Determination of the Volume of Unboiled Arecanut Kernels using Image Processing

Salunke, Ajit; Honnungar, Sunilkumar; Vadivel, Arunachalam

doi:10.2478/ata-2025-0001

Abstract

Arecanut cultivation, though widespread in India, accounting for 50% of the global output, relies on visual attributes such as colour, texture, size, shape, and tactile traits like softness for quality assessment. The paradigm shift towards alternate industrial and medical applications emphasises the need for grading based on physical and mechanical properties. Real-time measurement of crucial physical parameters, particularly the volume of kernels, poses a considerable challenge that is not yet effectively addressed. The study focused on estimating the volume of unboiled arecanut kernels cultivated in Goa, India, utilising real-time image processing with segmentation. The devised system employed two cameras positioned at right angles to determine the diameters and thickness of the kernels. The research findings disclosed an average difference of 2.67% between volume estimations by image processing and the water displacement method. The paired t-test results revealed no significant difference (P >0.05) between the volumes obtained from the two methods. Additionally, Spearman’s rank correlation coefficient demonstrated a very strong correlation between the two measurement techniques. The proposed method overcoming the limitations of the ellipsoidal method achieved 97.33% accuracy with the potential for real-time measurement of the kernel volumes, providing a basis for automated quality grading based on density and porosity.

References

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.
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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.
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article
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
Search in Google Scholar Back to article

Determination of the Volume of Unboiled Arecanut Kernels using Image Processing

Abstract

Paradigm

My account