Have a personal or library account? Click to login
Determination of the Volume of Unboiled Arecanut Kernels using Image Processing Cover

Determination of the Volume of Unboiled Arecanut Kernels using Image Processing

Open Access
|Mar 2025

References

  1. AWARE, V. V. – MEHTA, A. K. – BADHE, V. T. – TIWARI, G. S. – MATHUR, S. M. 2013. Physical properties of dried arecanut fruit and kernel. In Indian Journal of Arecanut, Spices, and Medicinal Plants, vol. 15, no. 2, pp. 30–36.
  2. BULAN, R. – DEVIANTI – AYU, E. S. – SITORUS, A. 2020. Effects of moisture content on some engineering properties of arecanut (Areca catechu L.) fruit which are relevant to the design of processing equipment. In INMATEH – Agricultural Engineering, vol. 60, no. 1, pp. 61–70. DOI: https://doi.org/10.35633/inmateh-60-07
  3. FAZEL, F. – GOLMOHAMMADI, A. – SHAHGHOLI, G. – AHMADI, E. 2020. Predictions of the apple bruise volume on the basis of impact energy or maximum contact force using adaptive neuro-fuzzy inference system. In Acta Technologica Agriculturae, vol. 23, no. 3, pp. 118–125. DOI: https://doi.org/10.2478/ata-2020-0019
  4. HUYNH, T. T. M. – TONTHAT L. – DAO, S. V. T. 2022. A vision-based method to estimate volume and mass of fruit/vegetable: Case study of sweet potato. In International Journal of Food Properties, vol. 25, no. 1, pp. 717–732. DOI: https://doi.org/10.1080/10942912.2022.2057528
  5. HUYNH, T. – TRAN, L. – DAO, S. 2020. Real-time size and mass estimation of slender axi-symmetric fruit/vegetable using a single top view image. In Sensors, vol. 20, no. 18, article no. 5406. DOI: https://doi.org/10.3390/s20185406
  6. JANA, S. – PAREKH, R. – SARKAR, B. 2020. A de novo approach for automatic volume and mass estimation of fruits and vegetables. In Optik, vol. 200, article no. 163443. DOI: https://doi.org/10.1016/j.ijleo.2019.163443
  7. KALEEMULLAH, S. – GUNASEKAR, J. J. 2002. PH–Postharvest technology: Moisture-dependent physical properties of arecanut kernels. In Biosystems Engineering, vol. 82, no. 3, pp. 331–338. https://doi.org/10.1006/bioe.2002.0079
  8. KESHAVARZPOUR, F. – ACHAKZAI, A. K. K. 2013. Cantaloupe volume determination using image processing method. In World Engineering & Applied Sciences Journal, vol. 4, no. 2, pp. 17–22. DOI: https://doi.org/10.5829/idosi.weasj.2013.4.2.1112
  9. KHOJASTEHNAZHAND, M. – OMID, M. – TABATABAEEFAR, A. 2010. Determination of tangerine volume using image processing methods. In International Journal of Food Properties, vol. 13, no. 4, pp. 760–770. DOI: https://doi.org/10.1080/10942910902894062
  10. KHOJASTEHNAZHAND, M. – OMID M. – TABATABAEEFAR, A. 2009. Determination of orange volume and surface area using image processing technique. In International Astrophysics, vol. 23, no. 3, pp. 237–242.
  11. KOC, A. B. 2007. Determination of watermelon volume using ellipsoid approximation and image processing. In Postharvest Biology and Technology, vol. 45, no. 3, pp. 366–371. DOI: https://doi.org/10.1016/j.postharvbio.2007.03.010
  12. LEE, D. H. – CHO, Y. – CHOI, J. M. 2017. Strawberry volume estimation using smartphone image processing. In Horticultural Science and Technology, vol. 35, no. 6, pp. 707–716. DOI: https://doi:org/10.12972/kjhst.20170075
  13. MEYER, A. C. – EIFERT, J. – WANG, H. – SANGLAY, G. 2018. Volume estimation of strawberries, mushrooms, and tomatoes with a machine vision system. In International Journal of Food Properties, vol. 21, no. 1, pp. 1867–1874. DOI: https://doi.org/10.1080/10942912.2018.1508156
