Coconut Trees Detection on the Tenarunga Using High-Resolution Satellite Images and Deep Learning

The Coconut tree is of great importance in economic values and ecological impacts for many tropical developing countries and lots of islands in the Pacific Ocean. Detecting and counting coconut is a meaningful and valuable research. In this paper, we present a coconut tree crown detection method to detect and count the coconut trees in the Tenarunga from high-resolution satellite images acquired by Google Earth. Our coconut tree detection method contains three major procedures: feature extraction, a multi-level Region Proposal Network (RPN) and a large-scale coconut tree detection workflow. We manually annotate all coconut trees for our study regions in the Tenarunga. Eventually, we achieve a higher average F1-score of 77.14% in our four test regions than pure Faster R-CNN. Experiment results demonstrate the potential for large-scale individual coconut tree detection and counting from high-resolution satellite images using deep learning.