What question did this study set out to answer?

This research aims to analyze the effectiveness of Faster R-CNN and YOLOv8 models for detecting mango fruits from aerial imagery.

Performance Analysis of Faster R-CNN and YOLOv8 Model for Mango Fruits Detection

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Randomized trial evaluates mango detection accuracy using Faster R-CNN and YOLOv8 models, suggesting AI improves agriculture.

This research aims to analyze the effectiveness of Faster R-CNN and YOLOv8 models for detecting mango fruits from aerial imagery.
Evaluated Faster R-CNN and YOLOv8 on the ACFR and local datasets
Used ResNet-50 backbone for Faster R-CNN
Applied simple image augmentation for YOLOv8 training.
YOLOv8 achieved mAP@0.5 of 0.959 on the ACFR dataset and 0.756 on the local dataset.
YOLOv8 outperformed Faster R-CNN significantly on both datasets.
Image augmentation improved detection performance, particularly in varied lighting conditions.

Abdullah et al. (2026) studied this question.

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View Full Paper

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Authors

Randomized trial evaluates mango detection accuracy using Faster R-CNN and YOLOv8 models, suggesting AI improves agriculture.

This research aims to analyze the effectiveness of Faster R-CNN and YOLOv8 models for detecting mango fruits from aerial imagery.
Evaluated Faster R-CNN and YOLOv8 on the ACFR and local datasets
Used ResNet-50 backbone for Faster R-CNN
Applied simple image augmentation for YOLOv8 training.
YOLOv8 achieved mAP@0.5 of 0.959 on the ACFR dataset and 0.756 on the local dataset.
YOLOv8 outperformed Faster R-CNN significantly on both datasets.
Image augmentation improved detection performance, particularly in varied lighting conditions.

Abdullah et al. (2026) studied this question.

View Full Paper

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