Resumen

Colombia is the country with the largest number of plant species in the world. Within it, the heliconias play an important ecological role within the ecosystems, since they are frequent components of the interior and limits of the forests, as well as of open environments such as pastures, roadsides and riverbanks. In some ecosystems they act as pioneers in the process of natural regeneration of vegetation and restoration of degraded soil. In addition, they maintain important co-evolutionary relationships with other animal and plant species, becoming an important element within the complex framework of life in the tropics. The classification of plant species is crucial for the protection and conservation of biodiversity. Manual classification is time-consuming, costly and requires experts who are often limited in their availability. To address these problems, three methods of classification of SVM (Support Vector Machine), ANN (Neural Networks), KNN (Nearest Neighbors) images with Euclidean distance and intersection were used in this work, which gave good results in the classification of four species of heliconias found at the University of Ibagué. The data used for training, testing and validation of the methods were RGB images taken in the natural habitat of the heliconias, in order to have information from their germination to their optimal cutting time. The images were pre-processed, making an adjustment of white balance, contrast and color temperature. To separate the heliconias from the background, a graphical segmentation technique using GPS was used. The descriptors were obtained using the technique known as BoW (Bag of Words), finding that the number of visual words most suitable for classification was between 20 and 30. The method with which the best results were obtained was the KNN; using the three closest neighbors, with an accuracy of 97%