Chemical ripening and contaminations detection using neural networks-based image features and spectrometric signatures

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Published:
Dec 19, 2021
Issue
Vol. 30 No. 1/4 (2021)
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Original research papers
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R Roopalakshmi
Manipal Institute of Technology, Manipal, India
https://orcid.org/0000-0001-7931-4608

DOI: https://doi.org/10.22630/MGV.2021.30.1.2
Keywords : chemical ripening, arsenic contamination, visual features, IR spectral signatures
Abstract
In this pandemic-prone era, health is of utmost concern for everyone and hence eating good quality fruits is very much essential for sound health. Unfortunately, nowadays it is quite very difficult to obtain naturally ripened fruits, due to existence of chemically ripened fruits being ripened using hazardous chemicals such as calcium carbide. However, most of the state-of-the art techniques are primarily focusing on identification of chemically ripened fruits with the help of computer vision-based approaches, which are less effective towards quantification of chemical contaminations present in the sample fruits. To solve these issues, a new framework for chemical ripening and contamination detection is presented, which employs both visual and IR spectrometric signatures in two different stages. The experiments conducted on both the GUI tool as well as hardware-based setups, clearly demonstrate the efficiency of the proposed framework in terms of detection confidence levels followed by the percentage of presence of chemicals in the sample fruit.

Article Details

How to Cite
Roopalakshmi, R. (2021). Chemical ripening and contaminations detection using neural networks-based image features and spectrometric signatures. Machine Graphics and Vision, 30(1/4), 23–43. https://doi.org/10.22630/MGV.2021.30.1.2
References

S. Ahmad, A. K. Thompson, I. A. Hafiz, and A. A. Asi. Effect of temperature on the ripening behavior and quality of banana fruit. International Journal of Agriculture and Biology, 3(2):224–227, 2001. http://www.fspublishers.org/published_papers/87164_..pdf.

S. Ansari and S. Salankar. An overview on thermal image processing. In V. K. Solanki, V. B. Semwal, R. Gonz´alez-Crespo, and V. Bijalwan, editors, Proc. 2nd Int. Conf. Research in Intelligent and Computing in Engineering RICE 2017, volume 10 of Annals of Computer Science and Information Systems, pages 117–120, Gopeshwar, Uttrakhand, India, 24-26 Mar 2017. Polish Information Processing Society, Warsaw. https://doi.org/10.15439/2017R111. (Crossref)

A. A. Bhosale and K. K. Sundaram. Nondestructive method for ripening prediction of papaya. Procedia Technology, 19:623–630, 2015. Part of special issue: L. Moldovan, editor, Proc. 8th Int. Conf. Interdisciplinarity in Engineering, INTER-ENG 2014, 9-10 Oct 2014, Tirgu Mures, Romania. https://doi.org/10.1016/j.protcy.2015.02.088. (Crossref)

F. J. Garcia-Ramos, C. Valero, I. Homer, et al. Non-destructive fruit firmness sensors: A review. Spanish Journal of Agricultural Research, 3(1):61–73, 2005. https://doi.org/10.5424/sjar/2005031-125. (Crossref)

V. Hallur, B. Atharga, A. Hosur, et al. Design and development of a portable instrument for the detection of artificial ripening of banana fruit. In Int. Conf. Circuits, Communication, Control and Computing, pages 139–140, Bangalore, India, 21-22 Nov 2014. IEEE, 2015. https://doi.org/10.1109/CIMCA.2014.7057776. (Crossref)

M. C. House, M. Nelson, and E. S. Haber. The vitamin A, B, and C content of artificially versus naturally ripened tomatoes. Journal of Biological Chemistry, 81(3):495–504, 1929. https://doi.org/10.1016/S0021-9258(18)63704-4. (Crossref)

R. Karthika, K. V. M. Ragadevi, and N. Asvini. Detection of artificially ripened fruits using image processing. Internatinal Journal of Advanced Science and Engineering Research, 2(1):576–582, 2017. http://www.ijaser.in/journals/view/volume2/issue1/detection-of-artificially-ripened-fruits-using/221.

J. Kathirvelan and R. Vijayaraghavan. An infrared based sensor system for the detection of ethylene for the discrimination of fruit ripening. Infrared Physics & Technology, 85:403–409, 2017. https://doi.org/10.1016/j.infrared.2017.07.022. (Crossref)

S. Kothari and H. Channe. Detection of nutrients and chemicals in food products using sensors in smart phones. Internatinal Journal of Engineering and Computer Science, 4(4):11651–11652, 2015. https://www.ijecs.in/index.php/ijecs/article/view/1696.

A. J. Lakade, K. Sundar, and P. H. Shetty. Gold nanoparticle-based method for detection of calcium carbide in artificially ripened mangoes (Magnifera indica). Food Additives & Contaminants: Part A, 35(6):1078–1084, 2018. https://doi.org/10.1080/19440049.2018.1449969. (Crossref)

A. J. Lakade, Venkataraman V., R. Ramasamy, and P. H. Shetty. NIR spectroscopic method for the detection of calcium carbide in artificial ripening of mangoes (Magnifera indica). Food Additives & Contaminants: Part A, 36(7):989–995, 2019. https://doi.org/10.1080/19440049.2019.1605206. (Crossref)

V. H.-C. Liao and K.-L. Ou. Development and testing of green fluorescent protein-based bacterial biosensor for measuring bioavailable arsenic in contaminated groundwater samples. Environmental Toxicology and Chemistry, 24(7):1624–1631, 2005. https://doi.org/10.1897/04-500R.1. (Crossref)

S. Maheswaran, S. Sathesh, P. Priyadarshini, and B. Vivek. Identification of artificially ripened fruits using smart phones. In 2017 Int. Conf. Intelligent Computing and Control I2C2, pages 1–6, Coimbatore, India, 23-24 Jun 2017. IEEE, 2018. https://doi.org/10.1109/I2C2.2017.8321857. (Crossref)

M. R. Meghana, R. Roopalakshmi, T. E. Nischitha, and P. Kumar. Detection of chemically ripened fruits based on visual features and non-destructive sensor techniques. In D. Pandiana, X. Fernando, Z. Baig, and F. Shi, editors, Proc. Int. Conf. ISMAC in Computational Vision and Bio-Engineering ISMAC-CVB 2018, volume 30 of Lecture Notes in Computational Vision and Biomechanics, pages 865–872, Palladam, India, 16-17 May 2018. Springer, Cham 2019. https://doi.org/10.1007/978-3-030-00665-5 84. (Crossref)

P. P. Ray, S. Pradhan, R. K. Sharma, et al. IoT based fruit quality measurement system. In 2016 Online Int. Conf. Green Engineering and Technologies IC-GET, pages 224–229, Coimbatore, India, 19 Nov 2016. IEEE, 2017. https://doi.org/10.1109/GET.2016.7916620. (Crossref)

R. Roopalakshmi, C. Shastri, P. Hegde, et al. Neural networks-based framework for detecting chemically ripened banana fruits. In H. Sharma, A. Pundir, N. Yadav, et al., editors, Recent Trends in Communication and Intelligent Systems – Proc. Int. Conf. Recent Trends in Communication & Intelligent Systems ICRTCIS 2019, volume of Algorithms for Intelligent Systems, pages 55–61, Jaipur, India, 8-9 Jun 2019. Springer, Singapore 2020. https://doi.org/10.1007/978-981-15-0426-6 6. (Crossref)

R. P. Salunkhe and A. A. Pathil. Image processing for mango ripening stage detection: RGB and HSV method. In 2015 3rd Int. Conf. Image Information Processing ICIIP, pages 362–365, Waknaghat, India, 21-24 Dec 2015. IEEE, 2016. https://doi.org/10.1109/ICIIP.2015.7414796. (Crossref)

V. Srividhya, K. Sujatha, and R. S. Ponmagal. Ethylene gas measurement for ripening of fruits using image processing. Indian Journal of Science and Technology, 9(31):1–7, 2016. https://doi.org/10.17485/ijst/2016/v9i31/93838. (Crossref)

tensorflower gardener and mingxingtan (GitHub nicknames). Running Inception on Cloud TPU, 2021. https://cloud.google.com/tpu/docs/tutorials/inception. [Last accessed Jun 2021].

A. Verma, R. Hegadi, and K. Sahu. Development of an effective system for remote monitoring of banana ripening process. In 2015 IEEE Int. WIE Conf. Electrical and Computer Engineering WIECON-ECE, pages 534–537, Dhaka, Bangladesh, 19-20 Dec 2015. IEEE, 2016. https://doi.org/10.1109/WIECON-ECE.2015.7443987. (Crossref)

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