Abstract
India holds the title of being the top banana producer globally, contributing approximately 25% of the total banana production. However, exporting it can be a challenge because of its shelf-life. To propose the best possible shelf-life extension methodology, it is important to classify based on the banana varieties and ripening stages to ensure sustainable growth and nutritional value. There are still not enough data sets with different varieties of bananas and their respective ripening stages. A review of research publications from the last five years has been conducted using electronic databases like Scopus, Google Scholar, and ResearchGate, as well as the details of publicly accessible dataset repository sites. The dataset captures images of different varieties of banana fruit as well as its respective different stages of ripening. Banana varieties considered include Robusta (MusaAA), Dwarf Cavendish (Musaacuminata), Nanjangud bananas, and Red bananas (Musa acuminata). The dataset contains over 41,900 processed images. In this paper, the authors provide researchers with an opportunity to develop and investigate machine learning and deep learning algorithms that are used to predict and extend the shelf life of banana fruits.
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Manasa, T.N., Pushpalatha, M.P. (2024). Novel Dataset Creation of Varieties of Banana and Ripening Stages for Machine Learning Applications. In: Kaur, H., Jakhetiya, V., Goyal, P., Khanna, P., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2023. Communications in Computer and Information Science, vol 2010. Springer, Cham. https://doi.org/10.1007/978-3-031-58174-8_32
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