Classification bootstrap resnet50 for potato diseases datasets - Hasanal Fachri Satia Simbolon (Jurnal Mantik, 2024)
Potato diseases can have negative impacts on crop production, food security, income generation, trade competitiveness, consumer health, environmental sustainability, etc. Therefore, it is important to prevent, monitor, diagnose, treat, and control potato diseases using various methods such as cultural practices (e.g., crop rotation, intercropping), biological control (e.g., beneficial insects), chemical control (e.g., fungicides), physical control (e.g., pruning), etc.Classification Bootstrap ResNet50 is a topic related to image classification using a deep learning model called ResNet50. ResNet50 is a convolutional neural network that has 50 layers and can classify images into 1000 object categories, such as animals, plants, vehicles, etc. ResNet50 is based on the concept of residual learning, which means that it uses skip connections to avoid the problem of vanishing gradients and improve the performance of the network. we used classification bootstrap and ResNet50 to analyze a dataset of potato images with different diseases. We compared the performance of ResNet50 with other models, such as Inception-v3 and Xception, and used bootstrap to evaluate the accuracy and variability of the models. We also used data augmentation and dropout techniques to optimize the models and prevent overfitting. We define some functions to run training by doing Early Stop, train_steps, val_steps, predictions. In the research I conducted, I got the highest accuracy results, reaching 100% accuracy by testing epochs with a total of 0-41 epochs.