary outcome was the PDR. It was registered with clinicaltrials.gov. (NCT047126265). Results In this study, we randomized 150 patients. The AI system significantly increased the PDR (34.0% vs 38.7%, p < 0.001). In addition, AI-assisted colonoscopy increased the detection of polyps smaller than 6 mm (69 vs 91, p < 0.001), but no difference was found with regard to larger lesions. Conclusions A real-time automatic polyp detection system can increase the PDR, primarily for diminutive polyps. However, a larger sample size is still needed in the follow-up study to further verify this conclusion. Trial Registration clinicaltrials.gov Identifier: NCT047126265

Big Data Analytics Framework for Real-Time Genome Analysis: A Comprehensive Approach

2019 ◽  
Vol 16 (8) ◽  
pp. 3419-3427
Author(s):  
Shishir K. Shandilya ◽  
S. Sountharrajan ◽  
Smita Shandilya ◽  
E. Suganya
Keyword(s):  
Big Data ◽  
Real Time ◽  
Genome Analysis ◽  
High Speed ◽  
High Performance ◽  
Big Data Analytics ◽  
Good Precision ◽  
Entire Genome ◽  
Big Data Technologies ◽  
Sequencing Platforms

Big Data Technologies are well-accepted in the recent years in bio-medical and genome informatics. They are capable to process gigantic and heterogeneous genome information with good precision and recall. With the quick advancements in computation and storage technologies, the cost of acquiring and processing the genomic data has decreased significantly. The upcoming sequencing platforms will produce vast amount of data, which will imperatively require high-performance systems for on-demand analysis with time-bound efficiency. Recent bio-informatics tools are capable of utilizing the novel features of Hadoop in a more flexible way. In particular, big data technologies such as MapReduce and Hive are able to provide high-speed computational environment for the analysis of petabyte scale datasets. This has attracted the focus of bio-scientists to use the big data applications to automate the entire genome analysis. The proposed framework is designed over MapReduce and Java on extended Hadoop platform to achieve the parallelism of Big Data Analysis. It will assist the bioinformatics community by providing a comprehensive solution for Descriptive, Comparative, Exploratory, Inferential, Predictive and Causal Analysis on Genome data. The proposed framework is user-friendly, fully-customizable, scalable and fit for comprehensive real-time genome analysis from data acquisition till predictive sequence analysis.

scholarly journals The Role of High Performance Computing and Communication for Real-Time Biofeedback in Sport

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Anton Umek ◽  
Anton Kos
Keyword(s):  
High Performance Computing ◽  
Real Time ◽  
High Performance ◽  
Tracking System ◽  
Optical Tracking ◽  
Local Processing ◽  
Special Focus ◽  
Dataflow Computing ◽  
Performance Computing

This paper studies the main technological challenges of real-time biofeedback in sport. We identified communication and processing as two main possible obstacles for high performance real-time biofeedback systems. We give special attention to the role of high performance computing with some details on possible usage of DataFlow computing paradigm. Motion tracking systems, in connection with the biomechanical biofeedback, help in accelerating motor learning. Requirements about various parameters important in real-time biofeedback applications are discussed. Inertial sensor tracking system accuracy is tested in comparison with a high performance optical tracking system. Special focus is given on feedback loop delays. Real-time sensor signal acquisitions and real-time processing challenges, in connection with biomechanical biofeedback, are presented. Despite the fact that local processing requires less energy consumption than remote processing, many other limitations, most often the insufficient local processing power, can lead to distributed system as the only possible option. A multiuser signal processing in football match is recognised as an example for high performance application that needs high-speed communication and high performance remote computing. DataFlow computing is found as a good choice for real-time biofeedback systems with large data streams.

Applications of Visible and near-Infrared Hyperspectral Imaging for Non-Destructive Detection of the Agricultural Products

2011 ◽  
Vol 317-319 ◽  
pp. 909-914
Author(s):  
Ying Lan Jiang ◽  
Ruo Yu Zhang ◽  
Jie Yu ◽  
Wan Chao Hu ◽  
Zhang Tao Yin
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Imaging System ◽  
Imaging Technique ◽  
Agricultural Products ◽  
Quality Inspection ◽  
Hyperspectral Reflectance ◽  
Imaging And Spectroscopy ◽  
Non Destructive

Agricultural products quality which included intrinsic attribute and extrinsic characteristic, closely related to the health of consumer and the exported cost. Now, imaging (machine vision) and spectrum are two main nondestructive inspection technologies to be applied. Hyperspectral imaging, a new emerging technology developed for detecting quality of the food and agricultural products in recent years, combined techniques of conventional imaging and spectroscopy to obtain both spatial and spectral information from an objective simultaneously. This paper compared the advantage and disadvantage of imaging, spectrum and hyperspectral imaging technique, and provided a description to basic principle, feature of hyperspectral imaging system and calibration of hyperspectral reflectance images. In addition, the recent advances for the application of hyperspectral imaging to agricultural products quality inspection were reviewed in other countries and China.

scholarly journals Improved real-time bio-aerosol classification using Artificial Neural Networks

