A Comparative Study of Modern Object Detectors for Robust Apple Detection in Orchard Imagery

TL;DR

This study compares six modern object detectors for apple detection in orchard images, establishing a controlled benchmark to evaluate their accuracy, robustness, and suitability for agricultural applications.

Contribution

It provides a fair, reproducible comparison of detectors on a public dataset using a unified evaluation protocol, highlighting their strengths and weaknesses.

Findings

YOLO11n achieves the highest strict localization accuracy.

RT-DETR-L has very high recall but low precision at low confidence thresholds.

Detector choice should consider threshold robustness and downstream task needs.

Abstract

Accurate apple detection in orchard images is important for yield prediction, fruit counting, robotic harvesting, and crop monitoring. However, changing illumination, leaf clutter, dense fruit clusters, and partial occlusion make detection difficult. To provide a fair and reproducible comparison, this study establishes a controlled benchmark for single-class apple detection on the public AppleBBCH81 dataset using one deterministic train, validation, and test split and a unified evaluation protocol across six representative detectors: YOLOv10n, YOLO11n, RT-DETR-L, Faster R-CNN (ResNet50-FPN), FCOS (ResNet50-FPN), and SSDLite320 (MobileNetV3-Large). Performance is evaluated primarily using COCO-style mAP@0.5 and mAP@0.5:0.95, and threshold-dependent behavior is further analyzed using precision-recall curves and fixed-threshold precision, recall, and F1-score at IoU = 0.5. On the validation split, YOLO11n achieves the best strict localization performance with mAP@0.5:0.95 = 0.6065 and mAP@0.5 = 0.9620, followed closely by RT-DETR-L and YOLOv10n. At a fixed operating point with confidence >= 0.05, YOLOv10n attains the highest F1-score, whereas RT-DETR-L achieves very high recall but low precision because of many false positives at low confidence. These findings show that detector selection for orchard deployment should be guided not only by localization-aware accuracy but also by threshold robustness and the requirements of the downstream task.