scholarly journals Grape Drying Process Using Machine Vision Based on Multilayer Perceptron Networks

2020 ◽  
Vol 5 (3) ◽  
pp. 382-394
Author(s):  
Suprapto Suprapto ◽  
Edy Riyanto
Keyword(s):  
Computer Vision ◽  
Machine Vision ◽  
Motion Control ◽  
Multilayer Perceptron ◽  
Multi Layer Perceptron ◽  
Drying Process ◽  
Different Temperatures ◽  
Motion Speed ◽  
Similar Area ◽  
Multilayer Perceptron Networks

This paper proposed a grape drying machine using computer vision and Multi-layer Perceptron (MLP) method. Computer vision is for taking grapes’ image on conveyor, whereas MLP is for controlling grape drying machine and classifying its output. To evaluate the proposed, a kind of grapes are put on conveyor of the machine and their images are taken every two min. Some parameters of MLP to control the drying machine includes dried grape, temperature, grape area, motor position, and motion speed. Those parameters are to adjust an appropriate MLP’s output, including motion control and heater control. Two different temperatures are employed on the machine, including 60 and 75°C. The results showed that the grape could be dried with similar area 3800 pixel at the 770th min using temperature 60°C and at the 410th min using temperature 75°C.  Comparing between them, the similar ratio could also be achieved at 0.64 with different time 360 min. Indeed, the temperature setting at 75°C resulted faster drying performance.

scholarly journals Cherry Tomato Drying: Sun versus Convective Oven

Horticulturae ◽  
2021 ◽  
Vol 7 (3) ◽  
pp. 40
Author(s):  
Vincenzo Alfeo ◽  
Diego Planeta ◽  
Salvatore Velotto ◽  
Rosa Palmeri ◽  
Aldo Todaro
Keyword(s):  
Moisture Content ◽  
Initial Moisture Content ◽  
Drying Process ◽  
Drying Time ◽  
Oven Drying ◽  
Sun Drying ◽  
Different Temperatures ◽  
Final Water Content ◽  
Drying Curves ◽  
Drying Conditions

Solar drying and convective oven drying of cherry tomatoes (Solanum lycopersicum) were compared. The changes in the chemical parameters of tomatoes and principal drying parameters were recorded during the drying process. Drying curves were fitted to several mathematical models, and the effects of air temperature during drying were evaluated by multiple regression analyses, comparing to previously reported models. Models for drying conditions indicated a final water content of 30% (semidry products) and 15% (dry products) was achieved, comparing sun-drying and convective oven drying at three different temperatures. After 26–28 h of sun drying, the tomato tissue had reached a moisture content of 15%. However, less drying time, about 10–11 h, was needed when starting with an initial moisture content of 92%. The tomato tissue had high ORAC and polyphenol content values after convective oven drying at 60 °C. The dried tomato samples had a satisfactory taste, color and antioxidant values.

scholarly journals Application of Zernike moments in computer vision problems for infrared images

2021 ◽  
Vol 310 ◽  
pp. 01002
Author(s):  
Dmitriy Otkupman ◽  
Sergey Bezdidko ◽  
Victoria Ostashenkova
Keyword(s):  
Computer Vision ◽  
Machine Vision ◽  
Thermal Imaging ◽  
Imaging System ◽  
Infrared Region ◽  
Zernike Moments ◽  
Infrared Images ◽  
Theoretical Justification ◽  
Thermal Imaging System ◽  
Binarization Method

The efficiency of using Zernike moments when working with digital images obtained in the infrared region of the spectrum is considered to improve the accuracy and speed of an autonomous thermal imaging system. The theoretical justification of the choice of Zernike moments for solving computer (machine) vision problems and the choice of a suitable threshold binarization method is given. In order to verify the adequacy and expediency of using the chosen method, practical studies were conducted on the use of Zernike methods for distorting various thermal images in shades of gray.