2018 ◽  
Author(s):  
Maciej Leśkiewicz ◽  
Miron Kaliszewski ◽  
Maksymilian Włodarski ◽  
Jarosław Młyńczak ◽  
Zygmunt Mierczyk ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
High Performance ◽  
Spectral Characteristics ◽  
Air Contamination ◽  
Real Time Analysis ◽  
Fluorescence Characteristics ◽  
Aerosol Classification ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract. Air contamination has had stronger and stronger impact on everyday life of humans. An increasing number of people are aware of the health problems that may result from inhaling air containing dust, bacteria, pollens or fungi. Society is awaiting anxiously for a system that could inform them in real-time about a real danger that is suspended in the air. The devices, currently available on the market, are able to detect some particles in the air, but cannot classify them by the health threats. Fortunately, a new type of technology is emerging as a really promising solution. Laser based bio-detectors are opening a new era in aerosol research. They are capable of characterizing a great number of individual particles in seconds by analyzing optical scattering and fluorescence characteristics. In this study we demonstrate application of Artificial Neural Network (ANN) to real-time analysis of single particle fluorescence fingerprints. We gathered a total of 114 779 spectra of 48 aerosols. We discuss an entirely new approach to data analysis using decision tree comprising 22 independent neural networks. Applying confusion matrices and ROC analysis the best sets of ANN’s for each group of similar aerosols has been determined. As a result we achieved very high performance of aerosol classification in real-time. We found that for some substances that have characteristic spectra almost each particle can be properly classified. The aerosols with similar spectral characteristics can be classified as a specific cloud with high probability.

A Real-Time Automatic Inspection System for Pattavia Pineapples

2006 ◽  
Vol 321-323 ◽  
pp. 1186-1191 ◽  
Author(s):  
Watcharin Kaewapichai ◽  
Pakorn Kaewtrakulpong ◽  
Asa Prateepasen
Keyword(s):  
Real Time ◽  
High Performance ◽  
Inspection System ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Recognition Phase ◽  
Histogram Intersection ◽  
Two Phases ◽  
Belt Transmission ◽  
Non Destructive

This paper presents a machine vision method to inspect the maturity of pineapples that ripe naturally. Unlike previous methods, the proposed technique can be categorized as a real-time non destructive testing (Real-Time NDT) approach. It consists of two phases, learning and recognition phases. In the learning phase, the system constructs a library of reference pineappleskin- color models. In the recognition phase, the same process is performed to build a pineappleskin- color model of the testing subject. The model is then compared with each of the reference in the library by a method called region-segmented histogram intersection. The subject is then labeled with the grade of the best match. The system achieved a high performance and speed (3 frames/sec.) in our experiment. The system also includes weighing machine on belt transmission for weight prediction.

Numerical study of light propagation in agricultural products for non-destructive assessment of food quality

2015 ◽  
Author(s):  
Kiyohito Hattori ◽  
Hiroyuki Fujii ◽  
Yuki Tatekura ◽  
Kazumichi Kobayashi ◽  
Masao Watanabe
Keyword(s):  
Food Quality ◽  
Light Propagation ◽  
Numerical Study ◽  
Agricultural Products ◽  
Non Destructive

Real-Time Solution for Down Hole Torque Estimation and Drilling Optimization in High Deviated Wells Using Artificial Intelligence

2021 ◽  
Author(s):  
Mahmoud Nader Elzenary
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Major Influence ◽  
Support Vector ◽  
Rate Of Penetration ◽  
Artificial Intelligence Technology ◽  
Weight On Bit ◽  
Artificial Neural Network Ann ◽  
Torque Values ◽  
Deviated Wells

ABSTRACT This project provides a new realistic solution for the accuracy of down hole torque measurements using the integration of the Artificial intelligence (AI) technology with the downhole challenges being faced while drilling deep and high deviated wells. The new estimates are based on surface measurements which have the major influence on the bit torque (downhole torque) values while drilling. Artificial intelligence technology and its related applications such as; artificial neural network (ANN), support vector machine (SVM) and adaptive neuro fuzzy interference system (ANFIS) will be utilized to predict and estimate accurate wellbore torque which will be applied effectively to prevent real time stuck pipe situation through a friendly user software which will maintain the downhole torque within the SAFE zone by controlling the unified surface drilling variables such as; weight on bit (WOB), Rate of Penetration (ROP) and Flow Rate. This downhole torque model will be validated and verified through a real drilling scenario from a field in north of Africa. The field data includes weight on bit, surface torque, stand-pipe pressure, and rate of penetration were collected from the mentioned well which had experienced a costly stuck pipe situation. However, with the provided model the same encountered scenario will be avoided, due to the optimization of the real time drilling variables and hence, saving the well and evade a costly non-productive time.

scholarly journals Applications of Artificial Intelligence in Agriculture: A Review

2019 ◽  
Vol 9 (4) ◽  
pp. 4377-4383 ◽  
Author(s):  
N. C. Eli-Chukwu
Keyword(s):  
Artificial Intelligence ◽  
Weed Management ◽  
High Performance ◽  
Agricultural Sector ◽  
Special Focus ◽  
Main Concept ◽  
Pest Infestation ◽  
Performance Accuracy ◽  
Data Requirements ◽  
Applications Of Ai

The application of Artificial Intelligence (AI) has been evident in the agricultural sector recently. The sector faces numerous challenges in order to maximize its yield including improper soil treatment, disease and pest infestation, big data requirements, low output, and knowledge gap between farmers and technology. The main concept of AI in agriculture is its flexibility, high performance, accuracy, and cost-effectiveness. This paper presents a review of the applications of AI in soil management, crop management, weed management and disease management. A special focus is laid on the strength and limitations of the application and the way in utilizing expert systems for higher productivity.

Sign in / Sign up

Export Citation Format

Share Document