Research Article
Year 2021,
Volume: 2 Issue: 2, 330 - 338, 31.12.2021
Abstract
References
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- Leon K, Mery D and Leon J (2006). Colour measurment in L*a*b* units from RGB digital images. Food Research International, 39: 1084-1091.
- Mahendran R and Jayashree GC and Alagusundaram K (2011). Application of computer vision technique on sorting and grading of fruits and vegetables. Journal of Food Process Technology, S1-001.
- Mendoza F, Dejmek P and Aguilera JM (2006). Calibrated colour measurements of agricultural foods using image analysis. Postharvest Biology and Technology, 41: 285-295.
- Narendra VG and Hareesh KS (2010). Prospects of computer vision automated grading and sorting systems in agricultural and food products for quality evaluation. International Journal of Computer Applications, (0975 – 8887), 1(4): 1-9.
- Pawar SS and Dale MP (2016). Lemon detection and sorting system based on computer vision. 3rd International Conference on Electrical, Electronics, Engineering Trends, Communication, Optimization and Sciences (EEECOS) -2016, 376-379.
- Ratule MT, Osman A, Ahmad SH and Saari N (2006). Development of chilling injury of ‘Berangan’ banana (Musa cv. Berangan (AAA)) during storage at low temperature. Journal of Food Agriculture & Environment, 4(1): 128-134.
- Riyadi DS and Aisyah S (2018). Vision based flame detection system for surveillance camera. In 2018 International Conference on Applied Engineering (ICAE) (pp. 1-6). IEEE.
- Yu L, Shi J, Huang C, Duan L, Wu D, Fu D, Wu C, Xiong L, Yang W and Liu Q (2021). An integrated rice panicle phenotyping method based on X-ray and RGB scanning and deep learning. The Crop Journal, 9(1): 42-56.
- Xing J, Bravo C, Moshou D, Roman H and Baerdemaeker JD (2006). Bruise detection on ‘Golden Delicious’ apples by vis/NIR spectroscopy. Computers and Electronics in Agriculture, 52: 11-20.
- Yeni L and Shaowei Y (2017). Defect inspection system of carbonized bamboo cane based on LabView and Machine Vision. In 2017 International Conference on Information, Communication and Engineering (ICICE) pp. 314-317. IEEE.
Year 2021,
Volume: 2 Issue: 2, 330 - 338, 31.12.2021
Abstract
Colorimetry is of paramount importance to the agricultural industry. Colorimetry refers to the processing of agricultural products for consumer needs from a marketing point of view, and therefore the agricultural industry spends a lot of money and time classifying each product. In the past, agricultural professionals had to use program codes that are difficult to learn, and even the most basic image analysis for agricultural product classification required mastering different program libraries. Today, the LabVIEW platform offers a flexible, fast, easy-to-learn, and complete image analysis infrastructure with various useful modules. For this reason, in this study, a method analysis for color perception with a simple USB webcam and software developed for real-time color analysis on the LabVIEW platform is presented and its success in the basic color analysis is tried to be revealed. The basic application developed for this purpose in LabVIEW v2019 using NI Vision Development Module v19 and NI IMAQ v19 modules. The basic fact that is the LabVIEW application is the idea that LabVIEW can only be analyzed with expensive IEEE 1394, but it should be known that these analyzes can be done with USB webcams. For this purpose, the application includes a USB webcam driver that can be stacked seamlessly. USB Webcam and colorimeter measurement-based results of ƔR factors for each of RGB color channels are 1.161232, 0.506287, 0.432229; ƔG factors for each of RGB color channels are 0.519619, 1.025383, 1.201444; at last ƔB factors for each of RGB color channels are 0.600362, 0.714016, 1.413406, respectively.
References
- Anonymous (2021). Ci6X series spectrophotometer user manual. https://www.xrite.com/-/media/xrite/files/manuals_and_userguides/c/i/ci6x-500/ci6x-500_user_guide_en.pdf (29.08.2021)
- Brosnan T and Wen Sun D (2004). Improving quality inspection of food products by computer vision-a review. Journal of Food Engineering, 61: 3-16.
- Fouda T and Salah, S (2014). Using imaging analyses to predict chemical properties of orange fruits. Scientific Papers Series Management. Economic Engineering in Agriculture and Rural Development, 14(3): 83-86.
- Leon K, Mery D and Leon J (2006). Colour measurment in L*a*b* units from RGB digital images. Food Research International, 39: 1084-1091.
- Mahendran R and Jayashree GC and Alagusundaram K (2011). Application of computer vision technique on sorting and grading of fruits and vegetables. Journal of Food Process Technology, S1-001.
- Mendoza F, Dejmek P and Aguilera JM (2006). Calibrated colour measurements of agricultural foods using image analysis. Postharvest Biology and Technology, 41: 285-295.
- Narendra VG and Hareesh KS (2010). Prospects of computer vision automated grading and sorting systems in agricultural and food products for quality evaluation. International Journal of Computer Applications, (0975 – 8887), 1(4): 1-9.
- Pawar SS and Dale MP (2016). Lemon detection and sorting system based on computer vision. 3rd International Conference on Electrical, Electronics, Engineering Trends, Communication, Optimization and Sciences (EEECOS) -2016, 376-379.
- Ratule MT, Osman A, Ahmad SH and Saari N (2006). Development of chilling injury of ‘Berangan’ banana (Musa cv. Berangan (AAA)) during storage at low temperature. Journal of Food Agriculture & Environment, 4(1): 128-134.
- Riyadi DS and Aisyah S (2018). Vision based flame detection system for surveillance camera. In 2018 International Conference on Applied Engineering (ICAE) (pp. 1-6). IEEE.
- Yu L, Shi J, Huang C, Duan L, Wu D, Fu D, Wu C, Xiong L, Yang W and Liu Q (2021). An integrated rice panicle phenotyping method based on X-ray and RGB scanning and deep learning. The Crop Journal, 9(1): 42-56.
- Xing J, Bravo C, Moshou D, Roman H and Baerdemaeker JD (2006). Bruise detection on ‘Golden Delicious’ apples by vis/NIR spectroscopy. Computers and Electronics in Agriculture, 52: 11-20.
- Yeni L and Shaowei Y (2017). Defect inspection system of carbonized bamboo cane based on LabView and Machine Vision. In 2017 International Conference on Information, Communication and Engineering (ICICE) pp. 314-317. IEEE.
There are 13 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Agricultural Engineering |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | December 31, 2021 |
| Submission Date | August 15, 2021 |
| Acceptance Date | October 14, 2021 |
| Published in Issue | Year 2021 Volume: 2 Issue: 2 |
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