Comparative analysis of the Support Vector Machine and Convolutional Neural Network algorithms applied in classifying Acer Palmatum plant subspecies

Abstract

Background: Plant identification is an essential task as it provides valuable information about plant characteristics and helps determine the population and distribution of species. This paper presents an artificial intelligence solution developed to automatically classify 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. Results: 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. Conclusions: These findings suggest that advanced Machine Learning techniques can be highly effective in classifying subspecies of  Acer palmatum, providing a valuable biodiversity monitoring and mapping tool.