Machine Learning Applied to Fruit Quality Assessment

Authors

  • Armano Barros Alves Junior Universidade Federal do Tocantins - Campus Palmas
  • Warley Gramacho da Silva Universidade Federal do Tocantins - Campus Palmas

DOI:

https://doi.org/10.20873/uft.2675-3588.2023.v4n2.p83-86

Keywords:

Neural Networks, Deep Learning, Classification

Abstract

The present research project aimed to explore the use of computer vision and machine learning techniques for the analysis and classification of fruits, focusing on quality and post-harvest control. By employing segmentation and feature extraction algorithms, it was possible to identify important attributes of fruits, such as color, shape, and texture, enabling a more objective and precise evaluation. The application of artificial neural networks, such as the MultiLayer Perceptron and Convolutional Neural Network, allowed for the automatic classification of fruits based on their physical characteristics, facilitating the detection of defects and separation of fruits at different stages of ripeness. The results obtained demonstrated the potential of these techniques for automating the fruit selection and classification process, contributing to the improvement of agricultural production efficiency and quality.

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Published

2023-10-16

How to Cite

[1]
Barros Alves Junior, A. and Gramacho da Silva, W. 2023. Machine Learning Applied to Fruit Quality Assessment. Academic Journal on Computing, Engineering and Applied Mathematics. 4, 2 (Oct. 2023), 83–86. DOI:https://doi.org/10.20873/uft.2675-3588.2023.v4n2.p83-86.

Issue

Vol. 4 No. 2 (2023)

Section

Special Issue

Categories

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Copyright (c) 2023 Armano Barros Alves Junior, Warley Gramacho da Silva

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