UTILIZING MACHINE VISION AND ARTIFICIAL NEURAL NETWORKS FOR DRIED GRAPE SORTING DURING PRODUCTION
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| Publicado en: | Chemical Industry & Chemical Engineering Quarterly vol. 31, no. 3 (Jul-Sep 2025), p. 219-228 |
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| Autor principal: | |
| Otros Autores: | , , , , , |
| Publicado: |
Association of the Chemical Engineers of Serbia
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 001 | 3225548037 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1451-9372 | ||
| 022 | |a 2217-7434 | ||
| 022 | |a 0354-7531 | ||
| 024 | 7 | |a 10.2298/CICEQ231003030R |2 doi | |
| 035 | |a 3225548037 | ||
| 045 | 2 | |b d20250701 |b d20250930 | |
| 100 | 1 | |a Ruangurai, Piyanun |u College of Industrial Technology, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand | |
| 245 | 1 | |a UTILIZING MACHINE VISION AND ARTIFICIAL NEURAL NETWORKS FOR DRIED GRAPE SORTING DURING PRODUCTION | |
| 260 | |b Association of the Chemical Engineers of Serbia |c Jul-Sep 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This study introduces a machine vision technique that utilizes an artificial neural network (ANN) to develop a predictive model for classifying dried grapes during the drying process. The primary objective of this model is to mitigate the burden placed on the operator and minimize the occurrence of over-dried items. The present study involves the development of a model that is constructed using the characteristics of grape color and shape. There exist two distinct categories of labels for grapes: fully desiccated grapes, commonly referred to as raisins, and grapes that have undergone partial drying. Image processing is utilized to collect and observe five significant characteristics of grapes during the drying process. The findings indicate a significant decrease in the levels of red, green, and blue colors (RGB) during the initial 15-hour drying period. The predictive model extracts properties such as RGB color, roundness, and shrinkage from the image while it undergoes the drying process. The artificial neural network (ANN) model achieved a level of accuracy performance of 78%. In this work, the dehydration apparatus will cease operation in an automated manner whenever the entirety of the grapes situated on the tray has been projected to transform raisins. Ovaj rad uvodi tehniku obrade slike koja koristi veštačku neuronsku mrežu (ANN) za razvoj prediktivnog modela za klasifikaciju suvog grožða tokom procesa sušenja. Primarni cilj ovog modela je da se ublaži teret koji se stavlja na operatera i minimizira pojavu previše osušenih grozdova. Ova studija podrazumeva razvoj modela koji se konstruiše korišćenjem karakteristika boje i oblika grožða. Postoje dve različite kategorije za grožðe: potpuno isušeno grožðe, koje se obično naziva suvo grožðe, i grožðe koje je podvrgnuto delimičnom sušenju. Obrada slike se koristi za prikupljanje i posmatranje pet značajnih karakteristika grožða tokom procesa sušenja. Nalazi ukazuju na značajno smanjenje nivoa crvene, zelene i plave boje (RGB) tokom početnog perioda sušenja od 15 sati. Prediktivni model izdvaja svojstva, kao što su RGB boja, zaobljenost i skupljanje iz slike, dok se grožðe podvrgava procesu sušenja. Model veštačke neuronske mreže (ANN) postigao je nivo tačnosti od 78%. U ovom radu, aparat za dehidraciju cé automatski prestati sa radom kad god se planira da celokupno grožðe na tacni preobrazi u suvo grožðe. | |
| 610 | 4 | |a Raspberry Pi Ltd | |
| 653 | |a Roundness | ||
| 653 | |a Embedded systems | ||
| 653 | |a Dehydration | ||
| 653 | |a Control algorithms | ||
| 653 | |a Machine vision | ||
| 653 | |a Investigations | ||
| 653 | |a Food | ||
| 653 | |a Grapes | ||
| 653 | |a Computer vision | ||
| 653 | |a Vision systems | ||
| 653 | |a Prediction models | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Drying | ||
| 653 | |a Neural networks | ||
| 653 | |a Classification | ||
| 653 | |a Shape | ||
| 653 | |a Fruits | ||
| 653 | |a Color | ||
| 653 | |a Microwaves | ||
| 653 | |a Image processing | ||
| 653 | |a Raisins | ||
| 653 | |a Moisture content | ||
| 653 | |a Image processing systems | ||
| 700 | 1 | |a Tanasansurapong, Nattabut |u College of Industrial Technology, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand | |
| 700 | 1 | |a Prasitsanha, Sirakupt |u College of Industrial Technology, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand | |
| 700 | 1 | |a Bunchan, Rewat |u College of Industrial Technology, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand | |
| 700 | 1 | |a Tuvayanond, Wiput |u Industry Agricultural Engineering, Rajamangala University of Technology Thanyaburi, Pathum Thani, Thailand | |
| 700 | 1 | |a Haval, Thana Chotchuangchutc | |
| 700 | 1 | |a Silawatchananai, Chaiyaporn | |
| 773 | 0 | |t Chemical Industry & Chemical Engineering Quarterly |g vol. 31, no. 3 (Jul-Sep 2025), p. 219-228 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3225548037/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3225548037/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3225548037/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |