Research Article
Fruits Classification and Detection Application Using Deep Learning
Table 1
Class names and numbers of dataset-I.
| Class names | Number of samples |
| Acerolas | 24 | Apples | 38 | Apricots | 30 | Avocados | 26 | Bananas | 42 | Blackberries | 37 | Blueberries | 32 | Cantaloupes | 31 | Cherries | 33 | Coconuts | 26 | Figs | 26 | Grapefruits | 31 | Grapes | 38 | Guava | 33 | Kiwifruit | 36 | Mangos | 34 | Olives | 23 | Oranges | 35 | Passion fruit | 22 | Peaches | 27 | Pears | 32 | Pineapples | 34 | Plums | 31 | Pomegranates | 30 | Raspberries | 39 | Strawberries | 46 | Tomatoes | 46 | Watermelons | 39 | Lemons | 29 | Limes | 29 |
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