Local Fruit Classification and Recognition using Transfer Learning Models

Abstract

The practice of automatically classifying images is gaining popularity. Many of us have very little knowledge of the local fruits, yet even in that case, we can vouch to their quality. In our study, we'll talk about a deep learning based system that can distinguish local fruits automatically. Fruit identification is a highly common activity, but automatically classifying fruits based on their placements, shapes, colors, and other attributes is a difficult task. Our study involved the collection of samples from several local areas, followed by the use of various transfer learning models, including VGG-19, Inception-v3, MobileNet, etc. MobileNet provided us with the highest accuracy of 99.53% among them. A top model was also suggested depending on the accuracy of our training. We used 60% of the image data from the 3240 total samples for training, for the purpose of validation we use 20% of the image data, and 20% of the total image data for testing. We received a satisfactory outcome after training and testing. Local fruits are classified as a consequence of this research model, which can be useful for everyday fruit identification.