A Novel Public Dataset for Strawberry (Fragaria x ananassa) Ripeness Detection and Comparative Evaluation of YOLO-Based Models

2026-02-17

AI Summary

Strawberry maturity assessment has long relied on subjective visual inspection, and the absence of a standardized public dataset has hindered algorithmic development. To address this gap, this work presents the first publicly available dataset for strawberry maturity detection, comprising 566 images with 1,201 meticulously annotated instances captured under varying lighting conditions and greenhouse environments. Leveraging this dataset, the study systematically evaluates models from the YOLOv8, YOLOv9, and YOLO11 families. Experimental results show that YOLOv9c achieves the highest precision (90.94%), YOLO11s attains the best recall (83.74%), and YOLOv8s delivers the strongest overall performance with an mAP@50 of 86.09%. These findings underscore the efficacy of small-to-medium-scale models in agricultural vision tasks and establish a reliable benchmark for intelligent harvesting systems.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Evaluation and Analysis
• Search and Optimization: Algorithm Configuration

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

The strawberry (Fragaria x ananassa), known worldwide for its economic value and nutritional richness, is a widely cultivated fruit. Determining the correct ripeness level during the harvest period is crucial for both preventing losses for producers and ensuring consumers receive a quality product. However, traditional methods, i.e., visual assessments alone, can be subjective and have a high margin of error. Therefore, computer-assisted systems are needed. However, the scarcity of comprehensive datasets accessible to everyone in the literature makes it difficult to compare studies in this field. In this study, a new and publicly available strawberry ripeness dataset, consisting of 566 images and 1,201 labeled objects, prepared under variable light and environmental conditions in two different greenhouses in Turkey, is presented to the literature. Comparative tests conducted on the data set using YOLOv8, YOLOv9, and YOLO11-based models showed that the highest precision value was 90.94% in the YOLOv9c model, while the highest recall value was 83.74% in the YOLO11s model. In terms of the general performance criterion mAP@50, YOLOv8s was the best performing model with a success rate of 86.09%. The results show that small and medium-sized models work more balanced and efficiently on this type of dataset, while also establishing a fundamental reference point for smart agriculture applications.

Problem

• strawberry ripeness detection
• public dataset
• computer-assisted systems
• smart agriculture
• object detection

Innovation

• strawberry ripeness detection
• public dataset
• YOLO-based models
• smart agriculture
• object detection