Compresion y analisis de imagenes por medio de algoritmos para la ganaderia de precision
This is an incremental approach for precision livestock farming to aid in cattle selection by automating health assessment from images.
The paper tackled the problem of identifying healthy versus sick bovine animals from images using compression algorithms and machine learning to improve precision over time, with significant results on execution speed.
The problem that we want to solve in this project of the subject of Data Structures and Algorithms, is to decipher some images, which have in them animals, being more specific, bovine animals; in which it is necessary to identify if the animal is healthy, that is to say, if it is in good conditions to be taken into account in the process of selection of the cattle, or if it is sick, to know if it is discarded. All this by means of an algorithm of compression, which allows to take the images and to take them to an examination of these in the code, where not always the results are going to be one hundred percent exact, but what allows this code to be efficient, is that it works with machine learning, which means that the more information it takes, the more precise the results are going to be without bringing with it general affectations. The proposed algorithms are NN and bilinear interpolation, where significant results were obtained on the execution speed. It is concluded that a better job could have been done, but with what was delivered, it is believed that it is a good result of the work.