Color Measurement and Analysis of Fruit with a Battery-Less NFC Sensor
<p>Block diagram of the system.</p> "> Figure 2
<p>(<b>a</b>) Block diagram of the tag; (<b>b</b>) detail of the colorimeter sub-block.</p> "> Figure 3
<p>Photograph of the tag prototype: (<b>a</b>) Front side, (<b>b</b>) back side, (<b>c</b>) tag within the 3D printed enclosure.</p> "> Figure 4
<p>(<b>a</b>) Voltage of the harvesting NFC IC output in (V) as a function of the distance to the mobile reader; (<b>b</b>) Measured antenna factor as a function of distance to the mobile reader; (<b>c</b>) Measured magnetic field in A<sub>RMS</sub>/m as a function of the distance to the mobile reader.</p> "> Figure 5
<p>(<b>a</b>) RGB color space, (<b>b</b>) HSV color space, (<b>c</b>) CIELab color space.</p> "> Figure 6
<p>Histogram for the golden apple for different days: Histogram of the hue (<b>a</b>), saturation (<b>b</b>) and value (<b>c</b>) parameters in the fridge. Histogram of the hue (<b>d</b>), saturation (<b>e</b>) and value (<b>f</b>) parameters at room temperature.</p> "> Figure 7
<p>Cumulative Distribution Function (CDF) of the hue parameter for the golden apple in the fridge (<b>a</b>) and at room temperature (<b>b</b>) as a function of the number of days out of the fridge. Cumulative Distribution Function (CDF) of the saturation parameter for the golden apple in the fridge (<b>c</b>) and at room temperature (<b>d</b>) as a function of the number of days out of the fridge.</p> "> Figure 8
<p>Histogram for a banana for different days: Histogram of the hue (<b>a</b>), saturation (<b>b</b>) and value (<b>c</b>) parameters in the fridge. Histogram of the hue (<b>d</b>), saturation (<b>e</b>) and value (<b>f</b>) parameters at room temperature.</p> "> Figure 9
<p>Cumulative Distribution Function (CDF) of the hue parameter for the banana in the fridge (<b>a</b>) and at room temperature (<b>b</b>) as a function of the number of days. Cumulative Distribution Function (CDF) of the saturation parameter for the banana in the fridge (<b>c</b>) and at room temperature (<b>d</b>) as a function of the number of days.</p> "> Figure 10
<p>Histogram for a red apple for different days: Histogram of the hue (<b>a</b>), saturation (<b>b</b>) and value (<b>c</b>) parameters in the fridge. Histogram of the hue (<b>d</b>), saturation (<b>e</b>) and value (<b>f</b>) parameters at room temperature.</p> "> Figure 11
<p>Cumulative Distribution Function (CDF) of the Hue parameter for the red apple in the fridge (<b>a</b>) and at room temperature (<b>b</b>) as a function of the number of days. Cumulative Distribution Function (CDF) of the saturation parameter for the red apple in the fridge (<b>c</b>) and at room temperature (<b>d</b>) as a function of the number of days.</p> "> Figure 12
<p>Decision boundaries and scatter plot (class 1 squares, class 2, crosses) for the golden apple. (<b>a</b>) Naive Bayes, (<b>b</b>) linear discriminant analysis, (<b>c</b>) classification tree and (<b>d</b>) Nearest Neighbor (k = 5).</p> "> Figure 13
<p>Decision boundaries and scatter plot (class 1 squares, class 2, crosses) for the banana. (<b>a</b>) Naive Bayes, (<b>b</b>) linear discriminant analysis, (<b>c</b>) classification tree and (<b>d</b>) Nearest Neighbor (k = 5).</p> "> Figure 14
<p>Decision boundaries and scatter plot (class 1 squares, class 2, crosses) for the red apple. (<b>a</b>) Naive Bayes, (<b>b</b>) linear discriminant analysis, (<b>c</b>) classification tree and (<b>d</b>) Nearest Neighbor (k = 5).</p> "> Figure 15
<p>Decision boundaries and scatter plot (class 1 squares, class 2, crosses) for the golden apple. (<b>a</b>) Naive Bayes, (<b>b</b>) linear discriminant analysis, (<b>c</b>) classification tree and (<b>d</b>) Nearest Neighbor (k = 5).</p> "> Figure 16
