Automatic Optical Imaging System for Mango Fruit using Hyperspectral Camera and Deep Learning Algorithm

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Pathogenicity tests of different mango species
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Published: May 17, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i5s.6635
Keywords:
hyperspectral camera, internet of things, image processing, deep learning algorithm, quality recognition

Main Article Content

Pushpendra Kumar Verma
Associate Professor, School of Computer Science and Application, IIMT University, Meerut Uttar Pradesh, India-250001
Paresh Pathak
Assistant Professor, School of Computer Science and Application, IIMT University, Meerut Uttar Pradesh, India-250001
Bhupendra Kumar
Assistant Professor, School of Computer Science and Application, IIMT University, Meerut Uttar Pradesh, India-250001
Himani
Assistant Professor, School of Computer Science and Application, IIMT University, Meerut Uttar Pradesh, India-250001
Preety
Assistant Professor, Faculty of Management, Swami Vivekanand Subharti University Meerut ,Uttar Pradesh, India-25002

Abstract

This research paper explores focused on developing an automatic mango fruit quality detection system using a combination of artificial intelligence and the Internet of Things technologies. The system utilizes a hyperspectral camera to capture images of the mango fruit and image processing techniques to analyze the images. Deep learning algorithms are employed to classify the mango fruit based on quality parameters such as ripeness, size, and color. The proposed system aims to automate the mango fruit quality inspection process, improve the accuracy of quality assessment, and reduce human error. The results of this research could have applications in the food industry, specifically in the field of fruit quality inspection and sorting. Mango Fruit, Hyperspectral Camera, Image Processing, Deep Learning algorithms, Quality Recognition.

Article Details

How to Cite
Verma, P. K. ., Pathak, P. ., Kumar, B. ., Himani, H., & Preety, P. (2023). Automatic Optical Imaging System for Mango Fruit using Hyperspectral Camera and Deep Learning Algorithm. International Journal on Recent and Innovation Trends in Computing and Communication, 11(5s), 112–117. https://doi.org/10.17762/ijritcc.v11i5s.6635
Issue
Vol. 11 No. 5s (2023): Special Issue on Advances in Computing Systems for Web-Based Machine Learning Applications
Section
Articles

References

P. Pathmanaban, B. Gnanavel, S.S. Anandan Recent application of imaging techniques for fruit quality assessmentTrends in Food Science & Technology, 94, pp. 32-42, 2019.

T.O. Peng, M. Aizzat Zakaria, A. Fakhri, A. Nasir, A.P.P.A. Majeed, T.C. Young, L. Ng, C. YewAutonomous tomato harvesting robotic system in greenhouses: Deep learning classificationMekatronika-Journal of Intelligent Manufacturing & Mechatronics, 1 pp. 80-86, 2019.

H. J. Xin Huajia, “Application of computer vision in mango quality testing,” Agricultural Mechanization Research, vol. 41, no. 9, pp. 190–193, 2019.

C. McCool, T. Perez, and B. Upcroft, “Mixtures of lightweight deep convolutional neural networks: Applied to agricultural robotics,” IEEE Robotics and Automation Letters, vol. 2, no. 3, pp. 1344–1351, 2017.

J. Ma, K. Du, F. Zheng, L. Zhang, Z. Gong, and Z. Sun, “A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network,” Computers and electronics in agriculture, vol. 154, pp. 18–24, 2018.

C. S. Nandi, B. Tudu, and C. Koley, “A machine vision technique for grading of harvested mangoes based on maturity and quality,” IEEE sensors Journal, vol. 16, no. 16, 2021, pp. 6387–6396, 2021.

Cen, H., He, Y., & Ying, Y.. Hyperspectral imaging for food quality analysis and control: a review of recent advances. Critical reviews in food science and nutrition, 57(14), 3032-3042 in 2017.

Zhu, Q., Zhang, C., & Lu, R.. Advances in hyperspectral imaging technology for food quality and safety analysis and assessment: A review. Journal of food engineering, 220, 1-15 in 2018.

Huang, Y., Yu, H., Chen, Y., Zhang, B., & Fu, X.. Recognition of fruit quality and ripeness degree based on a hyperspectral imaging system. Journal of food engineering, 207, 52-60 in 2017.

