Fruit Image Classification Model Based on MobileNetV2 with Deep Transfer Learning Technique

Due to the rapid emergence and evolution of AI applications, utilization smart imaging devices has increased significantly. Researchers have started using deep learning models, such as CNN, for image classification. Unlike traditional which require a lot features perform well, CNN does not any handcrafted well. It uses numerous filters, extract required from images automatically One issues in horticulture industry is fruit classification, requires an expert with experience. To overcome this issue automated system can classify different types fruits without need human effort. In study, dataset total 26,149 40 was used experimentation. The training test set were randomly recreated divided into ratio 3:1. experiment introduces customized head five layers MobileNetV2 architecture. classification layer model replaced by head, produced modified version called TL-MobileNetV2. addition, transfer retain pre-trained model. TL-MobileNetV2 achieves accuracy 99%, 3% higher than MobileNetV2, equal error rate just 1%. Compared AlexNet, VGG16, InceptionV3, ResNet, better 8, 11, 6, 10%, respectively. Furthermore, obtained 99% precision, recall, F1-score. be concluded that plays big part achieving results, dropout technique helps reduce overfitting learning.