<p>Decision boundaries and scatter plot (class 1 squares, class 2, crosses) for the banana. (<b>a</b>) Naive Bayes, (<b>b</b>) linear discriminant analysis, (<b>c</b>) classification tree and (<b>d</b>) Nearest Neighbor (k = 5).</p> "> Figure 17
<p>Decision boundaries and scatter plot (class 1 squares, class 2, crosses) for the red apple. (<b>a</b>) Naive Bayes, (<b>b</b>) linear discriminant analysis, (<b>c</b>) classification tree and (<b>d</b>) Nearest Neighbor (k = 5).</p> "> Figure 18
<p>Flowchart of the mobile application.</p> "> Figure 19
<p>Phone screen of the developed application. (<b>a</b>) Fruit selection, (<b>b</b>) screen indicating to tap the tag, (<b>c</b>) representation of the detected color, (<b>d</b>) decision boundaries of the training, (<b>e</b>) additional user information.</p> "> Figure 20
<p>Measurement of a red apple using the designed application.</p> ">
Abstract
:1. Introduction
2. System Overview
3. Experimental Results
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Pathare, P.B.; Opara, U.L.; Al-Said, F.A.J. Colour measurement and analysis in fresh and processed foods: A review. Food Bioprocess Technol. 2013, 6, 36–60. [Google Scholar] [CrossRef]
- Lee, D.; Archibald, J.K.; Xiong, G. Rapid color grading for fruit quality evaluation using direct color mapping. IEEE Trans. Autom. Sci. Eng. 2011, 8, 292–302. [Google Scholar] [CrossRef]
- Syahrir, W.M.; Suryanti, A.; Connsynn, C. Color grading in tomato maturity estimator using image processing technique. In Proceedings of the 2nd IEEE International Conference on Computer Science and Information Technology, Beijing, China, 8–11 August 2009; pp. 276–280. [Google Scholar] [CrossRef]
- Mustafa, N.; Fuad, N.; Ahmed, S.; Abidin, A.; Ali, Z.; Yit, W.; Sharrif, Z. Image processing of an agriculture produce: Determination of size and ripeness of a banana. In Proceedings of the 2008 International Symposium on Information Technology, Kuala Lumpur, Malaysia, 26–28 August 2008; pp. 1–7. [Google Scholar] [CrossRef]
- Rennick, G.; Attikiouzel, Y.; Zaknic, A. Machine grading and blemish detection in apples. In Proceedings of the Fifth International Symposium on Signal Processing and its Applications, Brisbane, Queensland, Australia, 22–25 August 1999; Volume 2, pp. 567–570. [Google Scholar] [CrossRef]
- Zhao, Y.; Wang, D.; Qian, D. Machine vision based image analysis for the estimation of pear external quality. In Proceedings of the Second International Conference on Intelligent Computation Technology and Automation, Changsha, Hunan, China, 10–11 October 2009; pp. 629–632. [Google Scholar] [CrossRef]
- Recce, M.; Taylor, J.; Piebe, A.; Tropiano, G. High speed vision-based quality grading of oranges. In Proceedings of the International Workshop on Neural Networks for Identification, Control, Robotics and Signal/Image Processing, Venice, Italy, 21–23 August 1996; pp. 136–144. [Google Scholar] [CrossRef]
- Lee, D.J.; Archibald, J.K.; Chang, Y.C.; Greco, C.R. Robust color space conversion and color distribution analysis techniques for date maturity evaluation. J. Food Eng. 2008, 88, 364–372. [Google Scholar] [CrossRef]
- Mendoza, F.; Dejmek, P.; Aguilera, J.M. Calibrated color measurements of agricultural foods using image analysis. Postharvest Biol. Technol. 2006, 41, 285–295. [Google Scholar] [CrossRef]
- HunterLab. Colorimeters vs. Spectrophotometers, Applications Note. Insight on Color, Vol.6, no.5, 1–2. 2008. Available online: https://www.hunterlab.se/wp-content/uploads/2012/11/Colorimeters-Versus-Spectrophotometers.pdf (accessed on 2 January 2019).
