Abstract
Adulteration is a major cause of concern for the food industry pertaining to health of consumers as well as economical value in the market. Rice and paddy being one of the staple diets in India, are of utmost importance when it comes to the detection and treatment of impurities and hence, in this study, various works in the same domain are examined and their limitations are brought to a close with the introduction of a novel methodology. The objective of this proposed approach is to tackle the existing problem of food grain adulteration by applying deep learning based thermal image processing techniques on thermal image samples of various types of rice and paddy grains. The methodology put forth yields an accuracy of 95% in successfully differentiating between pure and impure grains images and hence accomplish the task of adulteration detection.
V. Ponnusamy—Supervised the project and aided in data collection.
P. Anand—Worked on thermal image processing and deep learning based model development.
V. Bhatt—Worked on data manipulation, model creation and testing.
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Ponnusamy, V., Anand, P., Bhatt, V. (2023). Classification of Adulterated Food Grain Thermal Images Using Convolutional Neural Networks. In: Shaw, R.N., Paprzycki, M., Ghosh, A. (eds) Advanced Communication and Intelligent Systems. ICACIS 2022. Communications in Computer and Information Science, vol 1749. Springer, Cham. https://doi.org/10.1007/978-3-031-25088-0_42
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DOI: https://doi.org/10.1007/978-3-031-25088-0_42
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