Image Classification for Egg Incubator using Transfer Learning of VGG16 and VGG19

Research in the field of image classification is proliferating and providing benefits to the community. This research focuses on image classification on incubators, incubators in addition to monitoring temperature and humidity. Monitoring of conditions in incubators is also required. This study proposes a classification of eggs, hatching eggs, and chicks. Many images of each object are needed, a total of 3,924 images from all three classes. The data set collected from google image and the collection of images owned by the author obtained from shooting with a smartphone camera on a private farm. Data preprocessing is carried out, such as changing the shape of the image to a square that VGG required 224x224 pixels, augmentation to reproduce data, and sharing training data are the input size 80 and 20 percent validation. After preprocessing the data, the model formation and training process was carried out with the results for each model: Custom CNN model yielded an accuracy of 0.8687, VGG16 produced an accuracy of 0.90, and VGG19 produced an accuracy of 0.92. This study shows that transfer learning has the highest accuracy in image classification.

Automated summary

Research in the field of image classification, particularly focusing on incubators, has shown that transfer learning yields the highest accuracy in this area. The study utilized various models for training and testing, with Custom CNN, VGG16, and VGG19 models achieving different levels of accuracy.