An Inexpensive AI-Powered IoT Sensor for Continuous Farm-to-Factory Milk Quality Monitoring

The amount of protein and fat in raw milk determines its quality, value in the marketplace, and related payment to suppliers. Technicians use expensive specialized laboratory equipment to measure milk quality in specialized laboratories. The continuous quality monitoring of the milk supply in the su...

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Main Authors: Kaneez Fizza, Abhik Banerjee, Dimitrios Georgakopoulos, Prem Prakash Jayaraman, Ali Yavari, Anas Dawod
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/25/14/4439
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author Kaneez Fizza
Abhik Banerjee
Dimitrios Georgakopoulos
Prem Prakash Jayaraman
Ali Yavari
Anas Dawod
author_facet Kaneez Fizza
Abhik Banerjee
Dimitrios Georgakopoulos
Prem Prakash Jayaraman
Ali Yavari
Anas Dawod
author_sort Kaneez Fizza
collection DOAJ
description The amount of protein and fat in raw milk determines its quality, value in the marketplace, and related payment to suppliers. Technicians use expensive specialized laboratory equipment to measure milk quality in specialized laboratories. The continuous quality monitoring of the milk supply in the supplier’s tanks enables the production of higher quality products, better milk supply chain optimization, and reduced milk waste. This paper presents an inexpensive AI-powered IoT sensor that continuously measures the protein and fat in the raw milk in the tanks of dairy farms, pickup trucks, and intermediate storage depots across any milk supply chain. The proposed sensor consists of an in-tank IoT device and related software components that run on any IoT platform. The in-tank IoT device quality incorporates a low-cost spectrometer and a microcontroller that can send milk supply measurements to any IoT platform via NB-IoT. The in-tank IoT device of the milk quality sensor is housed in a food-safe polypropylene container that allows its deployment in any milk tank. The IoT software component of the milk quality sensors uses a specialized machine learning (ML) algorithm to translate the spectrometry measurements into milk fat and protein measurements. The paper presents the design of an in-tank IoT sensor and the corresponding IoT software translation of the spectrometry measurements to protein and fat measurements. Moreover, it includes an experimental milk quality sensor evaluation that shows that sensor accuracy is ±0.14% for fat and ±0.07% for protein.
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spelling doaj-art-68ebd72b141b4617b207e47d18231ff32025-08-20T03:08:02ZengMDPI AGSensors1424-82202025-07-012514443910.3390/s25144439An Inexpensive AI-Powered IoT Sensor for Continuous Farm-to-Factory Milk Quality MonitoringKaneez Fizza0Abhik Banerjee1Dimitrios Georgakopoulos2Prem Prakash Jayaraman3Ali Yavari4Anas Dawod5Department of Computing Technology, Swinburne University of Technology, Melbourne, VIC 3122, AustraliaDepartment of Computing Technology, Swinburne University of Technology, Melbourne, VIC 3122, AustraliaDepartment of Computing Technology, Swinburne University of Technology, Melbourne, VIC 3122, AustraliaDepartment of Computing Technology, Swinburne University of Technology, Melbourne, VIC 3122, AustraliaDepartment of Computing Technology, Swinburne University of Technology, Melbourne, VIC 3122, AustraliaDepartment of Computing Technology, Swinburne University of Technology, Melbourne, VIC 3122, AustraliaThe amount of protein and fat in raw milk determines its quality, value in the marketplace, and related payment to suppliers. Technicians use expensive specialized laboratory equipment to measure milk quality in specialized laboratories. The continuous quality monitoring of the milk supply in the supplier’s tanks enables the production of higher quality products, better milk supply chain optimization, and reduced milk waste. This paper presents an inexpensive AI-powered IoT sensor that continuously measures the protein and fat in the raw milk in the tanks of dairy farms, pickup trucks, and intermediate storage depots across any milk supply chain. The proposed sensor consists of an in-tank IoT device and related software components that run on any IoT platform. The in-tank IoT device quality incorporates a low-cost spectrometer and a microcontroller that can send milk supply measurements to any IoT platform via NB-IoT. The in-tank IoT device of the milk quality sensor is housed in a food-safe polypropylene container that allows its deployment in any milk tank. The IoT software component of the milk quality sensors uses a specialized machine learning (ML) algorithm to translate the spectrometry measurements into milk fat and protein measurements. The paper presents the design of an in-tank IoT sensor and the corresponding IoT software translation of the spectrometry measurements to protein and fat measurements. Moreover, it includes an experimental milk quality sensor evaluation that shows that sensor accuracy is ±0.14% for fat and ±0.07% for protein.https://www.mdpi.com/1424-8220/25/14/4439Internet of Thingsin-tank milk monitoringIoT sensor
spellingShingle Kaneez Fizza
Abhik Banerjee
Dimitrios Georgakopoulos
Prem Prakash Jayaraman
Ali Yavari
Anas Dawod
An Inexpensive AI-Powered IoT Sensor for Continuous Farm-to-Factory Milk Quality Monitoring
Sensors
Internet of Things
in-tank milk monitoring
IoT sensor
title An Inexpensive AI-Powered IoT Sensor for Continuous Farm-to-Factory Milk Quality Monitoring
title_full An Inexpensive AI-Powered IoT Sensor for Continuous Farm-to-Factory Milk Quality Monitoring
title_fullStr An Inexpensive AI-Powered IoT Sensor for Continuous Farm-to-Factory Milk Quality Monitoring
title_full_unstemmed An Inexpensive AI-Powered IoT Sensor for Continuous Farm-to-Factory Milk Quality Monitoring
title_short An Inexpensive AI-Powered IoT Sensor for Continuous Farm-to-Factory Milk Quality Monitoring
title_sort inexpensive ai powered iot sensor for continuous farm to factory milk quality monitoring
topic Internet of Things
in-tank milk monitoring
IoT sensor
url https://www.mdpi.com/1424-8220/25/14/4439
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