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

Abstract

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, used in 28 (46.7%) of the 60 reviewed works, due to its real-time capability and architectural flexibility. Despite its short history, YOLOv11 has been adopted in 13 studies (21.7%) owing to its balanced precision and computational efficiency. Hybrid CNN–ViT models that integrate Transformer modules or networks into YOLO are gaining attention (8 studies, 13.3%) and show improved performance in complex scenarios, but they still incur higher computational cost. However, we identify critical methodological issues that affect the validity of reported results. In particular, the improper application of data augmentation prior to dataset splitting — a practice observed in precisely one-third of the reviewed studies — poses a significant risk of data leakage and can result in overly optimistic performance estimates. Additional challenges include inconsistent evaluation metrics, limited dataset diversity, and a lack of standardized benchmarks. 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.

Code and data availability

This is a systematic review of 60 published studies; the authors explicitly state 'No new data were created or analyzed in this study.' The strawberry datasets referenced (StrawDI, Strawberry-DS on Mendeley, Kaggle ripeness dataset, Baidu dataset) belong to the reviewed third-party studies, not to this paper's own phen

No evidence-backed public reproduction asset is currently recorded.