YOLOv5 for palm tree localization and classification for environmental and agricultural applications

Environmental monitoring, forestry management, and precision agriculture all depend on the accurate identification of individual palm trees. The usefulness of You Only Look Once fifth version (YOLOv5), a deep learning-based object detection model, for recognizing and categorizing individual palm trees is investigated in this work. The model was trained and validated using a dataset of 2,340 photos, resulting in an impressive 99% accuracy in tree detection and 100% accuracy in background classification, with low false positives. The model’s capacity to detect palm with exceptional localization and classification performance was confirmed by its high mean Average Precision (mAP@0.5 of —0.95). Despite these promising results, overlapping tree crowns are still a problem because there isn’t enough training data for the algorithm to detect distinct boundaries or combine closely spaced trees. Further improving identification accuracy may be possible by addressing this issue with better dataset annotations, sophisticated segmentation methods, or multi-view imaging. Our study demonstrates that YOLOv5 is a very dependable method for identifying individual palm trees, with great promise for automated tree counting, plantation monitoring, and extensive ecological evaluations.