An Automated Machine Learning Models for Feature-Based Classification of Raisin Variety Differentiation

DOI

10.2991/978-94-6463-738-0_25How to use a DOI?

Keywords

Raisin classification; Machine learning; Morphological features; Agricultural datasets; Food quality control; Decision trees; Neural networks; Support vector machines

Abstract

The Raisin Dataset contains the morphological data for two different raisin varieties of Kecimen and Besni and forms the very basis of an essential approach to classification methods in agriculture and food science. This deals with the effort of a machine learning algorithm in the feature-based classification of raisin varieties. Then, the dataset is preprocessed; the main attributes are filtered out, and classification models like as decision trees, support vector machines, and neural networks are advanced. Models were calculated on the basis of performance metric accuracy, precision, and recall. This study presented the capability of automated classification methodologies in quality control and identification of agricultural products. This research underlines the importance of combining data-driven insights with machine learning in furthering agricultural applications. This research highlights the potential for scalability and adaptability of machine learning models to other agricultural classifications beyond raisin varieties. By leveraging morphological features, the study bridges the gap concerning conventional agricultural practices and modern computational techniques. The findings emphasize the part of data preprocessing and feature selection in enhancing model accuracy and reliability.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Gaurang Sawant
AU  - G. S. Pradeep Ghantasala
AU  - R. Rajesh Sharma
AU  - Pellakuri Vidyullatha
AU  - Gaganpreet Kaur
AU  - Akey Sungheetha
PY  - 2025
DA  - 2025/06/22
TI  - An Automated Machine Learning Models for Feature-Based Classification of Raisin Variety Differentiation
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 299
EP  - 308
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_25
DO  - 10.2991/978-94-6463-738-0_25
ID  - Sawant2025
ER  -