E-Nose for Cashew Apple Ripeness Detection for Autonomous Fruit Plucking

International Journal of Electronics and Communication Engineering
© 2023 by SSRG - IJECE Journal
Volume 10 Issue 10
Year of Publication : 2023
Authors : C. Sudha, K. Jagan Mohan
pdf
How to Cite?

C. Sudha, K. Jagan Mohan, "E-Nose for Cashew Apple Ripeness Detection for Autonomous Fruit Plucking," SSRG International Journal of Electronics and Communication Engineering, vol. 10,  no. 10, pp. 49-56, 2023. Crossref, https://doi.org/10.14445/23488549/IJECE-V10I10P105

Abstract:

Electronic Noses are a helpful instrument in sensor technology. They are used in various industries, including food, cosmetics, agriculture, and others, for quality assurance and ripeness monitoring, process improvement, and product creation. It acts like a human nose, working as an electronic olfactory using an array sensor. Electronic Noses (eNoses) have found several applications in agriculture due to their ability to detect and analyze odours and volatile compounds. During ripening, fruits release Volatile Organic Compounds (VOC), producing aroma. Enose can identify the VOC emission of fruit during its ripening stage and measure the quality of the fruit. ENose was developed for ripe cashew fruit detection using an array of MQ sensors in this proposed work. Their ability to analyze and differentiate aroma profiles makes them essential for ensuring the ripeness quality of cashew fruits and meeting farmer preferences. Pattern recognition of sensors was done using PCA, Random Forest, and DNN. Experimental results on various Tamil Nadu cashew varieties were more accurate in feedforward DNN analysis, and 96.85 % of the cashew fruit samples were detected precisely.

Keywords:

ENose, Cashew, MQ sensor, VOC, DNN.

References:

[1] Pankaj Tyagi et al., “E-Nose: A Low-Cost Fruit Ripeness Monitoring System,” Journal of Agricultural Engineering, vol. 54, no. 1, pp. 1-11, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Manuela Baietto, and Alphus D. Wilson, “Electronic-Nose Applications for Fruit Identification, Ripeness and Quality Grading,” Sensors, vol. 15, no. 1, pp. 899-931, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Wenshen Jia et al., “Electronic Nose-Based Technique for Rapid Detection and Recognition of Moldy Apples,” Sensors, vol. 19, no. 7, pp. 1-11, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Nahid Aghilinategh, Mohammad Jafar Dalvand, and Adieh Anvar, “Detection of Ripeness Grades of Berries Using an Electronic Nose,” Food Science & Nutrition, vol. 8, no. 9, pp. 4919-4928, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Dan Melvin A. Ibarra, Stephen Jubert G. Patajo, and Meo Vincent C. Caya, “Characterization and Classification of Mangifera Indica Ripeness with Electronic Nose Using Fuzzy Logic Algorithm,” 2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Boracay Island, Philippines, pp. 1-6, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] M. Sanjay, and B. Kalpana, “Early Mass Diagnosis of Fusarium Wilt in Banana Cultivations Using an E-Nose Integrated Autonomous Rover System,” International Journal of Applied Sciences and Biotechnology, vol. 5, no. 2, pp. 261-266, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[7] André Gordon et al., “Changes in Phenolic Composition, Ascorbic Acid and Antioxidant Capacity in Cashew Apple (Anacardium Occidentale L.) during Ripening,” Fruits, vol. 67, no. 4, pp. 267-276, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Qinghang Ding et al., “Detection of Fruits in Warehouse Using Electronic Nose,” 2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018), vol. 232, pp. 1-6, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Roberto Beghi et al., “Electronic Nose and Visible-Near Infrared Spectroscopy in Fruit and Vegetable Monitoring,” Reviews in Analytical Chemistry, vol. 36, no. 4, pp. 1-24, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Jiawei Ma et al., “Design of an Artificial Assisted Fruit Picking Device,” International Journal of Computer and Organization Trends, vol. 10, no. 2, pp. 1-3, 2020.
[CrossRef] [Publisher Link]
[11] Alireza Sanaeifar et al., “Development and Application of a New Low-Cost Electronic Nose for the Ripeness Monitoring of Banana Using Computational Techniques (PCA, LDA, SIMCA and SVM),” Czech Journal of Food Sciences, vol. 32, no. 6, pp. 538-548, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Ailane S. de Freitas et al., “Chemometric Analysis of the Volatile Profile in Peduncles of Cashew Clones and Its Correlation with Sensory Attributes,” Journal of Food Science, vol. 86, no. 12, pp. 5120-5136, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Anuradha Gawande, and S.S. Dhande, “Implementation of Fruits Grading and Sorting System by Using Image Processing and Data Classifier,” SSRG International Journal of Computer Science and Engineering, vol. 2, no. 6, pp. 22-27, 2015.
[Google Scholar] [Publisher Link]
[14] John Patrick O. Gabriel, Mary Kris R. Cabunilas, and Jocelyn F. Villaverde, “Cantaloupe Ripeness Detection Using Electronic Nose,” 2022 14th International Conference on Computer and Automation Engineering (ICCAE), Brisbane, Australia, pp. 44-49, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] R. Nino-Esparza et al., “The Use of a VOC Sensor to Measure Freshness of Fruits,” The Canadian Medical and Biological Engineering Society Proceedings, vol. 42, pp. 1-4, 2019.
[Google Scholar] [Publisher Link]
[16] José De Jesús Rubio et al., “Classification via an Embedded Approach,” Designs, vol. 1, no. 1, pp. 1-16, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Onkar A. Apine, and Jyoti P. Jadhav, “Fermentation of Cashew Apple (Anacardium Occidentale) Juice into Wine by Different Saccharomyces Cerevisiae Strains: A Comparative Study,” Indian Journal of Research, vol. 4, no. 3, pp. 6-10, 2015.
[Google Scholar] [Publisher Link]
[18] Ameetha Junaina T. et al., “Using Deep Learning-Based Features and Image Augmentation to Predict Brix Values of Strawberries for Quality Control,” International Journal of Engineering Trends and Technology, vol. 71, no. 7, pp. 326-342, 2023.
[CrossRef] [Publisher Link]
[19] Beatriz Bicalho, and Claudia M. Rezende, “Volatile Compounds of Cashew Apple (Anacardium occidentale L.),” Zeitschrift für Naturforschung C, vol. 56, no. 1-2, pp. 35-39, 2001.
[CrossRef] [Google Scholar] [Publisher Link]