Classification of Adulterated Food Grain Thermal Images Using Convolutional Neural Networks
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-25088-0_42
Reference13 articles.
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3. Asefa, B.G., Hagos, L., Kore, T., et al.: Computer vision based detection and quantification of extraneous water in raw milk. https://doi.org/10.21203/rs.3.rs-625039/v1, 06 2021
4. Ekambaram, D., Kumar, V.: Identification of defects in casting products by using a convolutional neural network, June 2022. https://www.researchgate.net/publication/361985340_Identification_of_Defects_in_Casting_Products_by_using_a_Convolutional_Neural_Network
5. Estrada-Pérez, L.V., Pradana-López, S., Pérez-Calabuig, A.M., Mena, M.L., Cancilla, J.C., Torrecilla, J.S.: Thermal imaging of rice grains and flours to design convolutional systems to ensure quality and safety (2021). https://www.sciencedirect.com/science/article/pii/S0956713520304886
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