Classification of the FBSI dataset using neural networks
DOI:
https://doi.org/10.36825/RITI.13.32.006Keywords:
Precision Livestock Farming, Feed Bunk Managment, Artificial Intelligence, Dataset FBSI, InceptionV3Abstract
This paper presents the application of the InceptionV3, VGG16, ResNet50 models, and a simple convolutional neural network (CNN) to analyze the FBSI (Feed Bunk Score Image) dataset composed of 1,511 images grouped into six classes. The main objective is to identify the model with the best F1 score for image classification in the context of precision livestock farming. The study is based on a comparative analysis of pre-trained models. The characteristics of the FBSI dataset are described, as well as the equipment configuration, the methodological procedure, as well as the necessary software and programming language, validating the hypothesis that the feed bunk managment image classification system achieves a performance greater than 85%.
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