Sari, E. M., & Yanar, N. . An efficient deep learning approach for fruit recognition using convolutional neural networks. Computers and Electronics in Agriculture, 162, 25-32, 2020.

A. Bhargava and A. Bansal, “Automatic detection and grading of multiple fruits by machine learning,” Food Analytical Methods, vol. 13, no. 3, pp.751–761, 2020.

S. Munera, J. Gomez-Sanchis, N. Aleixos, J. Vila-Frances, G. Colelli, S. Cubero, E. Soler, J. Blasco Discrimination of common defects in loquat fruit cv. ‘Algerie’ using hyperspectral imaging and machine learning techniques Postharvest Biology and Technology, 171, 10.1016/j.postharvbio.2020.111356, 2021.

L. Le, A. Patterson, and M. White, “Supervised autoencoders: Improving generalization performance with unsupervised regularizers,” in Advances in Neural Information Processing Systems, pp. 107–117. 2018.

Ranganathan, G. "A Study to Find Facts Behind Preprocessing on Deep Learning Algorithms." Journal of Innovative Image Processing (JIIP) 3, no. 01: 66-74, 2021.

A. A. Shvets, V. I. Iglovikov, A. Rakhlin, and A. A. Kalinin, “Angiodysplasia detection and localization using deep convolutional neural networks,” in 2018 17th ieee international conference on machine learning and applications. IEEE, pp. 612–617,2018.

Dhaya, R. "Analysis of Adaptive Image Retrieval by Transition Kalman Filter Approach based on Intensity Parameter." Journal of Innovative Image Processing (JIIP) 3, no. 01, 7-20, 2021.

H. Lu, X. Fu, C. Liu, L.-g. Li, Y.-x. He, and N.-w. Li, “Cultivated land information extraction in uav imagery based on deep convolutional neural network and transfer learning,” Journal of Mountain Science, vol. 14,no. 4, pp. 731–741, 2017.

Dutta, Sayantan, and Ayan Banerjee. "Highly Precise Modified Blue Whale Method Framed by Blending Bat and Local Search Algorithm for the Optimality of Image Fusion Algorithm." Jou of Soft Computing Paradigm (JSCP) 2, no. 04 : 195-208, 2020.

Lai, Kong-Long, and Joy Iong Zong Chen. "Development of Smart Cities with Fog Computing and Internet of Things." Journal of Ubiquitous Computing and Communication Technologies (UCCT) 3, no. 01, 52-60, 2021.

P. Moghadam, D. Ward, E. Goan, S. Jayawardena, P. Sikka, E. Hernandez Plant disease detection using hyperspectral imagin 2017 International Conference on Digital Image Computing - Techniques and Applications, pp. 384-391, 2017.

N. Amin, T. G. Amin, and Y. D. Zhang, “Image-based deep learning automated sorting of date fruit,” Postharvest Biology and Technology, vol. 153, pp. 133–141, 2019.

M. Adkisson, J. C. Kimmell, M. Gupta, and M. Abdelsalam,‘‘Autoencoder-based anomaly detection in smart farming ecosystem,’’ inProc. IEEE Int. Conf. Big Data (Big Data), pp. 3390–3399, 2021. doi: 10.1109/BigData52589.2021.9671613.

H. Park, D. Park, and S. Kim, ‘‘Anomaly detection of operating equipmentin livestock farms using deep learning techniques,’’ Electronics, vol. 10,no. 16, p. 1958, Aug. 2021, doi: 10.3390/electronics10161958.

J. C. Moso, S. Cormier, C. de Runz, H. Fouchal, and J. M. Wandeto,‘‘Anomaly detection on data streams for smart agriculture,’’ Agriculture,vol. 11, no. 11, p. 1083, Nov. 2021, doi: 10.3390/agriculture11111083.

Bhavya K. R and S. Pravinth Raja, “Fruit Quality Prediction using Deep Learning Strategies for Agriculture”, Int J Intell Syst Appl Eng, vol. 11, no. 2s, pp. 301–310, Feb. 2023.