- Vandekinderen, I.; Van Camp, J.; Devlieghere, F.; Veramme, K.; Denon, Q.; Ragaert, P.; De Meulenaer, B. Effect of decontamination agents on the microbial population, sensorial quality, and nutrient content of grated carrots (Daucus carota L.). J. Agric. Food Chem. 2008, 56, 5723–5731. [Google Scholar] [CrossRef] [PubMed]
- Scanlon, M.; Roller, R.; Mazza, G.; Pritchard, M. Computerized video image analysis to quantify color of potato chips. Am. J. Potato Res. 1994, 71, 717–733. [Google Scholar] [CrossRef]
- Finkenzeller, K.; Müller, D. RFID Handbook: Fundamentals and Applications in Contactless Smart Cards, Radio Frequency Identification and Near-Field Communication; Wiley: New York, NY, USA, 2010; ISBN 0470695064. [Google Scholar]
- Chen, R.S.; Chen, C.C.; Yeh, K.C.; Chen, Y.C.; Kuo, C.W. Using RFID technology in food produce traceability. WSEAS Trans. Inf. Sci. Appl. 2008, 5, 1551–1560. [Google Scholar]
- Near Field Communications Forum. Available online: http://nfc-forum.org (accessed on 28 August 2018).
- Paret, D. Design Constraints for NFC Devices; Wiley: Hoboken, NJ, USA, 2016; ISBN 9781848218840. [Google Scholar]
- Jara, A.J.; Lopez, P.; Fernandez, D.; Zamora, M.A.; Ubeda, B.; Skarmeta, A.F. Communication protocol for enabling continuous monitoring of elderly people through near field communications. Interact. Comput. 2013, 26, 145–168. [Google Scholar] [CrossRef]
- Sipsas, K.; Alexopoulos, K.; Xanthakis, V.; Chryssolouris, G. Collaborative maintenance in flow-line manufacturing environments: An Industry 4.0 approach. Procedia CIRP 2016, 55, 236–241. [Google Scholar] [CrossRef]
- Boada, M.; Lazaro, A.; Villarino, R.; Girbau, D. Battery-less soil moisture measurement system based on a NFC device with energy harvesting capability. IEEE Sens. J. 2018, 18, 5541–5549. [Google Scholar] [CrossRef]
- Lazaro, A.; Villarino, R.; Girbau, D. A survey of NFC sensors based on energy harvesting for IoT applications. Sensors 2018, 18, 3746. [Google Scholar] [CrossRef]
- Boada, M.; Lazaro, A.; Villarino, R.; Girbau, D. Battery-less NFC sensor for pH monitoring. IEEE Access 2019, 7, 33226–33239. [Google Scholar] [CrossRef]
- TCS3472 Color Light-To-Digital Converter with IR Filter, TAOS135-August 2012. Available online: https://cdn-shop.adafruit.com/datasheets/TCS34725.pdf (accessed on 28 August 2018).
