Development of a Backpropagation Neural Network to Assist Cell Identification in Herbivore Diet at the Semiarid Chaco of North-West Argentina

Abstract

Raising goats is a strong component of regional economies and a main activity of little farmers in Argentina. Knowing the diet is fundamental to correct nutritional lacks resulting from grazing native species, as it will help to design management strategies both for the native forage resource and for the nutritional state of the animal. This fact has a preponderant relevance when goat production is done at natural areas. Microhistology is one of the more frequently used methodology to determine the botanical composition of herbivorous diet. The specialist working at a microscope, according to his training and experience, can identify the species taken by the animal and to which those cells belong. Neural network approaches are suited for recognition problems, this article presents a backpropagation neural network initially trained to identify the four most significant vegetal species found in goat diet that graze freely in the Semiarid Chaco of North-West Argentina.