Modify Resnet 101: Quality Evaluation of Fruit Recognition and Classification based on Deep Learning - Ahmed Khorsheed Mohammed , Nareen O. M. Salim (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Recognizing and categorizing fruits is a unique challenge in the field of computer vision due to the wide variation in their appearance, shape, and texture. In this research, the effectiveness of an enhanced ResNet-101 deep convolutional neural network (CNN) architecture to achieve higher accuracy in fruit recognition and classification is explored compared to other deep learning (DL) models. The improved ResNet-101 model leverages its depth, skip connections, and pre-trained weights to capture intricate and distinctive features, making it particularly adept at handling complex visual tasks. In our experiments, it was observed that this enhanced ResNet-101 model outperforms several other cutting-edge DL models when applied to a diverse and demanding fruit classification dataset. The model's robustness in dealing with a wide range of fruits, including those with subtle variations and occlusions, is highlighted in our study. Furthermore, the significance of tuning hyperparameters, applying data preprocessing techniques, and utilizing data augmentation strategies is delved into. These aspects are deemed crucial for optimizing the performance of the enhanced ResNet-101 model. By striking the right balance between model complexity and dataset size, the issue of overfitting is addressed while still achieving exceptional accuracy. In essence, the presented model showcases enhanced accuracy performance within 15 epochs, achieving an impressive 99.97% accuracy across a dataset comprising 40 different types of fruits. The findings underscore the superior performance of the proposed model, surpassing the efficacy of several commonly employed methods in current use. This model's effectiveness and robustness for fruit recognition and classification are demonstrated. By reducing the number of trainable parameters by 81%, the overall complexity of the model is decreased. Faster training times, fewer computational resources required, potentially improved generalization, and a reduced likelihood of overfitting can be achieved through this reduction in complexity.