- Pierre, F.; Aujol, J.-F.; Bugeau, A.; Ta, V.-T. Luminance-hue specification in the RGB space. In Proceedings of the International Conference on Scale Space and Variational Methods in Computer Vision, Lège-Cap Ferret, France, 31 May–4 June 2015; pp. 413–424. [Google Scholar] [CrossRef]
- Hanbury, A. Constructing cylindrical coordinate colour spaces. Pattern Recognit. Lett. 2008, 29, 494–500. [Google Scholar] [CrossRef] [Green Version]
- Trambadia, S.; Mayatra, H. Food detection on plate based on the HSV color model. In Proceedings of the Online International Conference on Green Engineering and Technologies (IC-GET), Coimbatore, India, 19 November 2016; pp. 1–6. [Google Scholar] [CrossRef]
- Chen, W.; Shi, Y.Q.; Xuan, G. Identifying computer graphics using HSV color model and statistical moments of characteristic functions. In Proceedings of the IEEE International Conference on Multimedia and Expo, Beijing, China, 2–5 July 2007; pp. 1123–1126. [Google Scholar] [CrossRef]
- Ke, X.; Guandong, G.; Jian, L. An improved method of detecting pork freshness based on CRR features. In Proceedings of the Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Beijing, China, 16–18 October 2013; pp. 194–197. [Google Scholar] [CrossRef]
- Medlicott, A.P.; Semple, A.J.; Thompson, A.J.; Blackbourne, H.R.; Thompson, A.K. Measurement of colour changes in ripening bananas and mangoes by instrumental, chemical and visual assessments. Trop. Agric. 1992, 69, 161–166. [Google Scholar]
- Brugiapaglia, A.; Destefanis, G.; Agosta, S.; Di Stasio, L. Repeatability and reproducibility of two instruments to measure meat colour. In Proceedings of the 62nd International Congress of Meat Science and Technology, Bangkok, Thailand, 14–19 August 2016; pp. 1–4. [Google Scholar] [CrossRef]
- Dominguez, R.B.; Orozco, M.A.; Chávez, G.; Márquez-Lucero, A. The evaluation of a low-cost colorimeter for glucose detection in salivary samples. Sensors 2017, 17, 2495. [Google Scholar] [CrossRef] [PubMed]
- Christianini, N.; Shawe-Taylor, J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods; Cambridge University Press: Cambridge, UK, 2000; ISBN 0521780195. [Google Scholar]
- Mika, S.; Ratsch, G.; Weston, J.; Scholkopf, B.; Mullers, K.R. Fisher discriminant analysis with kernels. In Proceedings of the 1999 IEEE Signal Processing Society Workshop, Madison, WI, USA, 25–25 August 1999; pp. 41–48. [Google Scholar] [CrossRef]
- Rish, I. An empirical study of the naive Bayes classifier. In Proceedings of the IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, Seattle, WA, USA, 4 August 2001; Volume 3, pp. 41–46. [Google Scholar]
- Breiman, L.; Friedman, J.H.; Olshen, R.A.; Stone, C.J. Classification and Regression Trees; Chapman & Hall: Boca Raton, FL, USA, 1984; ISBN 0412048418. [Google Scholar]
- Muja, M.; Lowe, D.G. Fast approximate nearest neighbors with automatic algorithm configuration. In Proceedings of the VISAPP International Conference on Computer Vision Theory and Applications, Lisboa, Portugal, 5–8 February 2009; pp. 331–340. [Google Scholar]
Component | Current Consumption (µA) | Approximate Cost for Large Quantities ($) |
---|---|---|
