Application of the convolutional network Mask R-CNN for the estimation of the body weight of the guinea pig
DOI:
https://doi.org/10.51252/rcsi.v4i1.614Keywords:
mobile application, image dataset, artificial intelligence, convolutional networksAbstract
Artificial intelligence can contribute in tracking the productive cycle of the cuy through the application of convolutional networks, being a necessity the estimation of its weight. This study focused on the application of the Mask R-CNN convolutional network, using a mobile application as a tool for image capture. The methodology covered the following stages: i) bibliographic review, ii) data collection (images and pig weights), iii) image processing through data augmentation, iv) construction of a dataset (image selection and data transformation); , v) adaptation and training of the convolutional network, vi) analysis of the results to validate its performance, and finally, vii) implementation of a mobile application as a weight estimation tool. A set of 6244 pig images with their respective weights and masks was managed to be collected, together with the Mask R-CNN network adaptation. These tasks led to a correlation of R2 = 80.2% with the validation set, as well as to the development of a functional prototype capable of estimating the weight of pigs using the camera of a cell phone.
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