A Comparison of Orthogonal Transforms in Engineering Computer Vision

1990 ◽  
Vol 204 (5) ◽  
pp. 321-327 ◽  
Author(s):  
G A H Al-Kindi ◽  
R M Baul ◽  
K F Gill
Keyword(s):  
Computer Vision ◽  
Machine Vision ◽  
Visual Inspection ◽  
Computational Time ◽  
Transform Coefficients ◽  
Orthogonal Transforms ◽  
The Impact

A comparison of a number of commonly used orthogonal transforms, when applied to the recognition and visual inspection of engineering components, has been made. The impact on the performance and computational time for the machine vision process due to varying numbers of transform coefficients is assessed.

Computer vision technology for real-time food quality assurance during drying process

2014 ◽  
Vol 39 (1) ◽  
pp. 76-84 ◽  
Author(s):  
Mortaza Aghbashlo ◽  
Soleiman Hosseinpour ◽  
Mahdi Ghasemi-Varnamkhasti
Keyword(s):  
Computer Vision ◽  
Quality Assurance ◽  
Real Time ◽  
Food Quality ◽  
Drying Process ◽  
Computer Vision Technology

Forecasting PM10 in Algiers: efficacy of multilayer perceptron networks

2015 ◽  
Vol 23 (2) ◽  
pp. 1634-1641 ◽  
Author(s):  
Hamza Abderrahim ◽  
Mohammed Reda Chellali ◽  
Ahmed Hamou
Keyword(s):  
Multilayer Perceptron ◽  
Multilayer Perceptron Networks

INSPECTION AND QUALITY CHECKING OF CERAMIC CUP USING MACHINE VISION TECHNIQUE: DESIGN AND ANALYSIS

2017 ◽  
Vol 79 (5-2) ◽  
Author(s):  
Nursabillilah Mohd Ali ◽  
Mohd Safirin Karis ◽  
Siti Azura Ahmad Tarusan ◽  
Gao-Jie Wong ◽  
Mohd Shahrieel Mohd Aras ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Machine Vision ◽  
Image Sensor ◽  
Histogram Method ◽  
Inspection Method ◽  
Inspection Process ◽  
Set Up ◽  
Vision Application ◽  
Vision Technique

The development of inspection and quality checking using machine vision technique are discussed where the design of the algorithm mainly to detect the sign of defect when a sample product is used for inspection purposes. There are several constraints that a machine need to be improved based on technology used in vision application. CMOS image sensor as well as programming language and open source computer vision library were used in designing the inspection method. Experimental set-up was conducted to test the proposed technique for evaluate the effectiveness process. The experimental results were obtained and represented in graphical and image processing form. Besides, analysis and discussion were made according to obtained results. The proposed technique is able to perform the inspection process using good and defect ceramic cup based on detection technique. Moreover, based on the analysis gathered, the proposed technique able to differentiate between good and defect ceramic cup. The result shows that there is a difference frequency by 236 which is 2% of total value in pixels frequency. The frequency indicated as pixel frequency of image using histogram method based on scaled value of image.

scholarly journals Bispectrum and Recurrent Neural Networks: Improved Classification of Interictal and Preictal States

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Laura Gagliano ◽  
Elie Bou Assi ◽  
Dang K. Nguyen ◽  
Mohamad Sawan
Keyword(s):  
Neural Networks ◽  
Multilayer Perceptron ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Focal Epilepsy ◽  
Single Layer ◽  
Epileptic Seizures ◽  
Novel Approach ◽  
Multilayer Perceptron Networks

Abstract This work proposes a novel approach for the classification of interictal and preictal brain states based on bispectrum analysis and recurrent Long Short-Term Memory (LSTM) neural networks. Two features were first extracted from bilateral intracranial electroencephalography (iEEG) recordings of dogs with naturally occurring focal epilepsy. Single-layer LSTM networks were trained to classify 5-min long feature vectors as preictal or interictal. Classification performances were compared to previous work involving multilayer perceptron networks and higher-order spectral (HOS) features on the same dataset. The proposed LSTM network proved superior to the multilayer perceptron network and achieved an average classification accuracy of 86.29% on held-out data. Results imply the possibility of forecasting epileptic seizures using recurrent neural networks, with minimal feature extraction.

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