Machine learning as a way to be more precise when defining milk quality classification

Resumo

Objective: Machine learning algorithm that manages to have a high rate of use in relation to the prediction of the evaluation of milk quality.

Methodology: Is explanatory in nature, quantitative and qualitative methods were used, and data was used to understand the final analyses.

Originality: The article shows how the models were developed, the tests applied before the implementation of the models, the utilization rate of each model and also an analysis of which is the most efficient model for a specific situation.

Mains results: The specifications of each machine learning model and its impact on the development of the models that were used in the work were determined by tests and applications made in the Python programming language; the positive and negative results were considered to arrive at a final position on what was the best way to use the algorithms in this case.

Theorical contributions: This work contributes to the literature on computer science world and in agricultural world too.

Social contributions: The article concludes that the application of Machine learning models in milk quality classification can help many companies or organizations that need to streamline processes and increase the accuracy rate when measuring milk classification, to have a high improvement in this process and consequently increase productivity and profitability.