What question did this study set out to answer?

To develop and validate an automated deep learning framework for detecting, mapping, and quantifying individual palm tree crowns from high-resolution UAV aerial imagery.

August 16, 2026Scientific ReportsOpen Access

Detection of palm tree from high-resolution UAV images using deep learning technique

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Authors

BEBosy A. El-Haddad AYAhmed M. Youssef OGOmid Ghorbanzadeh

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Overview

Machine learning study demonstrates high-accuracy palm crown detection from UAV imagery, indicating potential for automated agricultural auditing in precision farming.

Key Points

To develop and validate an automated deep learning framework for detecting, mapping, and quantifying individual palm tree crowns from high-resolution UAV aerial imagery.
Applied a Mask Region-based Convolutional Neural Network (Mask R-CNN) instance-segmentation architecture to tiled and sampled high-resolution UAV images.
Utilized an end-to-end processing pipeline including tiling with overlap, confidence filtering, and duplicate removal.
Evaluated model transferability against field-verified, manually delineated crown polygons across three independent sites (S1, S2, and S3).
Achieved precision ranging from 92.74% to 97.22% and recall ranging from 90.91% to 96.67% across the three test sites.
Recorded overall classification accuracy between 86.32% and 94.27% with corresponding F1-scores of 0.92 to 0.97 for palm crown quantification.

Cite This Study

El-Haddad et al. (2026) studied this question.

synapsesocial.com/papers/6a8179cbf2fb91fc834ad2b2 https://doi.org/10.1038/s41598-026-64260-x

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August 16, 2026Scientific ReportsOpen Access

Detection of palm tree from high-resolution UAV images using deep learning technique

View Full Paper

Ask AI

Bookmark

Authors

BEBosy A. El-Haddad AYAhmed M. Youssef OGOmid Ghorbanzadeh

Discussion

Member takes

Overview

Machine learning study demonstrates high-accuracy palm crown detection from UAV imagery, indicating potential for automated agricultural auditing in precision farming.

Key Points

To develop and validate an automated deep learning framework for detecting, mapping, and quantifying individual palm tree crowns from high-resolution UAV aerial imagery.
Applied a Mask Region-based Convolutional Neural Network (Mask R-CNN) instance-segmentation architecture to tiled and sampled high-resolution UAV images.
Utilized an end-to-end processing pipeline including tiling with overlap, confidence filtering, and duplicate removal.
Evaluated model transferability against field-verified, manually delineated crown polygons across three independent sites (S1, S2, and S3).
Achieved precision ranging from 92.74% to 97.22% and recall ranging from 90.91% to 96.67% across the three test sites.
Recorded overall classification accuracy between 86.32% and 94.27% with corresponding F1-scores of 0.92 to 0.97 for palm crown quantification.

Cite This Study

El-Haddad et al. (2026) studied this question.

synapsesocial.com/papers/6a8179cbf2fb91fc834ad2b2 https://doi.org/10.1038/s41598-026-64260-x

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