  14. NYALALA, I. – OKINDA, C. – NYALALA, L. – MAKANGE, N. – CHAO, Q. – CHAO, L. – YOUSAF, K. – CHEN, K. 2019. Tomato volume and mass estimation using computer vision and machine learning algorithms: Cherry tomato model. In Journal of Food Engineering, vol. 263, pp. 288–298. DOI: https://doi.org/10.1016/j.jfoodeng.2019.07.012
  15. OMID, M. – KHOJASTEHNAZHAND, M. – TABATABAEEFAR, A. 2010. Estimating volume and mass of citrus fruits by image processing technique. In Journal of Food Engineering, vol. 100, no. 2, pp. 315–321. DOI: https://doi.org/10.1016/j.jfoodeng.2010.04.015
  16. RASHIDI, M. – GHOLAMI, M. 2008. Determination of kiwifruit volume using ellipsoid approximation and image-processing methods. In International Journal of Agriculture & Biology, vol. 10, no. 4, pp. 375–380.
  17. RASHIDI, M. – GHOLAMI, M. – ABBASSI, S. 2009. Cantaloupe volume determination through image processing. In Journal of Agricultural Science and Technology, vol. 11, pp. 623–631.
  18. SAADATI, N. – POURDARBANI, R. – SABZI, S. – HERNANDEZ-HERNANDEZ, J. L. 2024. Identification of armyworm-infected leaves in corn by image processing and deep learning. In Acta Technologica Agriculturae, vol. 27, no. 2, pp. 92–100. DOI: https://doi.org/10.2478/ata-2024-0013
  19. SABLIOV, C. M – BOLDOR, D. – KEENER, K. M. – FARKAS, B. E. 2002. Image processing method to determine surface area and volume of axi-symmetric agricultural products. In International Journal of Food Products, vol. 5, no. 3, pp. 641–653. DOI: https://doi.org/10.1081/JFP-120015498
  20. SISWANTORO, J. – PRABUWONO, A. S. – ABDULLAH, A. 2014. Volume measurement algorithm for food product with irregular shape using computer vision based on Monte Carlo method. In Journal of ICT Research & Applications, vol. 8, no. 1. pp. 1–17. DOI: https://doi.org/10.5614/itbj.ict.res.appl.2014.8.1.1
  21. SRIDHAR, B. S. 2015. Development of an arecanut (Areca catechu L.) cracker. In International Journal of Applied and Pure Science and Agriculture, vol. 1, no 8, pp. 81–85.
  22. SALUNKE, A. – HONNUNGAR, S. 2020. Quality grading of areca nuts harvested and processed in Goa using image processing and lab view. In AIP Conference Proceedings, vol. 2247, article no. 020017. DOI: https://doi.org/10.1063/5.0004022
  23. SALUNKE, A. – HONNUNGAR, S. 2022. A review of unboiled arecanut drying process and its correlation with mechanical properties. In Materials Today Proceedings, vol. 56, part 5, pp. 2888–2892. DOI: https://doi.org/10.1016/j.matpr.2021.10.186
  24. WILHELM, L. R. – SUTER, D. A. – BRUSEWITZ, G. H. 2004. Chapter 2. Physical properties of food materials. In Food and Process Engineering Technology. Michigan, USA : American Society of Agricultural and Biological Engineers, pp. 23–52. DOI: https://doi.org/10.13031/2013.17550
  25. YEH, Y.-H. F. – LAI, T.-C. – LIU, T.-Y. – LIU, C.-C. – CHUNG, W.-C. – LIN, T.-T. 2014. An automated growth measurement system for leafy vegetables. In Biosystems Engineering, vol. 117, pp 43–50. DOI: https://doi.org/10.1016/j.biosystemseng.2013.08.011
Language: English
Page range: 1 - 9
Published on: Mar 4, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Ajit Salunke, Sunilkumar Honnungar, Arunachalam Vadivel, published by Slovak University of Agriculture in Nitra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.