Development of an Image Recognition System Based on Neural Networks for the Classification of Plant Species in the Amazon Rainforest, Peru, 2024

Introduction: The recognition and classification of plant species in the Amazon Rainforest is crucial for biodiversity conservation and ecological research. This study presents the development of an image recognition system based on neural networks for the classification of plant species in the Amazon Rainforest, Peru, 2024. Objective: Create an efficient model that can identify and classify various plant species from images, thus improving current methods of cataloging and studying Amazonian flora. Methodology: The methodology includes collecting a large dataset of plant images, followed by rigorous preprocessing to normalize and augment the data. A convolutional neural network (CNN) was designed and trained using advanced machine learning techniques, and its performance was evaluated using metrics such as precision, recall and F1-score. Results: The results show that the developed model achieves an accuracy of 92%, surpassing traditional methods and some previous models in the literature. This high precision suggests that the system can be a valuable tool for researchers and conservationists in the Amazon Rainforest. Conclusion: This study demonstrates the effectiveness of neural networks in the classification of plant species and highlights their potential to contribute significantly to the conservation and study of biodiversity in the Amazon region.