Multilabel Fruit Classification using Deep Learning

ABSTRACT

This study examines the use of Deep Learning techniques for fruit categorization and presents a number of real-world use situations where this technology outperforms more conventional approaches. With a major focus on fruit-related activities, the study explores the possibilities of Deep Learning-powered systems in fields including agriculture, horticulture, and botany. Sorting ripe fruits, seeing rotten fruits, managing inventories, and spotting fruit diseases are the four main use cases mentioned in the article. For instance, the capacity to detect rotten fruits helps with quality control and waste reduction while the automated separation of ripe from unripe fruits improves packaging efficiency. The research also emphasizes the potential of Deep Learning models in inventory management for supply chain optimization and stock level assurance.

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