Quality Assessment and Grading of Milk using Sensors and Neural Networks

Kumar, Jagdish; Indira, D. N. V. S. L. S.; Srinivas, Kalyanapu; Kumar, M N Satish

doi:10.1109/icears53579.2022.9752269

Cited by 9 publications

(2 citation statements)

References 6 publications

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“…broadband infrared light source to simultaneously capture multi-wave length feature data [4]. Therefore, assure the milk's quality, it must be examined for the presence of required ingredients and any potential adulterants [5].…”

Section: Introductionmentioning

confidence: 99%

Milk Quality Prediction Using Machine Learning

Bhavsar,

Jobanputra,

Swain

et al. 2023

EAI Endorsed Trans IoT

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Milk is the main dietary supply for every individual. High-quality milk shouldn't contain any adulterants. Dairy products are sold everywhere in society. Yet, the local milk vendors use a wide range of adulterants in their products, permanently altering the evaporated. Using milk that has gone bad can have serious health consequences. On October 18 of this year, the Food Safety and Standards Authority of India (FSSAI), the nation's top food safety authority, released the final result of the National Milk Safety and Quality Survey (NMSQS) and declared the milk readily available in India to be "mostly safe." According to an FSSAI survey, 68.4% of the milk in India is tainted. The quality of milk cannot be checked by any equipment or special system. Milk that has not been pasteurized has not been treated to get rid of harmful bacteria. Infected raw milk may contain Salmonella, Campylobacter, Cryptosporidium, E. coli, Listeria, Brucella, and other dangerous pathogens. These microorganisms pose a major risk to your family's health. Manually analyzing the various milk constituents can be very challenging when determining the quality of the milk. Analyzing and discovering with the aid of machine learning can help with this endeavor. Here a machine learning-based milk quality prediction system is developed. The proposed technology has shown 99.99% classification accuracy.

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Section: Introductionmentioning

confidence: 99%

Milk Quality Prediction Using Machine Learning

Bhavsar,

Jobanputra,

Swain

et al. 2023

EAI Endorsed Trans IoT

View full text Add to dashboard Cite

show abstract

“…Studies that prefer the ML algorithm for modeling milk productivity and quality have obtained high-accuracy prediction data with artificial neural networks (ANN) (87%) and ANN-artificial butterfly optimization algorithms. Shine et al (2018) used ML algorithms to estimate the amount of milk production by considering energy and environmental parameters (Swarup Kumar et al, 2022).…”

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confidence: 99%

Forecasting drinking milk price based on economic, social, and environmental factors using machine learning algorithms

Atalan

2022

Agribusiness

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The study aimed to describe and test machine learning (ML)-based algorithms to evaluate the unit price of drinking milk. The algorithms were applied to the data collected over 8 years in 2014 and 2021 related to the price of drinking milk in Turkey. The economic, social, and environmental factors that have an impact on the unit price of drinking milk were evaluated. Five ML algorithms, including random forest, gradient boosting, support vector machine (SVM), neural network, and AdaBoost algorithms, were utilized to predict the drinking milk unit price. ML also applied hyperparameter tuning with nested cross-validation to calculate the prediction accuracy for each algorithm. The results show that the random forest algorithm based on the features of the ML algorithms has the best performance, with the accuracy of 99.30% for training and 98.10% for testing the dataset. The average accuracy of gradient boosting, SVM, neural network, and AdaBoost are obtained as 97.30%, 96.15%, 95.65%, and 96.05%, respectively. Random forest performed best as the target variable with the lowest deviation values of mean squared error (MSE) (0.004), root mean square error (RMSE) (0.060), and mean

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Machine Learning Approaches for Dairy(Milk) Quality Assurance

Rajini,

Sravani

2024

Lecture Notes in Electrical Engineering

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Quality Assessment and Grading of Milk using Sensors and Neural Networks

Cited by 9 publications

References 6 publications

Milk Quality Prediction Using Machine Learning

Milk Quality Prediction Using Machine Learning

Forecasting drinking milk price based on economic, social, and environmental factors using machine learning algorithms

Machine Learning Approaches for Dairy(Milk) Quality Assurance

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