Aprendizado de máquina aplicado na classificação de diferentes subespécies de plantas da espécie Acer palmatum

Abstract

Plant identification is an important task as it provides valuable information about plant characteristics and helps determine the population and distribution of species. This work presents an Artificial Intelligence solution developed for the automatic classification of subspecies of the plant species Acer palmatum based on images. A database containing subspecies of Acer palmatum was created, and supervised learning algorithms such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN) were used to classify them. The experiments included different scenarios, such as original images, feature extraction, and data augmentation techniques. The results showed that the CNN with transfer learning and data augmentation performed best, standing out as the best model tested regardless of the dataset evaluated. These findings suggest that the use of advanced Machine Learning techniques can be highly effective in classifying subspecies of Acer palmatum, providing a valuable tool for biodiversity monitoring and mapping.