What question did this study set out to answer?

The central aim is to assess the effectiveness of YOLOv8 and U-Net in detecting and segmenting healthy and diseased palm trees using UAV imagery.

Evaluating AI for Palm Tree Disease Detection: A Comparative Study of YOLOv8 Object Detection and U-Net Segmentation Using UAV Imagery

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Comparative study evaluates AI techniques for detecting palm tree diseases, suggesting effective monitoring solutions.

The central aim is to assess the effectiveness of YOLOv8 and U-Net in detecting and segmenting healthy and diseased palm trees using UAV imagery.
Analyzed a dataset of 400 UAV images annotated for training, validation, and testing.
Used YOLOv8 for object detection and U-Net for segmentation of palm trees.
Evaluated model performance using accuracy, precision, recall, and F1-score metrics.
YOLOv8 achieved 78.48% accuracy with precision of 58.38% and recall of 47.70%.
U-Net excelled with precision of 0.8746, recall of 0.8713, and F1-score of 0.8727.
The study demonstrates complementary strengths, with YOLOv8 efficient in detection and U-Net effective in segmentation.

Hammadi et al. (2026) studied this question.

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View Full Paper

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Authors

Comparative study evaluates AI techniques for detecting palm tree diseases, suggesting effective monitoring solutions.

The central aim is to assess the effectiveness of YOLOv8 and U-Net in detecting and segmenting healthy and diseased palm trees using UAV imagery.
Analyzed a dataset of 400 UAV images annotated for training, validation, and testing.
Used YOLOv8 for object detection and U-Net for segmentation of palm trees.
Evaluated model performance using accuracy, precision, recall, and F1-score metrics.
YOLOv8 achieved 78.48% accuracy with precision of 58.38% and recall of 47.70%.
U-Net excelled with precision of 0.8746, recall of 0.8713, and F1-score of 0.8727.
The study demonstrates complementary strengths, with YOLOv8 efficient in detection and U-Net effective in segmentation.

Hammadi et al. (2026) studied this question.

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