M24LR04E | 400 | 0.3 |
TCS34725 | 235 | 1.5 |
LED | 2000 | 0.03 |
ATTiny85 | 300 | 1.0 |
Case and other passive components | - | 1.0 |
Total | 2935 | 3.9 |
Color | Sample 1 (Red) | Sample 2 (Green) | Sample 3 (Blue) |
---|---|---|---|
Average HUE | 0.31 | 150.41 | 205.34 |
Normalized HUE deviation | 0.09% | 0.03% | 0.12% |
Normalized Difference HUE between sensors | 0.06% | 0.19% | 0.46% |
Average Saturation parameter | 0.62 | 0.32 | 0.58 |
Normalized Saturation deviation | 0.6% | 0.14% | 1.03% |
Normalized Difference Saturation between sensors | 1.06% | 1.04% | 1.00% |
Average Value | 0.62 | 0.38 | 0.47 |
Normalized Saturation deviation | 0.47% | 0.64% | 0.45% |
Normalized Difference Value between sensors | 0.4% | 1.0% | 0.70% |
Fruit | Days | H | S | V | L* | a* | b* |
---|---|---|---|---|---|---|---|
Golden Apple | Day 0 | 59.4 | 0.48 | 0.40 | 41.1 | −7.3 | 26.6 |
Day 15 | 40.0 | 0.60 | 0.46 | 40.9 | 3.6 | 30.2 | |
Δ% | 5.4 | 12.00 | 6.00 | 0.1 | −4.3 | 1.4 | |
Red Apple | Day 0 | 17.4 | 0.58 | 0.51 | 37.9 | 20.6 | 22.1 |
Day 15 | 23.2 | 0.60 | 0.51 | 39.4 | 17.6 | 25.8 | |
Δ% | 1.6 | 2.00 | 0.00 | 1.5 | 1.2 | 1.4 | |
Banana | Day 0 | 41.2 | 0.55 | 0.44 | 40.0 | 2.0 | 27.1 |
Day 9 | 72.5 | 0.13 | 0.36 | 38.0 | −2.4 | 6.0 | |
Δ% | 8.7 | 42.00 | 8.00 | 2.0 | −1.7 | 8.2 |
Fruit | Classifier | TP % | FP % | FN % | TN % | Accu. % |
---|---|---|---|---|---|---|
Golden Apple | Naive Bayes | 73.66 | 6.67 | 26.33 | 93.33 | 83.50 |
Linear Discriminant Analysis | 73.00 | 8.67 | 27.00 | 91.33 | 82.17 | |
Decision Tree | 76.33 | 12.33 | 23.67 | 87.67 | 82.00 | |
Nearest Neighbor | 81.33 | 13.33 | 18.67 | 86.67 | 84.00 | |
Nearest Neighbor k = 5 | 80.00 | 13.67 | 20.00 | 86.33 | 83.17 | |
SVM | 68.00 | 2.00 | 32.00 | 98.00 | 83.00 | |
Banana | Naive Bayes | 90.00 | 4.00 | 10.00 | 96.00 | 93.00 |
Linear Discriminant Analysis | 90.50 | 5.00 | 9.50 | 95.00 | 92.75 | |
Decision Tree | 84.50 | 3.50 | 15.50 | 96.50 | 90.50 | |
Nearest Neighbor | 87.00 | 13.00 | 13.00 | 87.00 | 87.00 | |
Nearest Neighbor k = 5 | 87.00 | 3.00 | 13.00 | 97.00 | 92.00 | |
SVM | 90.50 | 5.50 | 9.50 | 94.50 | 92.50 | |
Red apple | Naive Bayes | 73.33 | 46.67 | 26.67 | 53.33 | 63.33 |
Linear Discriminant Analysis | 86.67 | 40.00 | 13.33 | 60.00 | 73.33 | |
Decision Tree | 63.33 | 40.00 | 36.37 | 60.00 | 61.67 | |
Nearest Neighbor | 60.00 | 66.67 | 40.00 | 33.33 | 46.67 | |
Nearest Neighbor k = 5 | 80.00 | 36.67 | 20.00 | 63.33 | 71.67 | |
SVM | 96.67 | 90.00 | 3.3 | 10.00 | 53.33 | |
QDA | 86.67 | 26.67 | 13.33 | 73.33 | 80.00 |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Lazaro, A.; Boada, M.; Villarino, R.; Girbau, D. Color Measurement and Analysis of Fruit with a Battery-Less NFC Sensor. Sensors 2019, 19, 1741. https://doi.org/10.3390/s19071741
Lazaro A, Boada M, Villarino R, Girbau D. Color Measurement and Analysis of Fruit with a Battery-Less NFC Sensor. Sensors. 2019; 19(7):1741. https://doi.org/10.3390/s19071741
Chicago/Turabian StyleLazaro, Antonio, Marti Boada, Ramon Villarino, and David Girbau. 2019. "Color Measurement and Analysis of Fruit with a Battery-Less NFC Sensor" Sensors 19, no. 7: 1741. https://doi.org/10.3390/s19071741