Methods for Classifying Fruit and Vegetable Ripe Levels: A Systematic Review
DOI:
https://doi.org/10.70609/gtech.v8i4.5067Keywords:
deep learning, fruit classification, vegetable classification, machine learningAbstract
Maturity classification of fruits and vegetables is important to ensure product quality in the agricultural industry. The aim of this research is to optimize harvest and distribution times using deep learning and machine learning methods. A Systematic Literature Review (SLR) was used to identify effective classification methods and models. The dominant method is image processing (65%), followed by machine learning (50%) and deep learning (42.5%). Models such as CNN, AlexNet, and ResNet-50 show high accuracy. Performance evaluation uses metrics such as accuracy, precision, recall, and F1-score. To improve accuracy, future research is recommended to collect more diverse datasets and use hybrid methods. Development of computing infrastructure and workforce training are also necessary for the application of this technology in the agricultural industry.
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Copyright (c) 2024 Rama Saktriawindarta , Kusrini Kusrini
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