Leveraging VGG-19 for automated fruit classification in smart agriculture

Fruit classification has become increasingly important in a wide range of industrial and consumer-oriented applications. Automated fruit classification systems can significantly enhance efficiency by accurately identifying fruit varieties and supporting informed decisions. In this research, we propose a fast, accurate, and robust fruit classification approach leveraging Deep Learning (DL) techniques. The proposed approach is a fine-tuned, pretrained Visual Geometry Group-19 (VGG-19) convolutional neural network model, known for its depth and superior performance in image recognition and classification tasks. Unlike existing approaches, our proposed approach addresses the unique visual challenges of fruit images, including inter-class similarity, intra-class variability, occlusions, background clutter, and varying illumination conditions, which enhances generalization across both controlled and real-world datasets. This approach is rigorously evaluated using two distinct datasets obtained from Kaggle. Dataset 1 comprises high-resolution images captured under controlled conditions, while Dataset 2, derived from the Kaggle 360 Fruits dataset, contains diverse real-world images with varying backgrounds, lighting conditions, and occlusions. Experimental results demonstrate that the proposed model achieves exceptional classification accuracy, recording 99.65% on Dataset 1 and 97.98% on Dataset 2. The above findings underline the accuracy and stability of the fine-tuned VGG-19 model in processing clean and complicated images and pose a viable and scalable real-time fruit classification approach.