Identification of native palms (Arecaceae) in tropical forest areas based on Convolutional Neural Network with UAV Images
DOI:
https://doi.org/10.26848/rbgf.v16.5.p2360-2374Keywords:
Segmentation, deep learning, CNN, palm trees, remote sensing.Abstract
Palm trees are important components for maintaining biodiversity and ecosystems in tropical forests. Additionally, they are widely used by extractive communities for various purposes, such as food, medicine, and commerce. However, traditional approaches to identifying and mapping their distribution have reported low accuracy rates and high financial and operational costs. To address this problem, artificial neural networks, especially convolutional neural networks, are being used for pattern recognition in images, particularly those collected by low-cost remote equipment such as drones. Such networks have shown high accuracy rates in identifying forest species. This study proposes a method to classify native palm trees of the Arecaceae family in tropical forest areas using images acquired by a low-cost unmanned aerial vehicle and a convolutional neural network. The method achieved more accurate results than conventional approaches, with an accuracy of 95.86%, precision metrics of 99.57%, and recall and precision metrics of 95.95%. Thus, maps derived from these low-cost systems can be useful for supporting community forest management and monitoring projects in the Amazon.
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