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Deep learning for real-time strawberry detection, ripeness classification, and picking point localization: a review of architectures, field studies, and open challenges

Year: 2026
Type: article
Abstract: Accurate detection of strawberry fruit, reliable ripeness estimation, and precise localization of the picking point are essential for automated harvesting and yield prediction in smart farming. However, real-world environments introduce significant challenges, including occlusion, illumination variability, and high visual similarity between ripeness stages. Deep learning (DL)-based object detection methods have become the dominant approach to address these issues. This paper presents a systematic review of 60 peer-reviewed studies published between 2023 and 2026, focusing on detecting strawberries and their ripeness using the YOLO family (v5–v11) of DL algorithms. The studies are analyzed with respect to dataset characteristics, preprocessing and augmentation strategies, model architectures, and evaluation protocols. Our results show a clear dominance of YOLOv8 (28 studies, 46.7%) and YOLOv11 (13 studies, 21.7%), while hybrid CNN–ViT models are gaining attention (8 studies, 13.3%). However, critical methodological issues are identified: in 21 out of 60 studies (35.0%), data augmentation was applied before dataset splitting, 13 studies (21.7%) adhered to the recommended split-then-augment protocol, and 8 studies (13.3%) applied online augmentation. The remaining 18 studies (30.0%) did not report using any augmentation. Reproducibility is severely limited by scarce sharing of code or trained weights (2 studies, 3.3%) and datasets (8 studies, 13.3%). To quantify the empirical impact of this methodological flaw, we conducted a controlled experiment on the StrawDI_Db1 dataset using YOLOv8, comparing four augmentation strategies. The results demonstrate that the full leakage protocol overestimates test performance by 11.76 to 19.28 percentage points (p.p.) in [email protected]:0.95 (and by 8.02 to 11.50 p.p. in [email protected]) compared to a clean, split-then-augment pipeline. The underlying validation-test discrepancy reaches up to 18.63 p.p. ([email protected]:0.95) and 13.81 p.p. ([email protected]). These findings provide direct quantitative evidence that the performance metrics reported in 35.0% of the reviewed studies may be substantially inflated. This review provides a structured overview of current approaches, a critical assessment of existing research practices, and actionable guidance for developing robust, deployment-ready DL solutions for precision agriculture.
DOI: 10.1007/s10462-026-11747-8
Language: English
Subfield: Plant Science
Open Access status: hybrid
Estimated APC paid: $3,190
OpenAlex ID: W7224773466
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