TY - GEN
T1 - Edge-AI Implementation for Milk Adulteration Detection
AU - Mhapsekar, Rahul Umesh
AU - Abraham, Lizy
AU - O'Shea, Norah
AU - Davy, Steven
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Milk is an important source of nutrition consumed by most of the world's population. The introduction of adulterants (i.e. starch, sucrose, formaldehyde, etc) into milk, impacts quality as well as can cause severe health problems in the people consuming it. The use of the Internet of Things (IoT) with Artificial Intelligence (AI) can help predict and classify milk adulterants in real-time thereby informing dairy processors about the quality of milk during intake to the plant from dairy farms. The Edge-AI-based architecture allows the implementation of in-situ milk adulteration detection techniques in dairy processing, hence providing real-time monitoring of milk quality. The proposed architecture in this paper uses an edge device (Jetson Nano) to process the Fourier Transformed Infrared (FTIR) based data to classify different adulterants present in the milk dataset. A Convolutional Neural Network (CNN) model is used to address the classification problem. The edge device achieved 94.87% accuracy in classifying the adulterants present in the milk dataset. The model is successfully trained on the edge device providing benefits such as data governance, and flexible options for customizing the machine learning model at the edge in real time based on variations in adulterants.
AB - Milk is an important source of nutrition consumed by most of the world's population. The introduction of adulterants (i.e. starch, sucrose, formaldehyde, etc) into milk, impacts quality as well as can cause severe health problems in the people consuming it. The use of the Internet of Things (IoT) with Artificial Intelligence (AI) can help predict and classify milk adulterants in real-time thereby informing dairy processors about the quality of milk during intake to the plant from dairy farms. The Edge-AI-based architecture allows the implementation of in-situ milk adulteration detection techniques in dairy processing, hence providing real-time monitoring of milk quality. The proposed architecture in this paper uses an edge device (Jetson Nano) to process the Fourier Transformed Infrared (FTIR) based data to classify different adulterants present in the milk dataset. A Convolutional Neural Network (CNN) model is used to address the classification problem. The edge device achieved 94.87% accuracy in classifying the adulterants present in the milk dataset. The model is successfully trained on the edge device providing benefits such as data governance, and flexible options for customizing the machine learning model at the edge in real time based on variations in adulterants.
KW - Convolutional Neural Networks (CNNs)
KW - Edge processing
KW - Edge-AI
KW - Internet of Things (IoT)
KW - Milk adulteration detection
UR - http://www.scopus.com/inward/record.url?scp=85147710443&partnerID=8YFLogxK
U2 - 10.1109/GCAIoT57150.2022.10019173
DO - 10.1109/GCAIoT57150.2022.10019173
M3 - Conference contribution
AN - SCOPUS:85147710443
T3 - 2022 IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIoT 2022
SP - 108
EP - 113
BT - 2022 IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIoT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Global Conference on Artificial Intelligence and Internet of Things, GCAIoT 2022
Y2 - 18 December 2022 through 21 December 2022
ER -