Abstract
Automation of freshness classification is an application of Deep learning and Computer Vision. Normal methods are manual labor which are time-consuming and inefficient. In recent years many new technologies come into place like deep learning and computer vision, since the shape is the main freshness classification, these new technologies are proved to be very useful, since the process has been improved in the terms of both accuracy and time. This paper consists of various image processing techniques used for Radish freshness classification. Comparison among different models has been made on the bases of training and testing accuracy.
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References
Abu-Jamie, T.N., Abu-Naser, S.S., Alkahlout, M.A., Aish, M.A.: Six fruits classification using deep learning (2022)
Arivazhagan, S., Shebiah, R.N., Nidhyanandhan, S.S., Ganesan, L.: Fruit recognition using color and texture features. J. Emerg. Trends Comput. Inf. Sci. 1(2), 90–94 (2010)
Behera, S.K., Rath, A.K., Sethy, P.K.: Maturity status classification of papaya fruits based on machine learning and transfer learning approach. Inf. Process. Agric. 8(2), 244–250 (2021)
Ciptohadijoyo, S., Litananda, W., Rivai, M., Purnomo, M., et al.: Electronic nose based on partition column integrated with gas sensor for fruit identification and classification. Comput. Electron. Agric. 121, 429–435 (2016)
Dubey, S.R., Jalal, A.S.: Fruit and vegetable recognition by fusing colour and texture features of the image using machine learning. Int. J. Appl. Pattern Recogn. 2(2), 160–181 (2015)
Koyama, K., Tanaka, M., Cho, B.H., Yoshikawa, Y., Koseki, S.: Predicting sensory evaluation of spinach freshness using machine learning model and digital images. Plos One 16(3), e0248769 (2021)
Li, Z., Li, F., Zhu, L., Yue, J.: Vegetable recognition and classification based on improved vgg deep learning network model. Int. J. Comput. Intel. Syst. 13(1), 559–564 (2020)
Liu, X., Zhao, D., Jia, W., Ji, W., Sun, Y.: A detection method for apple fruits based on color and shape features. IEEE Access 7, 67923–67933 (2019)
Mukherjee, A., Sarkar, T., Chatterjee, K., Lahiri, D., Nag, M., Rebezov, M., Shariati, M.A., Miftakhutdinov, A., Lorenzo, J.M.: Development of artificial vision system for quality assessment of oyster mushrooms. Food Anal. Methods 1–14 (2022)
Sakib, S., Ashrafi, Z., Siddique, M., Bakr, A.: Implementation of fruits recognition classifier using convolutional neural network algorithm for observation of accuracies for various hidden layers (2019). arXiv:1904.00783
Sarkar, T., Mukherjee, A., Chatterjee, K., Ermolaev, V., Piotrovsky, D., Vlasova, K., Shariati, M.A., Munekata, P.E., Lorenzo, J.M.: Edge detection aided geometrical shape analysis of Indian gooseberry (phyllanthus emblica) for freshness classification. Food Anal. Methods, 1–18 (2022)
Singla, A., Yuan, L., Ebrahimi, T.: Food/non-food image classification and food categorization using pre-trained googlenet model. In: Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management, pp. 3–11 (2016)
Zeng, X., Jie, L.: Time-frequency image recognition based on convolutional neural network. Mach. Electron. 34(5), 25–29 (2016)
Zhang, Y., Wang, S., Ji, G., Phillips, P.: Fruit classification using computer vision and feedforward neural network. J. Food Eng. 143, 167–177 (2014)
Zhu, L., Li, Z., Li, C., Wu, J., Yue, J.: High performance vegetable classification from images based on alexnet deep learning model. Int. J. Agric. Biol. Eng. 11(4), 217–223 (2018)
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Choudhury, T., Singh, T.P., Jain, P., Arunachalaeshwaran, V.R., Sarkar, T. (2023). Radish Freshness Classification Using Deep Learning. In: Nagar, A.K., Singh Jat, D., Mishra, D.K., Joshi, A. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 579. Springer, Singapore. https://doi.org/10.1007/978-981-19-7663-6_46
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DOI: https://doi.org/10.1007/978-981-19-7663-6_46
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